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Rare-earth oxysulfides are a class of functional ceramic materials with excellent physico-chemical properties and rich functionality. In this study, La2O2S powders were prepared from La2S3 and La2O3 powders at 1000 °C by pressureless sintering. La2O2S compacts were synthesized from La2S3 and La2O3 powders at 800–1600 °C by spark plasma sintering. The influences of sintering temperature and time on the preparation of La2O2S were studied. XRD results indicated that La2O2S ceramics were synthesized successfully and that the lattice constants of La2O2S were close to the theoretical values. SEM showed that the microstructure of La2O2S compacts was homogeneous. The specific heat of La2O2S mainly came from lattice contribution, and its Debye temperature was 237 K. The UV–visible absorption spectra showed different absorption levels in the 240–300 nm range. Raman spectroscopy revealed distinct peaks at different temperatures, indicating changes in the covalence band. The relative density of La2O2S ceramics was 92% and lower than theoretical values. Hardness of the synthesized La2O2S was greater than that of Gd2O2S ceramics. [ABSTRACT FROM AUTHOR]

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas. [ABSTRACT FROM AUTHOR]

Applying plant-derived fungicides is a safe and sustainable way to control wheat scab. In this study, volatile organic compounds (VOCs) of wheat cultivars with and without the resistance gene Fhb1 were analyzed by GC-MS, and 2-phenylethanol was screened out. The biocontrol function of 2-phenylethanol on Fusarium graminearum was evaluated in vitro and in vivo. Metabolomics analysis indicated that 2-phenylethanol altered the amino acid pathways of F. graminearum, affecting its normal life activities. Under SEM and TEM observation, the mycelial morphology changed, and the integrity of the cell membrane was destroyed. Furthermore, 2-phenylethanol could inhibit the production of mycotoxins (DON, 3-ADON, 15-ADON) by F. graminearum and reduce grain contamination. This research provides new ideas for green prevention and control of wheat FHB in the field. [ABSTRACT FROM AUTHOR]

The theory of compressive sampling (CS) has revolutionized data compression technology by capitalizing on the inherent sparsity of a signal to enable signal recovery from significantly far fewer samples than what is required by the Nyquist–Shannon sampling theorem. Recent advancement in deep generative models, which can represent high-dimension data in a low-dimension latent space efficiently when trained with big data, has been used to further reduce the sample size for image data compressive sampling. However, compressive sampling for 1D time series data has not significantly benefited from this technological progress. In this study, we investigate the application of different architectures of deep neural networks suitable for time series data compression and propose an efficient method to solve the compressive sampling problem on one-dimensional (1D) structural health monitoring (SHM) data, based on block CS and the vector quantized–variational autoencoder model with a naïve multitask paradigm (VQ-VAE-M). The proposed method utilizes VQ-VAE-M to learn the data characteristics of the signal, replaces the "hard constraint" of sparsity to realize the compressive sampling signal reconstruction and thereby does not need to select the appropriate sparse basis for the signal. A comparative analysis against various CS methods and other deep neural network models was performed in both synthetic data and real-world data from two real bridges in China. The results have demonstrated the superiority of the proposed method, with achieving the smallest reconstruction error of 0.038, 0.034 and 0.021, and the highest reconstruction accuracy of 0.882, 0.892 and 0.936 for compression ratios of 4.0, 2.66, and 2.0, respectively. [ABSTRACT FROM AUTHOR]

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial–temporal graph convolutional networks' bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust. [ABSTRACT FROM AUTHOR]

