Mapping the self-generated magnetic fields due to thermal Weibel instability.

The origin of the seed magnetic field that is amplified by the galactic dynamo is an open question in plasma astrophysics. Aside from primordial sources and the Biermann battery mechanism, plasma instabilities have also been proposed as a possible source of seed magnetic fields. Among them, thermal Weibel instability driven by temperature anisotropy has attracted broad interests due to its ubiquity in both laboratory and astrophysical plasmas. However, this instability has been challenging to measure in a stationary terrestrial plasma because of the difficulty in preparing such a velocity distribution. Here, we use picosecond laser ionization of hydrogen gas to initialize such an electron distribution function. We record the 2D evolution of the magnetic field associated with the Weibel instability by imaging the deflections of a relativistic electron beam with a picosecond temporal duration and show that the measured [Formula: see text]-resolved growth rates of the instability validate kinetic theory. Concurrently, self-organization of microscopic plasma currents is observed to amplify the current modulation magnitude that converts up to ~1% of the plasma thermal energy into magnetic energy, thus supporting the notion that the magnetic field induced by the Weibel instability may be able to provide a seed for the galactic dynamo.

Bidirectional Cascaded Superfluorescent Lasing in Air Enabled by Resonant Third Harmonic Photon Exchange from Nitrogen to Argon.

Cavity-free lasing in atmospheric air has stimulated intense research toward a fundamental understanding of underlying physical mechanisms. In this Letter, we identify a new mechanism-a third-harmonic photon mediated resonant energy transfer pathway leading to population inversion in argon via an initial three-photon excitation of nitrogen molecules irradiated by intense 261 nm pulses-that enables bidirectional two-color cascaded lasing in atmospheric air. By making pump-probe measurements, we conclusively show that such cascaded lasing results from superfluorescence rather than amplified spontaneous emission. Such cascaded lasing with the capability of producing bidirectional multicolor coherent pulses opens additional possibilities for remote sensing applications.

The heterogeneous impact of population mobility on the influent characteristics of wastewater treatment facilities.

Improving the resilience of wastewater treatment facilities (WWTFs) has never been more important with rising risks of disasters under climate change. Beyond physical damages, non-physical shocks induced by disasters warrant attention. Human mobility is a vital mediator in transferring the stresses from extreme events into tangible challenges for urban sewage systems by reshaping influent characteristics. However, the impact path remains inadequately explored. Leveraging the stay-at-home orders during the COVID-19 pandemic as a natural experiment, this study aims to quantify and interpret the heterogeneous impacts of mobility reduction on the influent characteristics of WWTFs with different socio-economic, infrastructural, and climatic conditions. To achieve this goal, we developed a research framework integrating causal inference and interpretable machine learning techniques. Based on the empirical data from China, we find that 79.1% of the studied WWTFs, typically located in cities with well-developed drainage infrastructures and low per capita water usage, exhibited resilience against drastic mobility reduction. In contrast, 20.9% of the studied WWTFs displayed significant variations in influent characteristics. Large-capacity WWTFs in subtropical regions encountered challenges with low-load operations, and small-capacity facilities in suburban areas grappled with nutrient imbalances. This study provides valuable insights to equip WWTFs in anticipating and adapting potential variations in influent characteristics triggered by mobility reduction.

Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations.

Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.

Cross-polarized common-path temporal interferometry for high-sensitivity strong-field ionization measurements.

Absolute density measurements of low-ionization-degree or low-density plasmas ionized by lasers are very important for understanding strong-field physics, atmospheric propagation of intense laser pulses, Lidar etc. A cross-polarized common-path temporal interferometer using balanced detection was developed for measuring plasma density with a sensitivity of ∼0.6 mrad, equivalent to a plasma density-length product of ∼2.6 × 1013 cm-2 if using an 800 nm probe laser. By using this interferometer, we have investigated strong-field ionization yield versus intensity for various noble gases (Ar, Kr, and Xe) using 800 nm, 55 fs laser pulses with both linear (LP) and circular (CP) polarization. The experimental results were compared to the theoretical models of Ammosov-Delone-Krainov (ADK) and Perelomov-Popov-Terent'ev (PPT). We find that the measured phase change induced by plasma formation can be explained by the ADK theory in the adiabatic tunneling ionization regime, while PPT model can be applied to all different regimes. We have also measured the photoionization and fractional photodissociation of molecular (MO) hydrogen. By comparing our experimental results with PPT and MO-PPT models, we have determined the likely ionization pathways when using three different pump laser wavelengths of 800 nm, 400 nm, and 267 nm.

