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Seasonal (temporal) variations can influence the δ13C, δ2H, δ18O, and δ15N values and nutrient composition of organic (ORG), green (GRE), and conventional (CON) vegetables with a short growth cycle. Stable isotope ratio mass spectrometry (IRMS) and near-infrared spectroscopy (NIRS) combined with the partial least squares-discriminant analysis (PLS-DA) method were used to investigate seasonal effects on the identification of ORG, GRE, and CON Brassica chinensis L. samples (BCs). The results showed that δ15N values had significant differences among the three cultivation methods and that δ13C, δ2H, and δ18O values were significantly higher in winter and spring and lower in summer. The NIR spectra were relatively clustered across seasons. Neither IRMS-PLS-DA nor NIRS-PLS-DA could effectively identify all BC cultivation methods due to seasonal effects, while IRMS-NIRS-PLS-DA combined with Norris smoothing and derivative pretreatment had better predictive abilities, with an 89.80% accuracy for ORG and BCs, 88.89% for ORG and GRE BCs, and 75.00% for GRE and CON BCs. The IRMS-NIRS-PLS-DA provided an effective and robust method to identify BC cultivation methods, integrating multi-seasonal differences. [ABSTRACT FROM AUTHOR]

Hydroponic cultivation of fresh produce is gaining popularity worldwide, but few studies have provided a comparative assessment of hydroponic and conventional soil-based vegetables. In this study, we analyzed a series of hazardous chemicals, including 120 pesticides, 18 phthalates (PAEs), and 2 heavy metals (lead and cadmium) in four vegetable commodities (lettuces, celeries, tomatoes, and cucumbers) from hydroponic and conventional soil-based cultivation. Our study showed that at least one pesticide was present in 84% of the conventionally grown samples, whereas only 30% of the hydroponic samples contained detectable pesticide residues. Regarding the total PAE concentrations, there was no significant difference between conventional and hydroponic vegetables. The lead and cadmium residues in conventionally cultivated vegetables were significantly higher than in those produced from hydroponic cultivation. Lead is the primary heavy metal pollutant across all vegetable samples. The hazard index (HI) values of the hydroponic and conventional vegetables were 0.22 and 0.64, respectively. Since both values are below one, the exposure to these hazardous chemicals through consumption of the studied vegetables may not pose a significant health risk. The HI values also suggested that the health risks of eating hydroponic vegetables are lower than for conventional soil-based vegetables. [ABSTRACT FROM AUTHOR]

The occurrence and development of tumors require the metabolic reprogramming of cancer cells, namely the alteration of flux in an autonomous manner via various metabolic pathways to meet increased bioenergetic and biosynthetic demands. Tumor cells consume large quantities of nutrients and produce related metabolites via their metabolism; this leads to the remodeling of the tumor microenvironment (TME) to better support tumor growth. During TME remodeling, the immune cell metabolism and antitumor immune activity are affected. This further leads to the escape of tumor cells from immune surveillance and therefore to abnormal proliferation. This review summarizes the regulatory functions associated with the abnormal biosynthesis and activity of metabolic signaling molecules during the process of tumor metabolic reprogramming. In addition, we provide a comprehensive description of the competition between immune cells and tumor cells for nutrients in the TME, as well as the metabolites required for tumor metabolism, the metabolic signaling pathways involved, and the functionality of the immune cells. Finally, we summarize current research targeted at the development of tumor immunotherapy. We aim to provide new concepts for future investigations of the mechanisms underlying the metabolic reprogramming of tumors and explore the association of these mechanisms with tumor immunity. [ABSTRACT FROM AUTHOR]

Simple Summary: The cashmere industry is a crucial resource and pillar of animal husbandry in the desert and semidesert regions of the central and western Inner Mongolia Autonomous Region. The development of embryonic hair follicles is vital in determining the quality and quantity of cashmere; however, the molecular mechanisms underlying embryonic hair follicle development remain unknown. This study employed proteomic and metabolomic techniques to analyse embryonic skin samples from cashmere goats. The results revealed that the oxytocin signalling pathway is crucial in embryonic hair follicle development by activating the MAPK and Ca2+ signalling pathways. The findings of this study expand our theoretical understanding of embryonic hair follicle development in cashmere goats and serve as a premise for future research. The hair follicle (HF) is the fundamental unit for fleece and cashmere production in cashmere goats and is crucial in determining cashmere yield and quality. The mechanisms regulating HF development in cashmere goats during the embryonic period remain unclear. Growing evidence suggests that HF development involves complex developmental stages and critical events, and identifying the underlying factors can improve our understanding of HF development. In this study, samples were collected from embryonic day 75 (E75) to E125, the major HF developmental stages. The embryonic HFs of cashmere goats were subjected to proteomic and metabolomic analyses, which revealed dynamic changes in the key factors and signalling pathways controlling HF development at the protein and metabolic levels. Gene ontology and the Kyoto Encyclopaedia of Genes and Genomes were used to functionally annotate 1784 significantly differentially expressed proteins and 454 significantly differentially expressed metabolites enriched in different HF developmental stages. A joint analysis revealed that the oxytocin signalling pathway plays a sustained role in embryonic HF development by activating the MAPK and Ca2+ signalling pathways, and a related regulatory network map was constructed. This study provides a global perspective on the mechanism of HF development in cashmere goats and enriches our understanding of embryonic HF development. [ABSTRACT FROM AUTHOR]