Colorectal cancer is one of the most prevalent and lethal malignancies, affecting approximately 900,000 individuals each year worldwide. Patients with colorectal cancer are found with elevated serum interleukin-6 (IL-6), which is associated with advanced tumor grades and is related to their poor survival outcomes. Although IL-6 is recognized as a potent inducer of colorectal cancer progression, the detail mechanisms underlying IL-6-induced colorectal cancer epithelial–mesenchymal transition (EMT), one of the major process of tumor metastasis, remain unclear. In the present study, we investigated the regulatory role of IL-6 signaling in colorectal cancer EMT using HCT116 human colorectal cancer cells. We noted that the expression of epithelial marker E-cadherin was reduced in HCT116 cells exposed to IL-6, along with the increase in a set of mesenchymal cell markers including vimentin and α-smooth muscle actin (α-SMA), as well as EMT transcription regulators—twist, snail and slug. The changes of EMT phenotype were related to the activation of Src, FAK, ERK1/2, p38 mitogen-activated protein kinase (p38MAPK), as well as transcription factors STAT3, κB and C/EBPβ. IL-6 treatment has promoted the recruitment of STAT3, κB and C/EBPβ toward the Twist promoter region. Furthermore, the Src-FAK signaling blockade resulted in the decline of IL-6 induced activation of ERK1/2, p38MAPK, κB, C/EBPβ and STAT3, as well as the decreasing mesenchymal state of HCT116 cells. These results suggested that IL-6 activates the Src-FAK-ERK/p38MAPK signaling cascade to cause the EMT of colorectal cancer cells. Pharmacological approaches targeting Src-FAK signaling may provide potential therapeutic strategies for rescuing colorectal cancer progression. [ABSTRACT FROM AUTHOR]

Oxide metallurgy technology can improve the microstructure of a coarse-grained heat-affected zone (CGHAZ) but introduces extra inclusions. Local corrosion behavior of the CGHAZ of a Zr–Ti–Al–RE deoxidized steel was investigated in this work using theoretical calculations and experimental verification. The modified inclusions have a (Zr–Mg–Al–Ca–RE)Ox core claded by a CaS and TiN shell. CaS dissolves first, followed by the oxide core, leaving TiN parts. This confirms that the addition of rare earth can reduce lattice distortion and prevent a galvanic couple between the inclusions and the matrix, while the chemical dissolution of CaS causes localized acidification, resulting in the pitting corrosion initiation. [ABSTRACT FROM AUTHOR]

As a newly identified type of programmed cell death, cuproptosis may have an impact on cancer development, including clear cell renal cell carcinoma (ccRCC). Herein, we first noticed that the expression levels of cuproptosis regulators exhibited a tight correlation with the clinicopathological characteristics of ccRCC. The cuproptosis-sensitive sub-type (CSS), classified via consensus clustering analysis, harbored a higher overall survival rate compared to the cuproptosis-resistant sub-type (CRS), which may have resulted from the differential infiltration of immune cells. FDX1, the cuproptosis master regulator, was experimentally determined as a tumor suppressor in ccRCC cells by suppressing the cell growth and cell invasion of ACHN and OSRC-2 cells in a cuproptosis-dependent and -independent manner. The results from IHC staining also demonstrated that FDX1 expression was negatively correlated with ccRCC tumor initiation and progression. Furthermore, we identified the miR-21-5p/FDX1 axis in ccRCC and experimentally verified that miR-21-5p directly binds the 3′-UTR of FDX1 to mediate its degradation. Consequently, a miR-21-5p inhibitor suppressed the cell growth and cell invasion of ACHN and OSRC-2 cells, which could be compensated by FDX1 knockdown, reinforcing the functional linkage between miR-21-5p and FDX1 in ccRCC. Finally, we evaluated the ccRCC tumor microenvironment under the miR-21-5p/FDX1 axis and noted that this axis was strongly associated with the infiltration of immune cells such as CD4+ T cells, Treg cells, and macrophages, suggesting that this signaling axis may alter microenvironmental components to drive ccRCC progression. Overall, this study constructed the miR-21-5p/FDX1 axis in ccRCC and analyzed its potential impact on the tumor microenvironment, providing valuable insights to improve current ccRCC management. [ABSTRACT FROM AUTHOR]

Synthetic nanocarriers are a promising therapeutic delivery strategy. However, these systems are often hampered by inherent disadvantages such as strong biotoxicity and poor biocompatibility. To overcome these issues, biological carriers with commonly used chemotherapy drugs have been developed. In this work, engineered bacterial ghosts (BGs) originated from probiotic Escherichia coli Nissle 1917 (EcN) were devised to specifically target acidic extracellular environments of tumor tissue. To improve the production efficiency and safety, a novel lysis protein E from phage α3 was applied to produce EcN BGs under high growth densities in high quality. In addition, the acidity-triggered rational membrane (ATRAM) peptides were displayed in EcN BGs to facilitate specific cancer cell internalization within the acidic tumor microenvironment before drug release. In conclusion, the engineered EcN BGs offer a promising means for bionic bacteria construction for hepatocellular carcinoma therapy. [ABSTRACT FROM AUTHOR]

