Microcystins (MCs) produced by some cyanobacteria can cause toxicity in animals and humans. In recent years, growing evidence suggests that MCs can act as endocrine disruptors. This research systematically investigated effects of microcystin-LR (MC-LR) on endocrine organs, biosynthesis of hormones and positive/negative feedback of the endocrine system in rats. Male, Sprague-Dawley rats were acutely administrated MC-LR by a single intraperitoneal injection at doses of 45, 67.5 or 90 μg MC-LR/kg body mass (bm), and then euthanized 24 h after exposure. In exposed rats, histological damage of hypothalamus, pituitary, adrenal, testis and thyroid were observed. Serum concentrations of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH) and corticosterone (CORT), expressions of genes and proteins for biosynthesis of hormones were lesser, which indicated an overall suppression of the hypothalamus-pituitary-adrenal (HPA) axis. Along the hypothalamus-pituitary-gonadal (HPG) axis, lesser concentrations of gonadotropin-releasing hormone (GnRH) and testosterone (T), but greater concentrations of luteinizing hormone (LH), follicle-stimulating hormone (FSH) and estradiol (E2) were observed. Except for greater transcription of cyp19a1 in testes, transcriptions of genes and proteins for T and E2 biosynthesis along the HPG axis were lesser. As for the hypothalamus-pituitary-thyroid (HPT) axis, after MCs treatment, greater concentrations of thyroid-stimulating hormone (TSH), but lesser concentrations of free tri-iodothyronine (fT3) were observed in serum. Concentrations of free tetra-iodothyronine (fT4) were greater in rats dosed with 45 μg MCs/kg, bm, but lesser in rats dosed with 67.5 or 90 μg MCs/kg, bm. Transcripts of genes for biosynthesis of hormones and receptors along the HPT axis and expressions of proteins for biosynthesis of tetra-iodothyronine (T4) and tri-iodothyronine (T3) in thyroid were significantly altered. Cross-talk among the HPA, HPG and HPT axes probably occurred. It was concluded that MCs caused an imbalance of positive and negative feedback of hormonal regulatory axes, blocked biosynthesis of key hormones and exhibited endocrine-disrupting effects.

The topic of satellite remote sensing of lake ice has gained considerable attention in recent years. Optical satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) allow for the monitoring of lake ice cover (an Essential Climate Variable or ECV), and dates associated with ice phenology (freeze-up, break-up, and ice cover duration) over large areas in an era where ground-based observational networks have nearly vanished in many northern countries. Ice phenology dates as well as dates of maximum and minimum ice cover extent (for lakes that do not form a complete ice cover in winter or do not totally lose their ice cover in summer) are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. Existing knowledge-driven (threshold-based) retrieval algorithms for lake ice cover mapping that use top-of-atmosphere (TOA) reflectance products do not perform well under lower solar illumination conditions (i.e. large solar zenith angles), resulting in low TOA reflectance. This research assessed the capability of four machine learning classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) for mapping lake ice cover, water and cloud cover during both break-up and freeze-up periods using the MODIS/Terra L1B TOA (MOD02) product. The classifiers were trained and validated using samples collected from 17 large lakes across the Northern Hemisphere (Europe and North America); lakes that represent different characteristics with regards to area, latitude, freezing frequency, and ice duration. Following an accuracy assessment using random k-fold cross-validation (k = 100), all machine learning classifiers using a 7-band combination (visible, near-infrared and shortwave-infrared) were found to be able to produce overall classification accuracies above 94%. Both RF and GBT provided overall and class-specific accuracies above 98% and a more visually accurate depiction of lake ice, water and cloud cover. The two tree-based classifiers offered the most robust spatial transferability over the 17 lakes and performed consistently well across ice seasons. However, only RF was relatively insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate the potential of RF for mapping lake ice cover globally from MODIS TOA reflectance data. • This study assessed the capability of ML classifiers for lake ice mapping from MOIDS. • RF and GBT produced the best performance in terms of classification accuracies. • RF and GBT offered the most robust spatial and temporal transferability. • RF was insensitive to the choice of the hyperparameters compared to other classifiers. • The results show the potential of RF for mapping lake ice cover globally from MODIS.

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.