Cosmic Coincidences of Primordial-Black-Hole Dark Matter.

If primordial black holes (PBHs) contribute more than 10% of the dark matter (DM) density, their energy density today is of the same order as that of the baryons. Such a cosmic coincidence might hint at a mutual origin for the formation scenario of PBHs and the baryon asymmetry of the Universe. Baryogenesis can be triggered by a sharp transition of the rolling rate of inflaton from slow-roll to (nearly) ultraslow-roll phases that produce large curvature perturbations for PBH formation in single-field inflationary models. We show that the baryogenesis requirement drives the PBH contribution to DM, along with the inferred PBH mass range, the resulting stochastic gravitational wave background frequency window, and the associated cosmic microwave background tensor-to-scalar ratio amplitude, into potentially observable regimes.

Extremely Dense Gamma-Ray Pulses in Electron Beam-Multifoil Collisions.

Sources of high-energy photons have important applications in almost all areas of research. However, the photon flux and intensity of existing sources is strongly limited for photon energies above a few hundred keV. Here we show that a high-current ultrarelativistic electron beam interacting with multiple submicrometer-thick conducting foils can undergo strong self-focusing accompanied by efficient emission of gamma-ray synchrotron photons. Physically, self-focusing and high-energy photon emission originate from the beam interaction with the near-field transition radiation accompanying the beam-foil collision. This near field radiation is of amplitude comparable with the beam self-field, and can be strong enough that a single emitted photon can carry away a significant fraction of the emitting electron energy. After beam collision with multiple foils, femtosecond collimated electron and photon beams with number density exceeding that of a solid are obtained. The relative simplicity, unique properties, and high efficiency of this gamma-ray source open up new opportunities for both applied and fundamental research including laserless investigations of strong-field QED processes with a single electron beam.

Up-regulation of miRNA-151-3p enhanced the neuroprotective effect of dexmedetomidine against ß-amyloid by targeting DAPK-1 and TP53.

Alzheimer's disease (AD) is the most common cause of dementia and is the leading lethal disease among the elderly. Dexmedetomidine (Dex) has been reported to have multiple neuroprotective effects, but its effect against beta-amyloid (Aß) has not been completely determined and understood. Dex can activate both α2 adrenoceptor/cAMP/PKA and imidazoline I receptors/ERK1/2 signals. To determine which signal is critical for the effect of Dex on Aß toxicity, we treated SH-SY5Y and PC12 cells with inhibitors of α2 adrenoceptor and ERK1/2. Dex suppressed the apoptosis of neuronal cells and production of reactive oxygen species induced by Aß. These suppressive effects were attenuated by both inhibitors. As indicated by western blot, Dex stimulates both pro-apoptosis (activating death-associated protein kinase 1 [DAPK-1] and p53) and anti-apoptotic (up-regulating bcl-2 and bcl-xL) signals in Aß-treated neuronal cells. This effect is likely associated with ERK1/2 signaling because ERK1/2 inhibitor disrupts the effect of Dex on these signals. To eliminate the pro-apoptotic effect of Dex while retaining its anti-apoptosis action, we screened miRNA-151-3p to target DAPK-1 and p53. Transfection with miRNA-151-3p mimics suppressed DAPK-1 and TP53 expression induced by Dex and increased Nrf-2 and SOD expression. More importantly, increasing miRNA-151-3p enhanced the anti-apoptotic and antioxidative effects of Dex in Aß-treated neuronal cells. Overall, this study revealed that Dex additionally stimulated pro-apoptosis signaling, although it suppressed Aß-induced apoptosis of neuronal cells. miRNA-151-3p enhanced the neuroprotective effect of Dex against Aß by targeting DAPK-1 and TP53.

Measurements of the Growth and Saturation of Electron Weibel Instability in Optical-Field Ionized Plasmas.

