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Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution. Plain Language Summary: Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification. Key Points: A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring dataThe proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary locationThe deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is the most challenging neoplasm to treat in clinical practice. Ankyrin repeat domain 49 protein (ANKRD49) is highly expressed in several carcinomas; however, its pattern of expression and role in LUAD are not known. Tissue microarrays, immunohistochemistry, χ2 test, Spearman correlation analysis, Kaplan–Meier, log‐rank test, and Cox's proportional hazard model were used to analyse the clinical cases. The effect of ANKRD49 on the LUAD was investigated using CCK‐8, clonal formation, would healing, transwell assays, and nude mice experiment. Expressions of ANKRD49 and its associated downstream protein molecules were verified by real‐time PCR, Western blot, immunohistochemistry, and/or immunofluorescence analyses. ANKRD49 expression was highly elevated in LUAD. The survival rate and Cox's modelling analysis indicated that there may be an independent prognostic indicator for LUAD patients. We also found that ANKRD49 promoted the invasion and migration in both in in vitro and in vivo assays, through upregulating matrix metalloproteinase (MMP)‐2 and MMP‐9 activities via the P38/ATF‐2 signalling pathway Our findings suggest that ANKRD49 is a latent biomarker for evaluating LUAD prognosis and promotes the metastasis of A549 cells via upregulation of MMP‐2 and MMP‐9 in a P38/ATF‐2 pathway‐dependent manner. [ABSTRACT FROM AUTHOR]

Summary: Camellia oil (CO) and medium chain triglycerides (MCTs), characterised by different carbon chain lengths, were gelled using beeswax (BW), candelilla wax (CLW) and carnauba wax (CRW). Critical concentrations, oil‐binding capacity (OBC), textural parameters, thermal properties, microscopic properties and infrared spectra were used to evaluate their differences. The carbon chain length of oil sources showed different results for the critical concentration of the three wax‐based gel oils at 25 °C, and the oleogel of MCT and CLW exhibited a minimum value (1%). The oleogels formed from CO and waxes with wax esters as the main components (BW and CRW) exhibit significantly lower OBC and textural value than the oleogels made from MCT with BW and CRW. Meanwhile, this is corroborated with the thermal behaviour and crystal morphology, and distribution of the oleogels. However, no differences in crystal types and generation of new functional groups were observed by X‐ray diffraction and Fourier‐transform infrared spectrometer, suggesting that the differences in the properties of wax‐based oleogels with different carbon chain lengths are attributed to the interactions between the waxes and the oil sources (physical interaction). [ABSTRACT FROM AUTHOR]

Stable isotope analysis is a universally recognized and efficient method of indicating trophic relationships that is widely applied in research. However, variation in stable isotope ratios may lead to inaccuracies due to the effects of complex environmental conditions. This research compared the carbon and nitrogen isotopic niches of fish communities between diverse biotopes around the Yellow River estuary and adjacent sea areas, with the aim of revealing distinctions in stable isotopic niche metrics, trophic positions, and feeding preferences. Our analysis of the food source contribution indicated that allochthonous sources were considered major energy sources in estuarine areas directly affected by Yellow River‐diluted water, while autochthonous benthic and pelagic producers dominated carbon input into the food web in Laizhou Bay and the open water. A significant variation in the fish δ15N characteristic was found within estuarine adjacent regions, so, together with the results from previous studies, we deemed the local high concentration of dissolved inorganic nitrogen as the original trigger of the abnormal δ15N characteristic in fishes via a transport process along food chains. These results provide a new perspective on the natural distinction of carbon and nitrogen isotopic niches. The detailed data reported here enhance our understanding of variations in fish communities in estuarine ecosystems. [ABSTRACT FROM AUTHOR]

The production of organogels from vegetable oils requires low‐molecular‐weight gelators. Herein, a novel small‐molecule sugar ester gelator is synthesized from erythritol and octanoic acid. The purified reaction product is identified as erythritol 1,6‐dioctanoate by high performance liquid chromatography (HPLC) with evaporative light‐scattering detection and nuclear magnetic resonance (NMR). This low‐molecular‐weight erythritol ester exhibits self‐assembly behavior in vegetable oils with a minimum gelation concentration of 4 wt%. Microscopy reveals needle‐like crystals that became slender as the gelator concentration increases. X‐Ray Diffraction (XRD) analyses indicate that the organogels are semicrystalline with both crystalline and amorphous domains. Fourier Transform Infrared (FTIR) spectroscopy reveals that the gel structure is maintained by non‐hydrogen‐bonding interactions. Furthermore, rheological tests show that the mechanical strength of the gel is poor, but as the gelator concentration is increased, the mechanical strength and hardness of the gel also increases. Thus, this erythritol ester effectively promotes the gelation of edible vegetable oils and increases the variety of available organogelators. Practical Applications: The synthesized erythritol ester is successfully applied as a gelator to produce organogels from a variety of vegetable oils, and are expected to advance the development of effective organogelators for edible oils. [ABSTRACT FROM AUTHOR]