In this work, a series of Gd-based amorphous/crystalline composite fibers (ANCFs) were prepared by regulating the Gd content in Gd-Co-Al alloys using the melt-extracted method. Compared to the amorphous alloy, the ANCFs display excellent magnetic refrigeration capacity (RC). Among them, Gd85Co5Al10 ANCF had the largest RC (841 J kg−1) and the widest working temperature range (245 K). Compared with Gd70Co10Al20, RC and working temperature range increased by 56% and 119%, respectively. This superior property is attributed to the ideal coupling between the amorphous phase and the crystalline. This result opens a new door to optimize the magnetic refrigeration capacity by controlling the amorphous crystalline composite structure. [ABSTRACT FROM AUTHOR]

Natural naphthoquinones and their derivatives exhibit a broad spectrum of pharmacological activities and have thus attracted much attention in modern drug discovery. However, it remains unclear whether naphthoquinones are potential drug candidates for anti-angiogenic agents. The aim of this study was to evaluate the anti-angiogenic properties of a novel naphthoquinone derivative, PPE8, and explore its underlying mechanisms. Determined by various assays including BrdU, migration, invasion, and tube formation analyses, PPE8 treatment resulted in the reduction of VEGF-A-induced proliferation, migration, and invasion, as well as tube formation in human umbilical vein endothelial cells (HUVECs). We also used an aorta ring sprouting assay, Matrigel plug assay, and immunoblotting analysis to examine PPE8's ex vivo and in vivo anti-angiogenic activities and its actions on VEGF-A signaling. It has been revealed that PPE8 inhibited VEGF-A-induced micro vessel sprouting and was capable of suppressing angiogenesis in in vivo models. In addition, PPE8 inhibited VEGF receptor (VEGFR)-2, Src, FAK, ERK1/2, or AKT phosphorylation in HUVECs exposed to VEGF-A, and it also showed significant decline in xenograft tumor growth in vivo. Taken together, these observations indicated that PPE8 may target VEGF-A–VEGFR-2 signaling to reduce angiogenesis. It also supports the role of PPE8 as a potential drug candidate for the development of therapeutic agents in the treatment of angiogenesis-related diseases including cancer. [ABSTRACT FROM AUTHOR]

The ADG of CMV-fed cattle was half that of cattle fed a concentrate diet, whereas grass-fed cattle gained about one-third that of concentrate-fed cattle. The blood plasma concentration of omega-6 fatty acids was reduced and the concentration of omega-3 fatty acids was increased for all pasture-fed steers compared with concentrate-fed steers, resulting in a more favorable omega-6 to omega 3 fatty acid ratio. [Extracted from the article]

The N application used for the production of forage affects environment stress. The N application rate used for the production of the whole maize (Zea mays L.) plant (WMP) affects the produced feed and environment. We carried out a 2-year field experiment aiming to measure how N application rates affect WMP forage production, and estimated the impact on the environment. Five N application treatments (0, 100, 200, 300 and 400 kg N ha−1) were included in our study. The results showed that N application improved forage yield, achieving a higher economic and ecological profit. After reaching a certain optimal rate, however, increasing the N rate further no longer increased the yield and quality of WMP forage, but instead greatly increased estimated N losses, thereby reducing ecological profits. The comprehensive benefit of the optimal N rate was ordered: the optimal N rate that maximized agronomic profit > that maximized economic profit > that maximized ecological benefit. The optimal N rate maximizing ecological profit was lower by 21% and 37% than that maximizing economic profit and forage yield, respectively. N application rates with the highest ecological profit (USD 2478 ha−1 in 2017 and USD 2448 ha−1 in 2018) were 248 and 245 kg N ha−1, respectively, in 2017 and 2018. The optimized N rate that maximized ecological profit maintained the economic profit while reducing N fertilizer input and associated N losses; it also carried a lower economic and ecological cost due to estimated N losses. Ecological criteria, which combine economic profit and economic losses due to their environmental impact, are more efficient than agronomic or economic criteria when used to provide guidance for WMP forage production. Therefore, in WMP forage production, optimizing N application rate by ecological criteria could maintain a high forage yield and economic profit, but greatly reduce input costs and ecological stress, maximizing ecological profit. [ABSTRACT FROM AUTHOR]