Accurate identification of the rational and standardized use of pesticides is important for the sustainable development of agriculture while maintaining a high quality. The insecticides thiamethoxam and fenvalerate and the vegetables spinach, cabbage, and lettuce were used here as study objects. Descriptive analysis and primary reaction kinetic equations were used to analyze the changes in metabolic residues of the two insecticides after different numbers of application in three vegetables. The effects of pesticide residue levels on the δ13C, δ15N, δ2H, and δ18O values of vegetables were analyzed by one-way analysis of variance and correlation analysis. Partial least squares discriminant analysis (PLS-DA) was applied to build discrimination models of the vegetables with different pesticide residues based on stable isotopes. The results showed that the first degradation residues of thiamethoxam and fenvalerate in spinach, cabbage, and lettuce conformed to primary reaction kinetic equations, but the degradation half-lives were long, and accumulation occurred in the second application. The differences in the four stable isotope ratios in the control group of the three vegetables were statistically significant, and two-thirds of the stable isotope ratios in the three vegetables with different numbers of pesticide applications were significantly different. The δ13C and δ15N values of spinach, the δ13C, δ15N, and δ2H values of cabbage, and the δ13C, δ15N, δ2H, and δ18O values of lettuce were significantly correlated with different residues of thiamethoxam and/or fenvalerate applications. The control groups of the three vegetables, spinach-thiamethoxam-first, spinach-thiamethoxam-second, cabbage-thiamethoxam-second, cabbage-fenvalerate-first, and lettuce-thiamethoxam-first, were fully identified by PLS-DA models, while the identification models of other vegetables containing pesticide residues still need to be further improved. The results provide technical support for identifying the rational use of pesticides in vegetables and provide a reference method for guaranteeing the authenticity of green and organic vegetables. [ABSTRACT FROM AUTHOR]

Simple Summary: Cashmere goats are one of the most important animal breeds in northern China. Cashmere is characterised by good quality and high yield and represents an excellent textile material. However, the molecular mechanism underlying the development of cashmere hair follicles is poorly understood, which limits the further development of the cashmere industry. Here, we used whole-transcriptome sequencing to analyse goat embryonic skin samples and carried out experimental verification. We found that fibroblast growth factor 10 and the interacting non-coding RNA miR-184 play important roles in dermal papilla cells of cashmere goat secondary hair follicles. This discovery not only further improves the theoretical understanding of the development of cashmere wool follicles but also has significant implications for functional research on fibroblast growth factor 10 and miR-184. Cashmere, a keratinised product of secondary hair follicles (SHFs) in cashmere goats, holds an important place in international high-end textiles. However, research on the complex molecular and signal regulation during the development and growth of hair follicles (HFs), which is essential for the development of the cashmere industry, is limited. Moreover, increasing evidence indicates that non-coding RNAs (ncRNAs) participate in HF development. Herein, we systematically investigated a competing endogenous RNA (ceRNA) regulatory network mediated by circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in skin samples of cashmere goat embryos, using whole-transcriptome sequencing technology. We obtained 6468, 394, and 239 significantly differentially expressed mRNAs, circRNAs, and miRNAs, respectively. These identified RNAs were further used to construct a ceRNA regulatory network, mediated by circRNAs, for cashmere goats at a late stage of HF development. Among the molecular species identified, miR-184 and fibroblast growth factor (FGF) 10 exhibited competitive targeted interactions. In secondary HF dermal papilla cells (SHF-DPCs), miR-184 promotes proliferation, inhibits apoptosis, and alters the cell cycle via the competitive release of FGF10. This study reports that FGF10 and its interaction with ncRNAs significantly affect SHF-DPCs, providing a reference for research on the biology of HFs in cashmere goats and other mammals. [ABSTRACT FROM AUTHOR]