The temporal evolution of the magnetic field associated with electron thermal Weibel instability in optical-field ionized plasmas is measured using ultrashort (1.8 ps), relativistic (45 MeV) electron bunches from a linear accelerator. The self-generated magnetic fields are found to self-organize into a quasistatic structure consistent with a helicoid topology within a few picoseconds and such a structure lasts for tens of picoseconds in underdense plasmas. The measured growth rate agrees well with that predicted by the kinetic theory of plasmas taking into account collisions. Magnetic trapping is identified as the dominant saturation mechanism.

Clinical Application of 3-Dimensional Printed Navigation Templates in Treating Femoral Head Osteonecrosis With Pedicled Iliac Bone Graft.

OBJECTIVE: The aim of the study was to explore the feasibility and early effect of digital design combined with 3-dimensional (3D) printing technique in the transplantation of vascular pedicled iliac bone flap in the treatment of avascular necrosis of the femoral head. METHODS: The navigation template was designed according to computed tomography scan and printed in 3D printing technique before operation, which was used to guide the localization and clearance of osteonecrosis of the femoral head in vascular pedicled iliac bone flap transplantation. In blank control group, 28 cases (32 hips) of osteonecrosis of the femoral head were treated with vascular pedicled iliac bone flap without the assistance of 3D navigation template from February 2002 to February 2009, including 19 males (21 hips) and 9 females (11 hips), with an average age of 37 years (range, 20-61 years). There were 12 cases of left hip, 16 cases of right hip, and 4 cases of double hip. According to the International Association of Bone Circulation staging, there were 8 hips in stage II B, 9 hips in stage II C, 8 hips in stage III B, and 7 hips in stage III C. In the experimental group, from February 2014 to June 2014, 15 patients (24 hips) with avascular necrosis of the femoral head were treated with vascular pedicled iliac bone flap with the aid of 3D navigation template. There were 11 males (17 hips) and 4 females (7 hips) with an average age of 38 years (range, 18-56 years). There were 2 cases of left hip, 4 cases of right hip, and 9 cases of double hip. According to the International Association of Bone Circulation staging, there were 5 hips in stage II B, 8 hips in stage II C, 6 hips in stage III B, and 5 hips in stage III C. The operation time, bleeding volume, and postoperative Harris score of the experimental group and the control group were statistically analyzed. RESULTS: The incisions in both groups healed in the first stage, and there were no operation-related complications such as deep venous thrombosis and infection of lower extremities. All patients were followed up for 12 to 16 months (with an average of 14 months). On the second day after operation, X-ray and computed tomography showed that the necrotic focus of the femoral head and the surrounding sclerotic bone was completely removed, and the position of the vascular pedicled iliac bone flap was satisfactory and did not penetrate the articular surface. The iliac bone flap and bone graft achieved bony fusion. In the navigation template group, the mean ± SD operation time was 135.38 ± 9.49 minutes, the mean ± SD blood loss was 225.13 ± 13.41 mL, the mean ± SD postoperative Harris score was 89.53 ± 5.83, 12 hips were excellent, 10 hips were good, and 2 hips were moderate, whereas in the group without navigation template, the mean ± SD operation time was 151.00 ± 15.28 minutes, the mean ± SD blood loss was 283.56 ± 30.60 mL, the mean ± SD postoperative Harris score was 83.32 ± 3.75, 15 hips were excellent, 14 hips were good, and 3 hips were fair. By independent sample t test, there were significant differences in average operation time, average blood loss, and postoperative Harris score between the 2 groups (P < 0.05). CONCLUSIONS: Compared with not using navigation template, vascular pedicled iliac bone flap combined with navigation template in the treatment of osteonecrosis of femoral head could locate the area of osteonecrosis of femoral head more accurately, shorten the time of operation, and reduce the amount of bleeding during operation. Postoperative hip joint function recovery was better, and the early effect was satisfactory.

Addressing data limitations in leakage detection of water distribution systems: Data creation, data requirement reduction, and knowledge transfer.