Since glucose biosensors are one of the most popular and widely used point-of-care testing devices, a novel electrochemical enzyme-linked immunosorbent assay (ELISA) for protein biomarkers has been developed based on a glucose detection strategy. In this study, α-fetoprotein (AFP) was used as the target protein. An electrochemical ELISA system was constructed using anti-AFP antibodies immobilized on microwell plates as the capture antibody (Ab1) and multi-label bioconjugates as signal tracer. The bioconjugates were synthesized by attaching glucoamylase and the secondary anti-AFP antibodies (Ab2) to gold nanoparticles (AuNPs). After formation of the sandwich complex, the Ab2-glucoamylase-AuNPs conjugates converted starch into glucose in the presence of AFP. The concentration of AFP can be calculated based on the linear relation between AFP and glucose, the concentration of which can be detected by the glucose biosensor. When the AFP concentration ranged from 0.05 to 100 ng/mL, a linear calibration plot (i (μA) = 13.62033 - 2.86252 logCAFP (ng/mL), r = 0.99886) with a detection limit of 0.02 ng/mL was obtained under optimal conditions. The electrochemical ELISA developed in this work shows acceptable stability and reproducibility, and the assay for AFP spiked in human serum also shows good recovery (97.0%-104%). This new method could be applied for detecting any protein biomarker with the corresponding antibodies. [ABSTRACT FROM AUTHOR]

Three-dimensional aerogels have great potential for antibiotic removal from aqueous solution due to their excellent solution mass transfer channels and special morphology. Herein, the metal ions were bound with alginate to form alginate-Fe, alginate-Cu, and alginate-Fe-Cu hydrogels, then they were used as nucleation sites for metal organic framework (MOF) growth to obtain MAlgs gels, respectively. Considering the aqueous environmental stability of MOFs particles, the alginate and MOF particles in MAlgs aerogels were pyrolyzed as templates to obtain the derived carbon aerogel CMAlgs. The results showed that the adsorption capacity of MAlgs-Fe-Cu aerogel was higher than that of MAlg-Cu and MAlg-Fe aerogels, up to ~130 mg·g−1. The adsorption performance of carbon aerogel CMAlg-Cu decreased obviously because of the decrease of pore size and oxygen-containing functional groups. The adsorption process is a combination of physical adsorption and chemical adsorption. In addition, CMAlgs aerogels exhibit better recyclability than MAlgs aerogels. This work provides a new strategy for fabricating MOFs-templated in-situ grown carbon aerogels for water purification. [ABSTRACT FROM AUTHOR]

Abnormal weather conditions due to climate change are currently increasing on both global and local scales. It is therefore important to ensure the safety of the areas where major national facilities are located by analyzing risk quantitatively and re-evaluating the existing major facilities, such as nuclear power plants, considering the load and capacity of extreme flood conditions. In this study, a risk analysis method is developed that combines flood hazard curves with fragility curves using hydraulic and hydrological models by GIS tools and the @RISK model for the probabilistic flood analysis of nuclear power plant sites. A two-dimensional (2D) analysis is first carried out to estimate flood depths in various watershed scenarios, and a representative hazard curve for both external and internal flooding is made by applying a verified probability distribution type for the flood watersheds. For the analysis of flooding within buildings, an internal grid is constructed using GIS with related design drawings, and based on the flood depth results of the 2D analysis, a hazard curve for the representative internal inundation using a verified probability distribution type is presented. In the present study, walkdowns with nuclear experts are conducted around the nuclear power plant area to evaluate the fragile structures and facilities under possible flooding. After reviewing the 2D inundation analysis results based on the selected major equipment and facilities, the zones requiring risk assessment are re-assigned. A fragility curve applying probability distribution for the site's major equipment and facilities is also presented. Failure risk analysis of the major facilities is then conducted by combining the proposed hazard and fragility curves. Results in the form of quantitative values are obtained, and the indicators for risks as well as the reliability and optimal measures to support decision-making are also presented. Through this study, it is confirmed that risk assessment based on the proposed probabilistic flood analysis technique is possible for flood events occurring at nuclear power plant sites. [ABSTRACT FROM AUTHOR]

