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Background/Objectives: Colorectal cancer (CRC) is characterized by a high rate of both incidence and mortality, and its treatment outcomes are often affected by recurrence and drug resistance. Ferroptosis, an iron-dependent programmed cell death mechanism triggered by lipid peroxidation, has recently gained attention as a potential therapeutic target. Graphene oxide (GO), known for its oxygen-containing functional groups, biocompatibility, and potential for functionalization, holds promise in cancer treatment. However, its role in ferroptosis induction in CRC remains underexplored. The objective of this study was to investigate the effects of High Carbonyl Graphene Oxide (HC-GO) on ferroptosis in CRC and elucidate the underlying mechanisms. Methods: In vitro assays were conducted to evaluate the impact of HC-GO on CRC cell proliferation, mitochondrial function, iron accumulation, lipid peroxidation, and reactive oxygen species (ROS) production. The ferroptosis inhibitor Fer-1 was used to confirm the role of ferroptosis in HC-GO's anti-tumor effects. In vivo, the anti-tumor activity of HC-GO was assessed in a CRC xenograft model, with organ toxicity evaluated. Results: HC-GO significantly inhibited CRC cell proliferation, induced mitochondrial damage, and enhanced iron accumulation, lipid peroxidation, and ROS production. It also downregulated the ferroptosis-inhibiting proteins GPX4 and SLC7A11, which were reversed by Fer-1, confirming the involvement of ferroptosis in HC-GO's anti-cancer effects. In vivo, HC-GO significantly suppressed tumor growth without noticeable toxicity to vital organs. Conclusions: HC-GO triggered ferroptosis in CRC cells by suppressing the System Xc−/GSH/GPX4 pathway, providing a novel therapeutic strategy for CRC treatment. These findings suggest HC-GO as a promising nanomedicine for clinical application, warranting further investigation to explore its potential in CRC therapy. [ABSTRACT FROM AUTHOR]

Background: To investigate the behavior of spiking neural networks (SNNs), the sensitivity of input perturbation serves as an effective metric for assessing the influence on the network output. However, existing methods fall short in evaluating the sensitivity of SNNs featuring biologically plausible leaky integrate-and-fire (LIF) neurons due to the intricate neuronal dynamics during the feedforward process. Methods: This paper first defines the sensitivity of a temporal-coded spiking neuron (SN) as the deviation between the perturbed and unperturbed output under a given input perturbation with respect to overall inputs. Then, the sensitivity algorithm of an entire SNN is derived iteratively from the sensitivity of each individual neuron. Instead of using the actual firing time, the desired firing time is employed to derive a more precise analytical expression of the sensitivity. Moreover, the expectation of the membrane potential difference is utilized to quantify the magnitude of the input deviation. Results/Conclusions: The theoretical results achieved with the proposed algorithm are in reasonable agreement with the simulation results obtained with extensive input data. The sensitivity also varies monotonically with changes in other parameters, except for the number of time steps, providing valuable insights for choosing appropriate values to construct the network. Nevertheless, the sensitivity exhibits a piecewise decreasing tendency with respect to the number of time steps, with the length and starting point of each piece contingent upon the specific parameter values of the neuron. [ABSTRACT FROM AUTHOR]

Blending with light oil is a commonly used and reliable method of crude oil transportation, and the blending ratio is a crucial operating parameter in determining the safe and efficient operation of the pipeline. In this paper, in-house flow and deposition experiments are used to evaluate the flow and deposition characteristics of crude oils with varying blending ratios. The results show that (1) blending with light oil basically does not affect the shape of the DSC curve of crude oil; (2) blending with light oil will not eliminate the thermal treatment effect, and the mixed oil flowability still remains highly dependent on the thermal treatment temperature; (3) blending with light oil can greatly decrease the abnormal point and oil viscosity, in which the low-temperature viscosity decreases more significantly; and (4) a wax deposition model of mixed oil is obtained through the fitting of Huang's model, where the blending ratio is a crucial factor in the determination of the model parameters k, m, and n. [ABSTRACT FROM AUTHOR]