Leakage in water distribution systems is a significant problem worldwide, leading to wastage of water resources, compromised water quality and excess energy consumption. Leakage detection is essential to reduce the duration of leaks and data-driven methods are increasingly being used for this purpose. However, these models are data hungry and available observed data, especially leakage data, is limited in most cases. In addition, these data need to be manually processed to label whether leaks occur, which is time-consuming and costly. These are significant obstacles for the development and application of these methods. This article provides a comprehensive review of relevant journal papers, categorizing all data-driven methods into unsupervised anomaly detection, semi-supervised anomaly detection and supervised classification methods based on how the data are utilized for developing these methods. In addition, strategies to address data limitations are summarized from both data and model perspectives, including data creation, reduction of a model's data requirements and knowledge transfer. After detailing these strategies, research gaps are identified. Based on these, future research directions are suggested, highlighting the need for further research in data augmentation, development of semi-supervised classification methods, exploration of multi-classification methods with model updating mechanisms, and development of novel knowledge transfer methods.

Antipyretic and anti-inflammatory effects of inosine, an active component of Kangfuxin.

Kangfuxin has been widely recognized for its use in treating ulcerative conditions and mucositis, primarily due to its anti-inflammatory properties, which promote cell proliferation, granulation tissue growth, and angiogenesis. However, the exact mechanisms underlying these effects remain poorly understood. In this study, we employed high-throughput mass spectrometry to identify 11 compounds in Kangfuxin, including uracil, hypoxanthine, xanthine, inosine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, and lysine. Notably, the antipyretic and anti-inflammatory properties of inosine, one of these compounds, have not been well characterized. To address this gap, we induced fever in vivo using lipopolysaccharide (LPS) and conducted various experiments, including the analysis of endogenous mediators, inflammatory factors, quantitative polymerase chain reaction (QPCR), Western blotting, and hematoxylin and eosin (HE) staining. Our findings indicate that inosine significantly reduces LPS-induced fever, inhibits the expression of inflammatory factors, and alleviates the inflammatory response. These results suggest that inosine may serve as a potential therapeutic target for inflammatory diseases.

Comprehensive Gene Analysis Reveals Cuproptosis-Related Gene Signature Associated with M2 Macrophage in Staphylococcus aureus-Infected Osteomyelitis.

Objective: Osteomyelitis is a challenging disease in the field of bone infections, with its immune and molecular regulatory mechanisms still poorly understood. The aim of this study is to explore the value and potential mechanisms of cuproptosis-related genes (CRGs) in Staphylococcus aureus (S. aureus)-infected osteomyelitis from an immunological perspective. Methods: Initially, three transcriptomic datasets from public databases were integrated and analyzed, and consistent expression of CRGs in S. aureus-infected osteomyelitis was identified. Subsequently, immune infiltration analysis was performed, and M2 macrophage-related CRGs (M2R-CRGs) were further identified. Their potential molecular mechanisms were evaluated using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Finally, distinct osteomyelitis subtypes and diagnostic models based on characteristic M2R-CRGs were constructed. Results: Through correlation analysis with immune cell infiltration, three characteristic M2R-CRGs (SLC31A1, DLD, and MTF1) were identified. Further analysis using unsupervised clustering and immune microenvironment analysis indicated that cluster 1 might activate pro-inflammatory responses, while cluster 2 was shown to exhibit anti-inflammatory effects in osteomyelitis. Compared to Cluster A, Cluster B demonstrated higher levels and a greater diversity of immune cell infiltrations in CRG-related molecular patterns, suggesting a potential anti-inflammatory role in osteomyelitis. A diagnostic model for S. aureus-infected osteomyelitis, based on the three M2R-CRGs, was constructed, exhibiting excellent diagnostic performance and validated with an independent dataset. Significant upregulation in mRNA and protein expression levels of the three M2R-CRGs was observed in rat models of S. aureus-infected osteomyelitis, aligning with bioinformatic results. Conclusion: The M2R-CRGs (SLC31A1, DLD, and MTF1) may be considered characteristic genes for early diagnosis and personalized immune therapy in patients with S. aureus-infected osteomyelitis.

Characteristic Study of a Typical Satellite Solar Panel under Mechanical Vibrations.