Falls are seriously threatening the health of elderly. In order to reduce the potential danger caused by falls, this paper proposes a two-stage fall recognition algorithm based on human posture features. For preprocessing, we construct the new key features: deflection angles and spine ratio to describe the changes of human posture based on the human skeleton extracted by OpenPose. In the first stage, based on the variables: tendency symbol and steady symbol integrated by the scattered key features, we divide the human body state into three states: stable state, fluctuating state, and disordered state. By analyzing whether the body is in a stable state, the ADL (activities of daily living) actions with high stability can be preliminarily excluded. In the second stage: to further identify the confusing ADL actions and the fall actions, we innovatively design a time-continuous recognition algorithm. When human body is constantly in an unstable state, the human posture features: compare value γ , energy value ε , state score τ are proposed to form a feature vector, and support vector machine (SVM), K nearest neighbors (KNN), decision tree (DT), random forest (RF) are utilized for classification. Experiment results demonstrate that SVM with linear kernel function can distinguish falling actions best and our approach achieved a detection accuracy of 97.34%, precision of 98.50%, and the recall, F1 score are 97.33%, 97.91% respectively. Compared with previous state-of-art algorithms, our algorithm can achieve the highest recognition accuracy. It proves that our fall detection method is effective. [ABSTRACT FROM AUTHOR]

An emergency action plan (EAP) for reservoirs and urban areas downstream of dams can alleviate damage caused by extreme flooding. An EAP is a disaster action plan that can designate evacuation paths for vulnerable districts. Generally, calculation of dam-break discharge in accordance with dam inflow conditions, calculation of maximum water surface elevation as per hydraulic channel routing, and flood map generation using topographical data are prepared for the purposes of creating an EAP. However, rainfall and flood patterns exhibited in the context of climate change can be extremely diverse. In order to prepare an efficient flood response, techniques should be considered that are capable of generating flood maps promptly while taking dam inflow conditions into account. Therefore, this study aims to propose methodology that is capable of generating flood maps rapidly for any dam inflow conditions. The proposed methodology was performed by linking a dynamic numerical analysis model (DAMBRK) with a random forest regression technique. The previous standard method of drawing flood maps often requires a significant amount of time depending on accuracy and personnel availability; however, the technique proposed here is capable of generating a flood map within one minute. Through use of this methodology, the time taken to prepare flood maps in large-scale water-disaster situations can be reduced. Moreover, methodology for estimating flood risk via use of flood mapping has been proposed. This study would provide assistance in establishing disaster countermeasures that take various flood scenarios into account by promptly providing flood inundation information to disaster-related agencies. [ABSTRACT FROM AUTHOR]

For flood risk assessment, it is necessary to quantify the uncertainty of spatiotemporal changes in floods by analyzing space and time simultaneously. This study designed and tested a methodology for the designation of evacuation routes that takes into account spatial and temporal inundation and tested the methodology by applying it to a flood-prone area of Seoul, Korea. For flood prediction, the non-linear auto-regressive with exogenous inputs neural network was utilized, and the geographic information system was utilized to classify evacuations by walking hazard level as well as to designate evacuation routes. The results of this study show that the artificial neural network can be used to shorten the flood prediction process. The results demonstrate that adaptability and safety have to be ensured in a flood by planning the evacuation route in a flexible manner based on the occurrence of, and change in, evacuation possibilities according to walking hazard regions. [ABSTRACT FROM AUTHOR]

To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period and inundation map would be a useful technique for responding to floods in real-time and could provide an expected inundation area by return period. In this study, return period estimation for observed rainfall was performed via PNN (probabilistic neural network). SVR (support vector regression) and a SOM (self-organizing map) were used to predict flood volume and inundation maps. The study area was the Gangnam area, which has experienced extensive urbanization. The database for training SVR and SOM was constructed by one- and two-dimensional flood analysis with consideration of 120 probable rainfall events. The probable rainfall events were composed with 2–100 year return periods and 1–3 hour durations. The SVR technique was used to predict flood volume according to the rainfall return period, and the SOM was used to cluster various expected flood patterns to be used for predicting inundation maps. The prediction results were compared with the simulation results of a two-dimensional flood analysis model. The highest fitness of the predicted flood maps in the study area was calculated at 85.94%. The proposed method was found to constitute a practical methodology that could be helpful in improving urban flood response capabilities. [ABSTRACT FROM AUTHOR]