In order to acquire scientific evidence for the application of thiamethoxam (TMX) in Agaricus bisporus cultivation, residue and dissipation experiments for field trials were performed with the application of TMX in compost and casing soil, respectively. An effective QuEChERS method was established to analyze TMX and its two metabolites, clothianidin (CLO) and thiamethoxam-urea (TMX-urea), in compost, casing soil, and fruiting bodies. The results indicated that the TMX dissipation half-lives (t1/2) at dosages of 10 and 50 mg kg−1 were 19.74 d (day) and 28.87 d in compost and 33.54 d and 42.59 d in casing soil, individually. TMX, CLO, and TMX-urea were observed after TMX application in compost and casing soil. For TMX applied to the casing soil, only TMX residues were detected in fruiting bodies with bioconcentration factors (BCFs) of 0.0003~0.0009. In addition, both the chronic risk quotient (RQ) and acute risk quotient (HQ) values of TMX in fruiting bodies were far less than 1, which means the dietary health risks to humans were acceptable. However, in the TMX application to the compost, these analytes were not detected in the fruiting bodies. This suggested that the application of TMX in compost was safer than in casing soil during A. bisporus cultivation. [ABSTRACT FROM AUTHOR]

Phosphoric acid-activated biochar has been proven to be a promising adsorbent for pollutant removal in an aqueous solution. It is urgent to understand how surface adsorption and intra-particle diffusion synergistically contribute to the adsorption kinetic process of dyes. In this work, we prepared a series of PPC adsorbents (PPCs) from red-pulp pomelo peel under different pyrolysis temperatures (150–350 °C), which have a broad specific surface area range from 3.065 m2/g to 1274.577 m2/g. The active sites on the surface of PPCs have shown specific change laws of decreasing hydroxyl groups and increasing phosphate ester groups occurring as the pyrolysis temperature rises. Both reaction models (PFO and PSO models) and diffusion models (intra-particle diffusion models) have been applied to simulate the adsorption experimental data to verify the hypothesis deduced from the Elovich model. PPC-300 exhibits the highest adsorption capacity of MB (423 mg/g) under given conditions. Due to its large quantities of active sites on the external and internal surfaces (1274.577 m2/g), a fast adsorption equilibrium can be achieved within 60 min (with an initial MB concentration of 100 ppm). PPC-300 and PPC-350 also exhibit an intra-particle-diffusion-controlled adsorption kinetic process with a low initial MB concentration (100 ppm) or at the very beginning and final stage of adsorption with a high initial MB concentration (300 ppm) at 40 °C, considering that the diffusion is likely hindered by adsorbate molecules through internal pore channels at the middle stage of adsorption in these cases. [ABSTRACT FROM AUTHOR]

With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters. [ABSTRACT FROM AUTHOR]

The MODIS Aqua and Terra Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series acquired during nearly two decades (2000 to 2020) covering the area burned by the Camp Fire (California) in 2018 is investigated in this study by using the multifractal detrended fluctuation analysis in relation to the recovery process of vegetation after fire. In 2008, the same area was partially burned by two wildfires, the BTU Lightning Complex Fire and the Humboldt Fire. Our results indicate that all vegetation index time series are featured by six- and twelve-month modulating periodicities, with a larger spectral content at longer periods for two-fire-affected sites. Furthermore, two fires cause an increase of the persistence of the NDVI and EVI time series and an increase of the complexity, suggesting that the recovery process of vegetation dynamics of fire-affected sites is characterized by positive feedback mechanisms, driving the growth-generating phenomena, which become even more effective in those sites affected by two fires. [ABSTRACT FROM AUTHOR]

Preparation of nano-rod aluminosilicate Mazzit (MAZ) zeolite under low-cost and environmental-friendly route is attractive, but still challenging. Herein, we report a green route for synthesizing nano-rod MAZ zeolite (MAZ-N) using low-cost and environmental-friendly choline chloride as template. Various characterizations including powder X-ray diffraction (XRD), scanning electron microscope (SEM), N2 sorption, and thermogravimetry-differential thermal analysis (TG-DTA) show that MAZ-N samples have good crystallinity and uniform porous structures. Furthermore, the crystallization process and impact of synthesis conditions of MAZ-N samples have been investigated in detail. These results suggest the potential applications of MAZ-N zeolites as supporting catalyst compounds in industrial processes. [ABSTRACT FROM AUTHOR]