As the most common energy source of spacecraft, photovoltaic (PV) power generation has become one of the hottest research fields. During the on-orbit operation of spacecraft, the influence of various uncertain factors and the unbalanced inertial force will make the solar PV wing vibrate and degrade its performance. In this study, we investigated the influence of mechanical vibration on the output characteristics of PV array systems. Specifically, we focused on a three-segment solar panel commonly found on satellites, analyzing both its dynamic response and electrical output characteristics under mechanical vibration using numerical simulation software. The correctness of the simulation model was partly confirmed by experiments. The results showed that the maximum output power of the selected solar panel was reduced by 5.53% and its fill factor exhibited a decline from the original value of 0.8031 to 0.7587, provided that the external load applied on the panel increased to 10 N/m2, i.e., the vibration frequency and the maximal deflection angle were 0.3754 Hz and 74.9871°, respectively. These findings highlight a significant decrease in the overall energy conversion efficiency of the solar panel when operating under vibration conditions.

Integrative gene expression analysis and animal model reveal immune- and autophagy-related biomarkers in osteomyelitis.

BACKGROUND: Osteomyelitis (OM) is recognized as a significant challenge in orthopedics due to its complex immune and inflammatory responses. The prognosis heavily depends on timely diagnosis, accurate classification, and assessment of severity. Thus, the identification of diagnostic and classification-related genes from an immunological standpoint is crucial for the early detection and tailored treatment of OM. METHODS: Transcriptomic data for OM was sourced from the Gene Expression Omnibus (GEO) database, leading to the identification of autophagy- and immune-related differentially expressed genes (AIR-DEGs) through differential expression analysis. Diagnostic and classification models were subsequently developed. The CIBERSORT algorithm was utilized to examine immune cell infiltration in OM, and the relationship between OM clusters and various immune cells was explored. Key AIR-DEGs were further validated through the creation of OM animal models. RESULTS: Analysis of the transcriptomic data revealed three AIR-DEGs that played a significant role in immune responses and pathways. Nomogram and receiver operating characteristic curve analyses were performed, demonstrating excellent diagnostic capability for differentiating between OM patients and healthy individuals, with an area under the curve of 0.814. An unsupervised clustering analysis discerned two unique patterns of autophagy- and immune-related genes, as well as gene patterns. Further exploration into immune infiltration exhibited notable variances across different subtypes, especially between OM cluster 1 and gene cluster A, highlighting their potential role in mitigating inflammatory responses by regulating immune activities. Moreover, the mRNA and protein expression levels of three AIR-DEGs in the animal model were aligned with those in the training and validation data sets. CONCLUSIONS: From an immunological perspective, a diagnostic model was successfully developed, and two distinct clustering patterns were identified. These contributions offer a significant resource for the early detection and personalized immunotherapy of patients with OM.

Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective.

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods. Five random transformation-based methods-jittering, scaling, warping, iterated amplitude adjusted Fourier transform (IAAFT), and masking-are proposed. Jittering, scaling, warping, and IAAFT directly process original signals, while masking operating on time-frequency spectrograms. Acoustic signals from a real-world WDS are augmented, and the efficacy is validated using convolutional neural network classifiers to identify the spectrograms of acoustic signals. Results indicate the importance of implementing data augmentation before data splitting to prevent data leakage and overly optimistic outcomes. Among the techniques, IAAFT stands out, significantly increasing data volume and diversity, improving recognition accuracy by over 7%. Masking enhances performance mainly by compelling the classifier to learn global features of the spectrograms. Sequential application of IAAFT and masking further strengthens leak detection performance. Furthermore, when applying a complex model to acoustic leakage detection through transfer learning, data augmentation can also enhance the effectiveness of transfer learning. These findings advance artificial intelligence-driven acoustic leak detection technology from a data-centric perspective towards more mature applications.

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects.

Graph theory (GT) and complex network theory play an increasingly important role in the design, operation, and management of water distribution networks (WDNs) and these tasks were originally often heavily dependent on hydraulic models. Facing the general reality of the lack of high-precision hydraulic models in water utilities, GT has become a promising surrogate or assistive technology. However, there is a lack of a systematic review of how and where the GT techniques are applied to the field of WDNs, along with an examination of potential directions that GT can contribute to addressing WDNs' challenges. This paper presents such a review and first summarizes the graph construction methods and topological properties of WDNs, which are mathematical foundations for the application of GT in WDNs. Then, main application areas, including state estimation, performance evaluation, partitioning, optimal design, optimal sensor placement, critical components identification, and interdependent networks analysis, are identified and reviewed. GT techniques can provide acceptable results and valuable insights while having a low computational burden compared with hydraulic models. Combining GT with hydraulic model significantly enhances the performance of analysis methods. Four research challenges, namely reasonable abstraction, data availability, tailored topological indicators, and integration with Graph Neural Networks (GNNs), have been identified as key areas for advancing the application and implementation of GT in WDNs. This paper would have a positive impact on promoting the use of GT for optimal design and sustainable management of WDNs.

