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Cement–Soil Mixing (CSM) Pile is an important technology for soft ground reinforcement, and its as-formed compressive strength directly affects engineering design and construction quality. To address the significant discrepancy between laboratory-tested strength and field as-formed strength arising from differing environmental conditions, this study conducted modified laboratory experiments simulating key field formation characteristics. A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. Engineering validation demonstrated that the model achieved an RMSE of 0.138, an MAE of 0.112, and an R2 of 0.961. It effectively addresses the issue of large prediction deviations caused by insufficient environmental simulation in traditional mix proportion tests. The research findings establish a quantitative relationship between as-formed strength and preparation parameters, providing an effective experimental improvement and strength prediction method for the engineering design of CSM Pile.

Prepulse inhibition (PPI), the suppression of the startle reflex when the startling stimulus is shortly preceded by a weaker non-startling sensory stimulus (prepulse), can be enhanced by selective attention to the prepulse with a marked prepulse-feature specificity. To determine if the attentional modulation of PPI in rats can also be perceptual location specific, this study investigated whether fear-conditioning of a prepulse perceived at a location can enhance PPI only when the conditioned prepulse is perceived at that conditioned location. A continuous narrowband noise (NBN) was delivered by each of the two spatially separated loudspeakers in the frontal azimuth with a silent gap embedded in each NBN. The inter-loudspeaker interval was 1 ms (either left or right loudspeaker leading). Due to the precedence effect, both the NBN and gap images were perceived at the leading loudspeaker. The perceptually fused gap was used as the prepulse. To fear-condition one gap prepulse, which was perceived at one loudspeaker, the prepulse was paired with footshock in a temporally precise manner and the other gap (the conditioning-control prepulse) perceived at the other (opposite) loudspeaker was paired with footshock in a random manner. Compared to PPI before conditioning, PPI induced by the fear-conditioned gap perceived at the fear-conditioned loudspeaker, but not that by the conditioning-control gap, was significantly enhanced. Thus, attentional modulation of PPI can be not only prepulse-feature specific, but also perceptual location specific, and involves combined central processes for content and location information.

Ferroptosis is a form of regulated cell death driven by the accumulation of iron-dependent lipid peroxides, and ferroptosis-mediated cancer therapy has gained considerable attention. Despite emerging evidence that ferroptosis induction effectively suppresses hepatocellular carcinoma (HCC) progression and enhances chemosensitivity, the development of resistance to ferroptosis-targeting therapies remains a critical challenge. Natural active compounds have great potential in cancer treatment. The impact of 6-ME on the cell viability of HCC cells was assessed using the Cell Counting Kit-8 (CCK-8) assay and colony formation assay. Furthermore, cellular morphology of HCC cells was visualized under inverted fluorescence microscopy. Intracellular reactive oxygen species (ROS) and lipid peroxidation levels were quantified using fluorescence probes and determined by flow cytometry analysis. The expression of ferroptosis-related proteins and genes was determined via Western blot and quantitative real-time PCR analyses. Here, we demonstrate that 6-Methoxydihydrosanguinarine (6-ME), an alkaloid from , exerts anti-tumor functions in HCC cells via ferroptosis. Stimulation with 6-ME induces intracellular ROS production, cell growth inhibition, and cell death in HCC cells, and these effects can be weakened by the ROS scavenger GSH or NAC and ferroptosis inhibitors deferoxamine mesylate (DFO) or ferrostatin-1 (Fer-1). Mechanistically, 6-ME downregulates the expression of the key ferroptosis defense enzyme GPX4 at the transcriptional level, leading to excessive lipid peroxidation and ferroptosis in HCC cells. Importantly, low concentrations of 6-ME also enhanced the ferroptosis sensitivity induced by RSL3 and IKE in HCC cells. These findings reveal that the natural product 6-ME exerts anti-tumor functions in HCC cells ferroptosis and underscore the potential of 6-ME administered alone or in combination with canonical ferroptosis inducers for the treatment of HCC patients.