Nonylphenol (NP) is considered to be an environmentally toxic, endocrine-disrupting chemical that affects humans and ecosystems. Adsorption is one of the most promising approaches for the removal of nonylphenol contamination from water. Herein, in order to design an adsorbent with high adsorption capacity, magnesium silicate with different Mg/Si ratios was successfully synthesized by a sol–gel method at 60 °C. Magnesium silicate with a Mg/Si ratio of 1:6 was found to possess the best adsorption performance, with maximum 4−NP sorption 30.84 mg/g under 25 °C and 0.2 g/L adsorbent dose. The adsorption was negatively affected by increasing adsorbent dose and temperature. The kinetics and isotherm of 4−NP adsorption by Mg/Si were well described by the pseudo−second−order and Sips model, respectively, and behavior was proven to be physisorption−enhanced by a chemical effect. Detailed characterization by XRD, BET, and SEM confirmed that the magnesium silicate possesses an amorphous, mesoporous structure. The study will contribute to the applicability of cheap magnesium silicate for removal of NP contamination in water. [ABSTRACT FROM AUTHOR]

As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation. [ABSTRACT FROM AUTHOR]

Rapid progress on disaster detection and assessment has been achieved with the development of deep-learning techniques and the wide applications of remote sensing images. However, it is still a great challenge to train an accurate and robust disaster detection network due to the class imbalance of existing data sets and the lack of training data. This paper aims at synthesizing disaster remote sensing images with multiple disaster types and different building damage with generative adversarial networks (GANs), making up for the shortcomings of the existing data sets. However, existing models are inefficient in multi-disaster image translation due to the diversity of disaster and inevitably change building-irrelevant regions caused by directly operating on the whole image. Thus, we propose two models: disaster translation GAN can generate disaster images for multiple disaster types using only a single model, which uses an attribute to represent disaster types and a reconstruction process to further ensure the effect of the generator; damaged building generation GAN is a mask-guided image generation model, which can only alter the attribute-specific region while keeping the attribute-irrelevant region unchanged. Qualitative and quantitative experiments demonstrate the validity of the proposed methods. Further experimental results on the damaged building assessment model show the effectiveness of the proposed models and the superiority compared with other data augmentation methods. [ABSTRACT FROM AUTHOR]

Multiple insecticides' residues after the mixed application of several neonicotinoids cause combined pollution and bring new challenges to food safety and pest control during agricultural production. In this study, three neonicotinoid insecticides, namely imidacloprid (IMI), acetamiprid (ACE), and thiamethoxam (TMX), were mixed and evenly sprayed on Brassica chinensis L. in the field. Then, the insecticides' residues were dynamically monitored to determine the differences in their rates of dissipation and final residues after 10 days. The results showed that the dissipation kinetics of neonicotinoids still conformed to the first-order kinetic model for binary or ternary application of neonicotinoid mixtures, with all determination coefficients (R2) being above 0.9 and the dissipation half-life (DT50) being 2.87–6.74 d. For treatment groups with five times the recommended dosages (IMI 300 g·hm−2, ACE 900 g·hm−2, and TMX 600 g·hm−2), mixed insecticides had a slower dissipation rate, and the DT50 values of mixtures were longer than those of single insecticides. Moreover, the final insecticide residues with mixed application were higher than those of single compounds at 10 d after spraying. Thus, mixed applications of neonicotinoids may increase food safety risks as they increase the final insecticide residues in Brassica chinensis L., and care should therefore be taken when considering the combined use of such compounds. [ABSTRACT FROM AUTHOR]

An effective and sensitive method is necessary for the determination of polybrominated diphenyl ethers (PBDEs) pollutants in water. In this study, effervescent-assisted dispersive liquid-liquid microextraction with solidification of the aqueous phase (EA-DLLME-SAP), followed by Gas Chromatography-Tandem Mass Spectrometry (GC-MS-MS) quantitative analysis, was established for the preconcentration and determination of PBDEs in real environmental water samples. 1,1,2,2-Tetrachloroethane was used as the extractant and directly dispersed into the water phase of the aqueous samples with the aid of a large number of carbon dioxide bubbles generated via the acid-base reaction of acetic acid and sodium bicarbonate, which did not require the use of a dispersant during the extraction process. The key factors affecting the extraction recovery were optimized, and an internal standard was used for quantitative analysis, which gave good linearity ranges of 1–100 ng·L−1 (BDEs 28, 47, 99, and 100), 2–200 ng·L−1 (BDEs 153, 154, and 183) and 5–500 ng·L−1 (BDE 209) with limits of quantification in the range of 1.0–5.0 ng·L−1. The accuracy was verified with relative standard deviations < 8.5% observed in tap, lake, river and reservoir water samples with relative recoveries ranging from 67.2 to 102.6%. The presented method contributes to the determination of PBDEs in environmental water samples. [ABSTRACT FROM AUTHOR]