A Network Pharmacology Approach and Validation Experiments to Investigate the Mechanism of Wen-Dan Decoction in the Treatment of SINFH.

INTRODUCTION: Steroid-induced necrosis of the femoral head (SINFH) is a femoral head necrotic disease caused by prolonged use of hormones. Wen-Dan decoction is used in Chinese clinical practice for the treatment of steroid-induced necrosis of the femoral head (SINFH). However, the mechanism and active compounds of Wen-Dan decoction used to treat SINFH are not well understood. OBJECTIVES: We studied the mechanism of action of Wen-Dan decoction in treating steroidinduced necrosis of the femoral head (SINFH) via network pharmacology and in vivo experiments. METHODS: The active compounds of Wen-Dan decoction and SINFH-related target genes were identified through public databases. Then, network pharmacological analysis was conducted to explore the potential key active compounds, core targets and biological processes of Wen-Dan decoction in SINFH. The potential mechanisms of Wen-Dan decoction in SINFH obtained by network pharmacology were validated through in vivo experiments. RESULTS: We identified 608 DEGs (differentially expressed genes) (230 upregulated, 378 downregulated) in SINFH. GO analysis revealed that the SINFH-related genes were mainly involved in neutrophil activation and the immune response. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the SINFH-related genes were mainly associated with cytokine receptor interactions, lipids, atherosclerosis, and tuberculosis. We identified 147 active ingredients of Wen-Dan decoction; the core ingredient was quercetin, and licorice was an active ingredient. Moreover, 277 target genes in the treatment of SINFH with Wen-Dan decoction were identified, and NCF1, PTGS2, and RUNX2 were selected as core target genes. QRT-PCR of peripheral blood from SINFH patients showed higher levels of PGTS2 and NCF1 and showed lower levels of RUNX2 compared to controls. QRT-PCR analysis of peripheral blood and femoral bone tissue from a mouse model of SINFH showed higher levels of PGTS2 and NCF1 and lower levels of RUNX2 in the experimental animals than the controls, which was consistent with the bioinformatics results. HE, immunohistochemistry, and TUNEL staining confirmed a significant reduction in hormone-induced femoral head necrosis in the quercetintreated mice. HE, immunohistochemistry, and TUNEL staining confirmed significant improvement in hormone-induced femoral head necrosis in the quercetin-treated mice. CONCLUSION: We provide new insights into the genes and related pathways involved in SINFH and report that PTGS2, RUNX2, and NCF1 are potential drug targets. Quercetin improved SINFH by promoting osteogenesis and inhibiting apoptosis.

Resilience evaluation for water distribution system based on partial nodes' hydraulic information.

Accurate resilience evaluation for water distribution systems generally requires all nodes' hydraulic data which are usually obtained from a well-calibrated hydraulic model. However, in reality, few utilities maintain a workable hydraulic model, making the resilience evaluation far more from practicability. Under this condition, whether resilience evaluation can be realized based on a small amount of monitoring nodes is still a research gap. Therefore, this paper investigates the possibility of accurate resilience evaluation using partial nodes by answering two problems: (1) whether the importance of nodes differs in resilience evaluation; (2) what proportion of nodes are indispensable in resilience evaluation. Accordingly, the Gini index of nodes' importance and the error distribution of partial node resilience evaluation are computed and analyzed. A database including 192 networks is used. Results show that the importance of nodes in the resilience evaluation varies. The Gini index of nodes' importance is 0.604 ± 0.106. The proportion of nodes that meet the accuracy requirement of resilience evaluation is 6.5% ± 2%. Further analysis shows that the importance of nodes is determined by the transmission efficiency between water sources and consumption nodes, and the degree of a node's influence on other nodes. The optimal proportion of required nodes is controlled by a network's centralization, centrality, and efficiency. These results show that accurate resilience evaluation using partial nodes' hydraulic data is feasible and provide some basis for the resilience evaluation-orientated selection of monitoring nodes.