Ferroptosis, a recently elucidated style of regulated cell death, has emerged as a significant area of investigation in cancer biology. Natural active compounds that have anti-cancer effects are promising candidates for cancer prevention. Iberverin, a natural compound derived from Brassica oleracea var. capitata, has been shown to exert anti-tumor activities in some cancers. However, its role in hepatocellular carcinoma (HCC) cells and the molecular mechanisms are still poorly understood. In this study, we proved that iberverin can induce intracellular reactive oxygen species (ROS) generation to inhibit cell proliferation and initiate ferroptotic cell death in HCC cells, which can be eradicated by the ferroptosis inhibitor ferrostatin-1 (Fer-1) or deferoxamine mesylate (DFO) and ROS scavenger (GSH or NAC). Mechanistically, iberverin treatment can simultaneously downregulate SLC7A11 mRNA level and degrade GPX4 through the ubiquitination pathway, leading to lipid peroxidation and ferroptotic cell death in HCC cells. Significantly, a low dose of iberverin can remarkably increase the sensitivity of HCC cells to ferroptosis induced by canonical ferroptosis inducers RSL3 and imidazole ketone erastin (IKE). This study uncovers a critical function of iberverin in preventing HCC through ferroptosis and provides a promising strategy for HCC treatment either via iberverin alone or in combination with canonical ferroptosis inducers in the future.

The natural product auraptene can influence tumor cell proliferation and invasion, but its effect on hepatocellular carcinoma (HCC) cells is unknown. Here, we report that auraptene can exert anti-tumor effects in HCC cells via inhibition of cell proliferation and ferroptosis induction. Auraptene treatment induces total ROS and lipid ROS production in HCC cells to initiate ferroptosis. The cell death or cell growth inhibition of HCC cells induced by auraptene can be eliminated by the ROS scavenger NAC or GSH and ferroptosis inhibitor ferrostatin-1 or Deferoxamine Mesylate (DFO). Mechanistically, the key ferroptosis defense protein SLC7A11 is targeted for ubiquitin–proteasomal degradation by auraptene, resulting in ferroptosis of HCC cells. Importantly, low doses of auraptene can sensitize HCC cells to ferroptosis induced by RSL3 and cystine deprivation. These findings demonstrate a critical mechanism by which auraptene exhibits anti-HCC effects via ferroptosis induction and provides a possible therapeutic strategy for HCC by using auraptene or in combination with other ferroptosis inducers.

In this paper, we constructed the Exospheric Temperature Models (ETM) on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs). The EOFs of the exospheric temperature can be derived either from satellite data directly or from the outputs of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) and MSIS models by applying the Principal Component Analysis method. Then, the thermospheric mass densities calculated from ETM are used to compare with the observed data in order to evaluate the performance of different ETM models. It was found that all these three models can provide good specification of thermospheric density including day‐night, seasonal, and latitudinal variations. However, the ETM based on CHAMP and GRACE data gives a better performance in modeling the Equatorial Thermospheric Anomaly and the Midnight Density Maximum features than the MSIS‐ETM and TIEGCM‐ETM. Specifically, independent SWARM‐C data comparison showed that the Relative Deviations and corresponding Root‐Mean‐Square‐Errors of our Texo models are less than 8.9% and 22.8%, much better than the MSIS‐00 model.

The automation level has been improved rapidly with the introduction of large-scale measurement technologies, such as indoor global positioning system, into the production process among the fields of car, ship, and aerospace due to their excellent measurement characteristics. In fact, the objects are usually in motion during the real measurement process; however, the dynamic measurement characteristics of indoor global positioning system are much limited and still in exploration. In this research, we focused on the dynamic tracking performance of indoor global positioning system and then successfully built a mathematical model based on its measurement principles. We first built single and double station system models with the consideration of measurement objects’ movement. Using MATLAB simulation, we realized the dynamic measurement characteristics of indoor global positioning system. In the real measurement process, the experimental results also support the mathematical model that we built, which proves a great success in dynamic measurement characteristics. We envision that this dynamic tracking performance of indoor global positioning system would shed light on the dynamic measurement of a motion object and therefore make contribution to the automation production.