Nanosized zeolites with larger external surface area and decreased diffusion pathway provide many potential opportunities in adsorption, diffusion, and catalytic applications. Herein, we report a designer synthesis of ultra-fine Fe-LTL zeolite nanocrystals under very mild synthesis conditions. We prepared Fe-LTL zeolite nanocrystals synthesized using L precursor. The precursor is aging at room temperature to obtain zeolite L nuclei. In order to investigate more details of Fe-LTL zeolite nanocrystals, various characterizations including X-ray diffraction (XRD), inductively coupled plasma (ICP), diffuse reflectance ultraviolet-visible (UV-Vis) spectroscopy, confirm the tetrahedral Fe3+ species in the zeolite framework. Besides, scanning electron microscope (SEM), Fourier transform infrared spectrometer (FT-IR), dynamic light scattering (DLS) indicate that the average particle size of Fe-LTL zeolite crystals is approximately 30 nm. Thus, ultra-fine Fe-LTL zeolite with large external surface area and shorter diffusion pathway to the active sites might have great potential in the near future. [ABSTRACT FROM AUTHOR]

The analysis of vegetation dynamics affected by wildfires contributes to the understanding of ecological changes under disturbances. The use of the Normalized Difference Vegetation Index (NDVI) of satellite time series can effectively contribute to this investigation. In this paper, we employed the methods of multifractal detrended fluctuation analysis (MFDFA) and Fisher–Shannon (FS) analysis to investigate the NDVI series acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar-Orbiting Partnership (Suomi-NPP). Four study sites that were covered by two different types of vegetation were analyzed, among them two sites were affected by a wildfire (the Camp Fire, 2018). Our findings reveal that the wildfire increases the heterogeneity of the NDVI time series along with their organization structure. Furthermore, the fire-affected and fire-unaffected pixels are quite well separated through the range of the generalized Hurst exponents and the FS information plane. The analysis could provide deeper insights on the temporal dynamics of vegetation that are induced by wildfire. [ABSTRACT FROM AUTHOR]

A variety of environmental analysis applications have been advanced by the use of satellite remote sensing. Smoke detection based on satellite imagery is imperative for wildfire detection and monitoring. However, the commonly used smoke detection methods mainly focus on smoke discrimination from a few specific classes, which reduces their applicability in different regions of various classes. To this end, in this paper, we present a new large-scale satellite imagery smoke detection benchmark based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, namely USTC_SmokeRS, consisting of 6225 satellite images from six classes (i.e., cloud, dust, haze, land, seaside, and smoke) and covering various areas/regions over the world. To build a baseline for smoke detection in satellite imagery, we evaluate several state-of-the-art deep learning-based image classification models. Moreover, we propose a new convolution neural network (CNN) model, SmokeNet, which incorporates spatial and channel-wise attention in CNN to enhance feature representation for scene classification. The experimental results of our method using different proportions (16%, 32%, 48%, and 64%) of training images reveal that our model outperforms other approaches with higher accuracy and Kappa coefficient. Specifically, the proposed SmokeNet model trained with 64% training images achieves the best accuracy of 92.75% and Kappa coefficient of 0.9130. The model trained with 16% training images can also improve the classification accuracy and Kappa coefficient by at least 4.99% and 0.06, respectively, over the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, >10%) and omission error (OE, >6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management. [ABSTRACT FROM AUTHOR]

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data's spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel's diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires. [ABSTRACT FROM AUTHOR]

The marine-derived fungus Talaromyces rugulosus isolated from the Mediterranean sponge Axinella cannabina and cultured on solid rice medium yielded seventeen lactone derivatives including five butenolides ( 1 - 5 ), seven (3 S )-resorcylide derivatives ( 6 - 12 ), two butenolide-resorcylide dimers ( 13 and 14 ), and three dihydroisocoumarins ( 15 - 17 ). Among them, fourteen compounds ( 1 - 3 , 6 - 16 ) are new natural products. The structures of the isolated compounds were elucidated by 1D and 2D NMR (Nuclear Magnetic Resonance) spectroscopy as well as by ESI-HRMS (ElectroSpray Ionization-High Resolution Mass Spectrometry). TDDFT-ECD (Time-Dependent Density Functional Theory-Electronic Circular Dichroism) calculations were performed to determine the absolute configurations of chiral compounds. The butenolide-resorcylide dimers talarodilactones A and B ( 13 and 14 ) exhibited potent cytotoxicity against the L5178Y murine lymphoma cell line with IC 50 values of 3.9 and 1.3 µM, respectively., Competing Interests: The authors declare no conflict of interest.