Data-driven models using an artificial neural network (ANN), deep learning (DL) and numerical models are applied in flood analysis of the urban watershed, which has a complex drainage system. In particular, data-driven models using neural networks can quickly present the results and be used for flood forecasting. However, not a lot of data with actual flood history and heavy rainfalls are available, it is difficult to conduct a preliminary analysis of flood in urban areas. In this study, a deep neural network (DNN) was used to predict the total accumulative overflow, and because of the insufficiency of observed rainfall data, 6 h of rainfall were surveyed nationwide in Korea. Statistical characteristics of each rainfall event were used as input data for the DNN. The target value of the DNN was the total accumulative overflow calculated from Storm Water Management Model (SWMM) simulations, and the methodology of data augmentation was applied to increase the input data. The SWMM is one-dimensional model for rainfall-runoff analysis. The data augmentation allowed enrichment of the training data for DNN. The data augmentation was applied ten times for each input combination, and the practicality of the data augmentation was determined by predicting the total accumulative overflow over the testing data and the observed rainfall. The prediction result of DNN was compared with the simulated result obtained using the SWMM model, and it was confirmed that the predictive performance was improved on applying data augmentation. [ABSTRACT FROM AUTHOR]

This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small–medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed rainfall and water level without extensive physical and topographic data, and can be performed in real-time by integrating point- and section flood forecasting and spatial inundation analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to consider the uncertainties for constructing flood hazard maps for levee failure. The flood depth, velocity, and arrival time were estimated by the 2-Dimensional model and were considered as flood indices for flood hazard mapping. Each flood index predicted from the 2-D flood analysis based on several scenarios was fuzzified to reflect the uncertainties of the indices. The fuzzified flood indices were integrated using the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), resulting in a single graded flood hazard map. This methodology was applied to the Gam river in South Korea and confirmed that the Fuzzy MCDM (Multiple Criteria Decision Making) technique can be used to produce flood hazard maps. The flood hazard map produced in this study compared with the current flood hazard map of MOLIT (Ministry of Land, Infrastructure and Transports). This study found that the proposed methodology was more advantageous than the current methods with regard to the accuracy and grading of the flood areas, as well as in regard to an integrated single map. This report is expected to be expand upon other floods, including dam failure and urban flooding. [ABSTRACT FROM AUTHOR]

Damage caused by flash floods is increasing due to urbanization and climate change, thus it is important to recognize floods in advance. The current physical hydraulic runoff model has been used to predict inundation in urban areas. Even though the physical calculation process is astute and elaborate, it has several shortcomings in regard to real-time flood prediction. The physical model requires various data, such as rainfall, hydrological parameters, and one-/two-dimensional (1D/2D) urban flood simulations. In addition, it is difficult to secure lead time because of the considerable simulation time required. This study presents an immediate solution to these problems by combining hydraulic and probabilistic methods. The accumulative overflows from manholes and an inundation map were predicted within the study area. That is, the method for predicting manhole overflows and an inundation map from rainfall in an urban area is proposed based on results from hydraulic simulations and uncertainty analysis. The Second Verification Algorithm of Nonlinear Auto-Regressive with eXogenous inputs (SVNARX) model is used to learn the relationship between rainfall and overflow, which is calculated from the U.S. Environmental Protection Agency's Storm Water Management Model (SWMM). In addition, a Self-Organizing Feature Map (SOFM) is used to suggest the proper inundation area by clustering inundation maps from a 2D flood simulation model based on manhole overflow from SWMM. The results from two artificial neural networks (SVNARX and SOFM) were estimated in parallel and interpolated to provide prediction in a short period of time. Real-time flood prediction with the hydraulic and probabilistic models suggested in this study improves the accuracy of the predicted flood inundation map and secures lead time. Through the presented method, the goodness of fit of the inundation area reached 80.4% compared with the verified 2D inundation model. [ABSTRACT FROM AUTHOR]