The crosstalk between tumor progression and ferroptosis is largely unknown. Here, we identify malate dehydrogenase 2 (MDH2) as a key regulator of ferroptosis. MDH2 deficiency inhibits the growth of hepatocellular carcinoma (HCC) cells and enhances their sensitivity to ferroptosis induced by RAS-selective lethal 3 (RSL3), a compound known to cause ferroptosis. MDH2 knock-down enhances RSL3-induced intracellular reactive oxygen species, free iron ions and lipid per-oxides levels, leading to HCC ferroptotic cell death which is rescued by ferrostatin-1 and iron chelator deferiprone. Importantly, the inhibition of HCC cell growth caused by MDH2 deficiency is partially rescued by ferroptosis blockade. Mechanistically, MDH2 resists RSL3-induced ferroptosis sensitivity dependent on glutathione peroxidase 4 (GPX4), an enzyme responsible for scavenging lipid peroxides, which is stabilized by MDH2 in HCC. The protein expressions of MDH2 and GPX4 are positively correlated with each other in HCC cell lines. Furthermore, through our UALCAN website analysis, we found that MDH2 and GPX4 are highly expressed in HCC samples. These findings reveal a critical mechanism by which HCC evades ferroptosis via MDH2-mediated stabilization of GPX4 to promote tumor progression and underscore the potential of MDH2 inhibition in combi-nation with ferroptosis inducers for the treatment of HCC.

Obstructive Sleep Apnea (OSA) is a sleep disorder where the brain and body receive insufficient oxygen during sleep. Traditional diagnosis involves Polysomnography (PSG), which is time-consuming, tedious, subjective, and costly in clinical settings. To address these drawbacks, computer-assisted diagnosis techniques have emerged, utilizing a single physiological signal. This study aims to introduce an innovative method for automatically detecting OSA based on the dynamics of the ECG system. The approach combines tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD), and three-dimensional (3D) phase space for feature extraction, capturing clinically relevant information from OSA ECG recordings. Neural networks are employed to model and identify ECG system dynamics via deterministic learning theory, classifying normal and OSA ECG signals based on differences in dynamics using a bank of dynamical estimators. An assessment is conducted utilizing a 10-fold cross-validation methodology on a PhysioNet apnea-ECG dataset, which comprises 70 nocturnal recordings derived from an equal number of subjects. The empirical outcomes demonstrate that the introduced approach, which amalgamates a classifier based on neural network principles and the recommended attributes, attains superior accuracy (98.27%), sensitivity (97.68%), and specificity (98.63%) in contrast to conventional PSG. The results corroborate the suggested technique as a viable substitute for automatic OSA detection in a clinical setting.

Obstructive Sleep Apnea (OSA) is a sleep disorder where the brain and body receive insufficient oxygen during sleep. Traditional diagnosis involves Polysomnography (PSG), which is time-consuming, tedious, subjective, and costly in clinical settings. To address these drawbacks, computer-assisted diagnosis techniques have emerged, utilizing a single physiological signal. This study aims to introduce an innovative method for automatically detecting OSA based on the dynamics of the ECG system. The approach combines tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD), and three-dimensional (3D) phase space for feature extraction, capturing clinically relevant information from OSA ECG recordings. Neural networks are employed to model and identify ECG system dynamics via deterministic learning theory, classifying normal and OSA ECG signals based on differences in dynamics using a bank of dynamical estimators. An assessment is conducted utilizing a 10-fold cross-validation methodology on a PhysioNet apnea-ECG dataset, which comprises 70 nocturnal recordings derived from an equal number of subjects. The empirical outcomes demonstrate that the introduced approach, which amalgamates a classifier based on neural network principles and the recommended attributes, attains superior accuracy (98.27 % ), sensitivity (97.68 % ), and specificity (98.63 % ) in contrast to conventional PSG. The results corroborate the suggested technique as a viable substitute for automatic OSA detection in a clinical setting.