Based on 360 event-based precipitation samples collected at six stations on the North Tibetan Plateau (NTP) in 2019–2020, we analyzed the influence of meteorological parameters, sub-cloud evaporation, moisture sources, and moisture transmission pathways on precipitation and its seasonal variations. The results show that precipitation δ18O, δ2H, and d-excess values show obvious seasonal variations, being depleted in winter and enriched in summer. Although temperature is an important variable that affects the change in δ18O values of precipitation, the results of the sub-cloud evaporation effect and moisture tracing show that differences in moisture sources caused by seasonal changes in large-scale water moisture transport are an important cause of seasonal changes in δ18O and d-excess of precipitation at NTP. Depleted δ18O and enriched d-excess in winter represent the source of moisture transported by the westerlies from the Mediterranean area and Central Asia. Enriched δ18O and d-excess values in summer precipitation are related to the temperature effect. In addition, the meridional motion of the atmospheric flow has an effect on the precipitation isotope values in the NTP. When the meridional circulation is enhanced, the water vapour from low latitudes is easily transported northwards, enriching the summer precipitation isotope values in the central and eastern parts of the plateau. This provides a new insight into the explanation of stable oxygen isotopes in climate proxies across the westerlies-dominated Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Intelligent monitoring approaches for long-term, real-time digitalization in structural health monitoring (SHM) are currently attracting significant interest. Among these, self-sensing cementitious composites stand out due to their easy preparation, cost-effectiveness, and excellent compatibility with concrete structures. However, the current research faces challenges, such as excessive conductive filler, difficulties in filler dispersion, and insufficient stress sensitivity and instability. This study presents a novel approach to these challenges by fabricating self-sensing cementitious sensors using silver nanoparticles (AgNPs), a new type of conductive filler. The percolation threshold of AgNPs in these materials was determined to be 0.0066 wt%, marking a reduction of approximately 90% compared to traditional conductive fillers. Moreover, the absorbance test with a UV spectrophotometer showed that AgNPs were well dispersed in an aqueous solution, which is beneficial for the construction of conductive pathways. Through various cyclic loading tests, it was observed that the self-sensing cementitious sensors with AgNPs exhibited robust pressure-sensitive stability. Additionally, their stress sensitivity reached 11.736, a value significantly surpassing that of conventional fillers. Regarding the conductive mechanism, when encountering the intricate environment within the cementitious material, AgNPs can establish numerous conductive pathways, ensuring a stable response to stress due to their ample quantity. This study provides a significant contribution to addressing the existing challenges in self-sensing cementitious materials and offers a novel reference for further research in this domain. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is an important task in natural language processing in which convolutional neural networks (CNNs) have been successfully applied. However, most existing CNNs can only extract predefined, fixed-scale sentiment features and cannot synthesize flexible, multi-scale sentiment features. Moreover, these models' convolutional and pooling layers gradually lose local detailed information. In this study, a new CNN model based on residual network technology and attention mechanisms is proposed. This model exploits more abundant multi-scale sentiment features and addresses the loss of locally detailed information to enhance the accuracy of sentiment classification. It is primarily composed of a position-wise gated Res2Net (PG-Res2Net) module and a selective fusing module. The PG-Res2Net module can adaptively learn multi-scale sentiment features over a large range using multi-way convolution, residual-like connections, and position-wise gates. The selective fusing module is developed to fully reuse and selectively fuse these features for prediction. The proposed model was evaluated using five baseline datasets. The experimental results demonstrate that the proposed model surpassed the other models in performance. In the best case, the model outperforms the other models by up to 1.2%. Ablation studies and visualizations further revealed the model's ability to extract and fuse multi-scale sentiment features. [ABSTRACT FROM AUTHOR]

Red fluorescent proteins (RFPs) have broad applications in life science research, and the manipulation of RFPs using nanobodies can expand their potential uses. However, the structural information available for nanobodies that bind with RFPs is still insufficient. In this study, we cloned, expressed, purified, and crystallized complexes formed by mCherry with LaM1, LaM3, and LaM8. Then, we analyzed the biochemical properties of the complexes using mass spectrometry (MS), fluorescence-detected size exclusion chromatography (FSEC), isothermal titration calorimetry (ITC), and bio-layer interferometry (BLI) technology. We determined the crystal structure of mCherry-LaM1, mCherry-LaM3, and mCherry-LaM8, with resolutions of 2.05 Å, 3.29 Å, and 1.31 Å, respectively. In this study, we systematically compared various parameters of several LaM series nanobodies, including LaM1, LaM3, and LaM8, with previously reported data on LaM2, LaM4, and LaM6, specifically examining their structural information. After designing multivalent tandem LaM1-LaM8 and LaM8-LaM4 nanobodies based on structural information, we characterized their properties, revealing their higher affinity and specificity to mCherry. Our research provides novel structural insights that could aid in understanding nanobodies targeting a specific target protein. This could provide a starting point for developing enhanced mCherry manipulation tools. [ABSTRACT FROM AUTHOR]