As a newly identified type of programmed cell death, cuproptosis may have an impact on cancer development, including clear cell renal cell carcinoma (ccRCC). Herein, we first noticed that the expression levels of cuproptosis regulators exhibited a tight correlation with the clinicopathological characteristics of ccRCC. The cuproptosis-sensitive sub-type (CSS), classified via consensus clustering analysis, harbored a higher overall survival rate compared to the cuproptosis-resistant sub-type (CRS), which may have resulted from the differential infiltration of immune cells. FDX1, the cuproptosis master regulator, was experimentally determined as a tumor suppressor in ccRCC cells by suppressing the cell growth and cell invasion of ACHN and OSRC-2 cells in a cuproptosis-dependent and -independent manner. The results from IHC staining also demonstrated that FDX1 expression was negatively correlated with ccRCC tumor initiation and progression. Furthermore, we identified the miR-21-5p/FDX1 axis in ccRCC and experimentally verified that miR-21-5p directly binds the 3'-UTR of FDX1 to mediate its degradation. Consequently, a miR-21-5p inhibitor suppressed the cell growth and cell invasion of ACHN and OSRC-2 cells, which could be compensated by FDX1 knockdown, reinforcing the functional linkage between miR-21-5p and FDX1 in ccRCC. Finally, we evaluated the ccRCC tumor microenvironment under the miR-21-5p/FDX1 axis and noted that this axis was strongly associated with the infiltration of immune cells such as CD4 + T cells, Treg cells, and macrophages, suggesting that this signaling axis may alter microenvironmental components to drive ccRCC progression. Overall, this study constructed the miR-21-5p/FDX1 axis in ccRCC and analyzed its potential impact on the tumor microenvironment, providing valuable insights to improve current ccRCC management.

The development of the placental fold, which increases the maternal-fetal interacting surface area, is of primary importance for the growth of the fetus throughout the whole pregnancy. However, the mechanisms involved remain to be fully elucidated. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) are a new class of RNAs with regulatory functions and could be epigenetically regulated by histone modifications. In this study, 141 lncRNAs (including 73 up-regulated and 68 down-regulated lncRNAs) were identified to be differentially expressed in the placentas of pigs during the establishment and expanding stages of placental fold development. The differentially expressed lncRNAs and genes (DElncRNA-DEgene) co-expression network analysis revealed that these differentially expressed lncRNAs (DElncRNAs) were mainly enriched in pathways of cell adhesion, cytoskeleton organization, epithelial cell differentiation and angiogenesis, indicating that the DElncRNAs are related to the major events that occur during placental fold development. In addition, we integrated the RNA-seq (RNA sequencing) data with the ChIP-seq (chromatin immunoprecipitation sequencing) data of H3K4me3/H3K27ac produced from the placental samples of pigs from the two stages (gestational days 50 and 95). The analysis revealed that the changes in H3K4me3 and/or H3K27ac levels were significantly associated with the changes in the expression levels of 37 DElncRNAs. Furthermore, several H3K4me3/H3K27ac-lncRNAs were characterized to be significantly correlated with genes functionally related to placental development. Thus, this study provides new insights into understanding the mechanisms for the placental development of pigs.

Since glucose biosensors are one of the most popular and widely used point-of-care testing devices, a novel electrochemical enzyme-linked immunosorbent assay (ELISA) for protein biomarkers has been developed based on a glucose detection strategy. In this study, α-fetoprotein (AFP) was used as the target protein. An electrochemical ELISA system was constructed using anti-AFP antibodies immobilized on microwell plates as the capture antibody (Ab1) and multi-label bioconjugates as signal tracer. The bioconjugates were synthesized by attaching glucoamylase and the secondary anti-AFP antibodies (Ab2) to gold nanoparticles (AuNPs). After formation of the sandwich complex, the Ab2-glucoamylase-AuNPs conjugates converted starch into glucose in the presence of AFP. The concentration of AFP can be calculated based on the linear relation between AFP and glucose, the concentration of which can be detected by the glucose biosensor. When the AFP concentration ranged from 0.05 to 100 ng/mL, a linear calibration plot (i (µA) = 13.62033 - 2.86252 logCAFP (ng/mL), r = 0.99886) with a detection limit of 0.02 ng/mL was obtained under optimal conditions. The electrochemical ELISA developed in this work shows acceptable stability and reproducibility, and the assay for AFP spiked in human serum also shows good recovery (97.0%-104%). This new method could be applied for detecting any protein biomarker with the corresponding antibodies.

Aptamers have been widely used as recognition elements for biosensor construction, especially in the detection of proteins or small molecule targets, and regarded as promising alternatives for antibodies in bioassay areas. In this review, we present an overview of reported design strategies for the fabrication of biosensors and classify them into four basic modes: target-induced structure switching mode, sandwich or sandwich-like mode, target-induced dissociation/displacement mode and competitive replacement mode. In view of the unprecedented advantages brought about by aptamers and smart design strategies, aptamer-based biosensors are expected to be one of the most promising devices in bioassay related applications.