Summary: Oleogels of β‐sitosterol (Sit) and beeswax (BW) were combined at varying ratios (w/w) and added to sunflower oil (SFO) at concentrations of 10 g per 100 g and 20 g per 100 g oil to prepare oleogels (Sit/BW/SFO). Structural and thermal properties were characterised and results showed that the hardness and enthalpy of oleogels were affected by the amount of β‐sitosterol and beeswax in the oleogelator combination (Sit/BW, w/w). Oleogels with beeswax as the only oleogelator (Sit0/BW10) had the highest hardness and maximum enthalpy change. Gel network form was influenced by the crystalline behaviour of the oleogelator, and Sit0/BW10‐oleogel was densely packed, spherical and white while Sit10/BW0‐oleogel displayed a needle shape. X‐ray diffraction patterns showed that the oleogel width of the crystals and D‐spacing increased with increasing amounts of β‐sitosterol and the FTIR spectra revealed that oleogels formed via non‐covalent bonding and may be stabilised with physical entanglements. [ABSTRACT FROM AUTHOR]

BACKGROUND In order to further develop and utilise Gynostemma pentaphyllum (Thunb.) Makino seeds, a detailed analysis of the characteristics of G. pentaphyllum seed oil ( GPSO), including its physico-chemical parameters, fatty acid composition and unsaponifiable matter constituents, has been investigated in this study. The antioxidant potential of GPSO was evaluated by radical-scavenging activity and ferric-reducing antioxidant power assay in vitro, and the antioxidant activity in vivo was examined by using an aged mice model. RESULTS The main components of the seeds are lipids (485.54 g kg−1) and proteins (203.26 g kg−1). The GPSO obtained by supercritical CO2 fluid extraction was rich in polyunsaturated fatty acids (92.85%), especially conjugated linolenic acid (88.17%); and various useful compounds (squalene, tocopherol and phytosterols) were identified in the unsaponifiable matter. The overall antioxidant capacity of GPSO in vitro was shown to be comparable to that of Camellia seed oil as a positive control. GPSO could provide protection to the aged mice against oxidative stress and minimised the impact of ageing. CONCLUSION All the results suggest that GPSO has direct and potent antioxidant activities; it could be utilised as a functional food to supplement or replace some conventional oils. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

ABSTRACT BACKGROUND Crude camellia seed oil is rich in free fatty acids, which must be removed to produce an oil of acceptable quality. In the present study, we reduced the free fatty acid content of crude camellia seed oil by lipophilization of epicatechin with these free fatty acids in the presence of Candida antarctica lipase B (Novozym 435), and this may enhance the oxidative stability of the oil at the same time. RESULTS The acid value of crude camellia seed oil reduced from 3.7 to 2.5 mgKOH g−1 after lipophilization. Gas chomatography-mass spectrometry analysis revealed that epicatechin oleate and epicatechin palmitate were synthesized in the lipophilized oil. The peroxide, p-anisidine, and total oxidation values during heating of the lipophilized oil were much lower than that of the crude oil and commercially available camellia seed oil, suggesting that lipophilized epicatechin derivatives could help enhance the oxidative stability of edible oil. CONCLUSION The enzymatic process to lipophilize epicatechin with the free fatty acids in crude camellia seed oil described in the present study could decrease the acid value to meet the quality standards for commercial camellia seed oil and, at the same time, obtain a new edible camellia seed oil product with good oxidative stability. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

An eggshell Mo2C catalyst which is designed from the rapid combination of molybdate with melamine is described. In contrast to Pd-based catalysts, the eggshell Mo2C operates effectively with a wide-concentration window in high-temperature gas phase hydrogenation of phenylacetylene, thus, an economical and energy-efficient front-end purification of styrene monomers might be possible. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2522-2531, 2015 [ABSTRACT FROM AUTHOR]

Highlights: [•] Let-7c inhibits lung adenocarcinoma cell proliferation in vitro and in vivo. [•] Let-7c is a tumor suppressor miRNA to induce A549 cell apoptosis. [•] The tumor suppressive role of let-7c is relevant to regulating cell cycle. [•] Oncogene TRIB2 is a new target of let-7c. [•] Let-7c can regulate TRIB2-related C/EBP-α and phosphorylated p38MAPK. [ABSTRACT FROM AUTHOR]