As an invasive alien species, Asian giant hornets are spreading rapidly and widely in Washington State and have caused significant disturbance to the daily life of residents. Therefore, this paper studies the hornets' spread and classification models based on the GM-Logistic and CSRF models, which are significant for using limited resources to control pests and protect the ecological environment. Firstly, by combining the improved grey prediction model (GM) with the logistic model, this paper proposes a GM-Logistic model to obtain hornets' spread rules regarding spatial location distribution and population quantity. The GM-Logistic model has higher accuracy and better fitting effect when only a few non-equally spaced sequences data are used for prediction. Secondly, a cost-sensitive random forest (CSRF) model was proposed to solve the problems of hornets' classification and priority survey decisions in unbalanced datasets. The hornets' binary classification model was established through feature extraction, the transformation from an unbalanced dataset to a balanced dataset, and the training dataset. CSRF improves the adaptability and robustness of the original classifier and provides a better classification effect on unbalanced datasets. CSRF outperforms the Random Forest, Classification and Regression Trees, and Support Vector Machines in performance evaluation indexes such as classification accuracy, G-mean, F1-measure, ROC curve, and AUC value. Thirdly, this paper adds human control factors and cycle parameters to the logistic model, obtaining the judgment conditions of report update frequency and pest elimination. Finally, the goodness-of-fit test on each model shows that the models established in this paper are feasible and reasonable. [ABSTRACT FROM AUTHOR]

To investigate the topographic effects on precipitation in the Liupan Mountains Area of Northwest China, three micro rain radars, located at a ridge, west valley, and east valley in the area, respectively, were used to observe precipitation processes. By comparing the characteristics of stratiform precipitation at three sites, it was found that (i) the effective radar reflectivity and characteristic falling velocity of hydrometeors at the ridge and east valley were larger than those at the west valley; (ii) the diameter and density of solid hydrometeors at the ridge and east valley were slightly larger than those at the west valley; and (iii) there was also a higher occurrence frequency of larger graupel at the ridge. It is inferred that the precipitable water vapor at the ridge and east valley is richer than at the west valley, which leads to a larger aggregation efficiency and degrees of riming at the former than the latter. Besides, forced uplifting of water vapor over the mountain area around the ridge may play a part in topographic supercooling, which leads to enhanced riming of supercooled liquid water. The conclusions will contribute to a better understanding of the mechanisms of precipitation–terrain interactions in the area. [ABSTRACT FROM AUTHOR]

The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China's terrain. It is also an intersection area of two air currents, which are caused by the combined action of the Qinghai–Tibet Plateau, the middle and lower levels of the westerly belt and the edge of the monsoon area. LPM is one of the main air water vapour transport pathways in Northwest China as well as a main water conservation area for nearly ten million people. Research on atmospheric precipitable water vapour (PWV) variation characteristics in LPM is beneficial for understanding the mechanisms of orographic precipitation and improving the effects of weather modification. Based on the data from 10 Global Navigation Satellite System Meteorology (GNSS/MET) stations for 6 years and the data of automatic weather stations in the LPM, the temporal and spatial variation characteristics of PWV in the LPM were analysed, and the differences in PWV in the 24 h before and after precipitation were compared in this study. The results showed that the hourly, monthly and seasonal variations in PWV displayed obvious patterns. PWV increased rapidly 10 h ahead of precipitation, while it decreased rapidly within 10 h after precipitation, which was slower than that before precipitation. In terms of spatial distribution, PWV was larger in the south than in the north and larger in the east than in the west. Although the precipitation on the LPM peak was the highest in the whole LPM area, its PWV was always the lowest, indicating that the PWV was obviously affected by the air temperature. This showed that under the same water vapour condition, precipitation was more likely to form in the area with low temperature, and the precipitation was larger, which also provided a train of thought for improving the method of artificial precipitation enhancement by using the condensation catalyst. [ABSTRACT FROM AUTHOR]

