Transport of prednisolone, cortisone, and triamcinolone acetonide in agricultural soils: Sorption isotherms, transport dynamics, and field-scale simulation.

The occurrence of glucocorticoids (GCs) in agricultural soils has raised concerns due to their high polarity, widespread biological effects in vertebrates, and their potential to disrupt vital processes such as glucose metabolism and immune function. This study investigated the sorption and transport dynamics of three GCs, namely cortisone (COR), prednisolone (PNL), and triamcinolone acetonide (TCA) in five soil-water systems (S1-S5 systems). The sorption data of the GCs were fitted to a linear sorption model (R2 = 0.95-0.99), with organic carbon (OC) normalized sorption coefficients ranging from 2.26 ± 0.02 to 3.36 ± 0.02. The sorption magnitudes (Kd) of the GCs exhibited a nearly linear correlation with their corresponding octanol-water partition coefficients (logKow) in the S1-S3 systems. However, some deviations from linearity were observed in the S4 and S5 systems. Furthermore, a strong correlation was observed between the Kd values of the GCs and the OC% of the soils. These data indicated that specific and hydrophobic partitioning interactions governed the sorption of GCs onto soils. The transport data of the GCs were fitted to a two-site nonequilibrium model using the CXTFIT program (R2 = 0.82-0.98). The retardation factor (R) for each GC exhibited a positive correlation with the OC% and clay contents of soils. Additionally, the relationships between the logR values and logKow values of the GCs deviated slightly from linear correlation in most columns. These results indicated that specific interactions in the columns were more pronounced compared to the batch systems. An initial field-scale simulation demonstrated that frequent precipitation can facilitate the dilution and vertical transport of the GCs through soil profiles. The transport potential of the GCs was affected by the properties and soils and GCs. Overall, these findings provide valuable insights into the transport potential and associated environmental risks of GCs in soil-water systems.

Biotransformation of cyproterone acetate, drospirenone, and megestrol acetate in agricultural soils: Kinetics, microbial community dynamics, transformation products, and mechanisms.

The occurrence of biologically active synthetic progestins in agricultural soils is of growing concern due to their potential to disrupt the endocrine function of aquatic fish in nearby surface waters. This study investigated the biotransformation outcomes of cyproterone acetate (CPA), drospirenone (DRO), and megestrol acetate (MGA) in four agricultural soils. The biotransformation data were fitted to a first-order decay model (R2 = 0.93-0.99), with half-lives and first-order decay coefficients ranging from 76.2-217 h and 9.10 × 10-3-3.20 × 10-3 (h-1), respectively. Abundant biotransformation products (TPs) were generated during incubation, with the number and yields varying across the four soils. 1,2-Dehydrogenation was the main transformation pathway of DRO in the four soils (yields of 32.3-214 %). Similarly, 1,2-dehydrogenation was the most relevant transformation pathway of MGA in the four soils (yields of 21.8-417 %). C3 reduction was the major transformation pathway of CPA in soils B, C, and D (yields of 114-245 %). Hydrogenation (yield of 133 %) and hydroxylation (yield of 21.0 %) were the second major transformation pathway of CPA in soil B and C, respectively. In particular, several TPs exhibited progestogenic and antimineralocorticoid activity, as well as genotoxicity. The high-throughput sequencing indicated that interactions between microorganisms and soil properties may affect biotransformation. Spearman correlation and bidirectional network correlation analysis further revealed that soil properties can directly interfere with the soil sorption capacity for the progestins, thus affecting biotransformation. In particular, soil properties can also limit or promote biotransformation and the formation of TPs (i.e., biotransformation pathways) by affecting the relative abundances of relevant microorganisms. The results of this study indicate that the ecotoxicity of synthetic progestins and related TPs can vary across soils and that the assessment of environmental risks associated with these compounds requires special consideration of both soil properties and microbial communities.

Biotransformation kinetics and pathways of typical synthetic progestins in soil microcosms.

Gestodene (GES), altrenogest (ALT), and medroxyprogesterone acetate (MPA) are three potent synthetic progestins detected in agricultural soils; however, their biotransformation outcomes in soils remain unclear. This study explored the biotransformation of these progestins in five agricultural soils with different physicochemical properties. The biotransformation data were well-described by a first-order decay model (R2 = 0.83-0.99), with estimated half-lives ranging between 12.1 and 188 h. Amplicon sequencing indicated that the presence of progestins changed the bacterial richness and community structure in the soils. Linear correlation, canonical correlation, and two-way correlation network analysis revealed that soil properties can affect biotransformation rates by interfering with progestin-soil interactions or with keystone taxa in soils. The clustermap demonstrated the formation of abundant transformation products (TPs). Isomerization and C4(5) hydrogenation were the major transformation pathways for GES (yields of ∼ 13.7 % and ∼ 10.6 %, respectively). Aromatic dehydrogenation was the major transformation pathway for ALT (yield of ∼ 17.4 %). The C17 hydrolysis with subsequent dehydration and hydrogenation was the major transformation pathway for MPA (yield of ∼ 196 %). In particular, some TPs exhibited progestagenic, androgenic, or estrogenic activity. This study highlights the importance of evaluating the ecotoxicity of progestin and TP mixtures for better understanding their risks in the environment.

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation.

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users' privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor's latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.