Two years of data on cloud condensation nuclei (CCN) measured at the Liupan Mountain (LPS) Meteorological Station from August 2020 to November 2021 were analyzed in this study. The results show that the mean annual CCN concentration was 851 cm−3 and that the mean concentration of CCN increases with the supersaturation degree. The curves of the diurnal variation in CCN concentration show one peak and one valley, which correspond to the diurnal variation in the mixed-layer height and valley wind. Regarding seasonal variations, the CCN concentration, as well as the degree of internal mixing, is higher in the spring and winter, while the degree of external mixing is higher in the summer and autumn. The transport of CCN is closely related to the wind transport evolution, and the southeast and southwest sides of the LPS station contribute more to the CCN concentration in the spring and winter due to central heating in the wintertime. Though correlations between CCN concentration and pressure are scarce, the CCN concentration and temperature (or humidity) are positively (or negatively) correlated, especially in the spring. Furthermore, the 48-h backward trajectory analysis indicates that the sources in the northwest direction are a major contributor to the CCN concentration. The pollutants mainly came from the northwest and southwest sides, according to the analysis of potential sources using the PSCF and CWT approach. The study of CCN evolution and contribution area is beneficial for further research on the physical properties of cloud droplets, the influence of mountains on CCN changes and the role of CCN in terrain cloud precipitation, which are significant for the improvement of weather modification techniques. [ABSTRACT FROM AUTHOR]

In order to improve the durability of the asphalt pavement on a cement concrete bridge, this study investigated the effect of the modulus of the asphalt mixture at the bottom layer on the mechanical response of bridge pavement, along with a type of emerging bridge pavement structure. In addition, the design method and pavement performance of a high-modulus asphalt mixture were investigated using laboratory and field tests, and the life expectancy of the deck pavement structure was predicted based on the rutting deformation. The results showed that the application of a high-modulus asphalt mixture as the bottom asphalt layer decreased the stress level of the pavement structure. The new high-modulus asphalt mixture displayed excellent comprehensive performance, i.e., the dynamic stability reached 9632 times/mm and the fatigue life reached 1.65 million cycles. Based on the rutting depth prediction, using high-modulus mixtures for the bridge pavement prolonged the service life from the original 5 years to 10 years, which significantly enhanced the durability of the pavement structure. These research results could be of potential interest for practical applications in the construction industry. [ABSTRACT FROM AUTHOR]

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs. [ABSTRACT FROM AUTHOR]

The synthesis of substituted indoles has received great attention in the field of organic synthesis methodology. C–H activation makes it possible to obtain a variety of designed indole derivatives in mild conditions. Ruthenium catalyst, as one of the most significant transition-metal catalysts, has been contributing in the synthesis of indole scaffolds through C–H activation and C–H activation on indoles. Herein, we attempt to present an overview about the construction strategies of indole scaffold and site-specific modifications for indole scaffold via ruthenium-catalyzed C–H activations in recent years. [ABSTRACT FROM AUTHOR]

Clay ball is a pavement surface defect which refers to a clump in which clay or dirt is mixed with hot asphalt mixture. Clay ball is typically caused by a combination of aggregate contamination of clay or soil, high aggregate moisture, and low production temperature at the asphalt plant. It usually appears a few weeks or months after paving under traffic load, after being liquefied and knocked from the pavement surface. Clay balls can be the source of potholing, raveling, and other issues such as moisture infiltration and reduced ride quality. This paper presents an investigation of the clay balls on US-31 one winter after construction in Hamilton County, Indiana. In order to understand the pavement condition, their severity was measured using both visual observation and infrared image collection system. In addition, a clay ball amount, its distribution pattern, and cores condition were evaluated. A precipitation effect on clay ball formation was investigated for finding a cause of the clay balls. The investigation found that infrared image collection system was appropriate in detecting the clay balls. The clay balls were elliptic in shape with 2.5 cm to 10 cm in diameter, and the maximum clay ball depth was almost penetrating the entire surface course. It was also found that the asphalt paving on the raining days or right after raining could increase the potential of clay balls. Monitoring of aggregate moisture during construction on or after raining days should be able to reduce the risk of clay balls. [ABSTRACT FROM AUTHOR]