Methodology for Effect-Based Identification of Bioconcentratable Endocrine Disrupting Chemicals (EDCs) in Water: Establishment, Validation, and Application.

Since the wide occurrence of endocrine disrupting chemicals (EDCs) in water is associated with various adverse effects in aquatic organisms, it is urgent to identify key bioconcentratable EDCs. Currently, bioconcentration is generally ignored during the identification of key EDCs. Thus, a methodology for effect-based identification of bioconcentratable EDCs was established in Microcosm, validated in the field, and applied to typical surface water in Taihu Lake. In Microcosm, an inverted U-shaped relationship between logBCFs and logKows was observed for typical EDCs, with medium hydrophobic EDCs (3 ≤ logKow ≤ 7) exhibiting the greatest bioconcentration potentials. On this basis, enrichment methods for bioconcentratable EDCs were established using POM and LDPE, which better fitted the bioconcentration characteristics and enabled the enrichment of 71 ± 8% and 69 ± 6% bioconcentratable compounds. The enrichment methods were validated in the field, where LDPE exhibited a more significant correlation with the bioconcentration characteristics than POM, with mean correlation coefficients of 0.36 and 0.15, respectively, which was selected for further application. By application of the new methodology in Taihu Lake, 7 EDCs were prioritized from 79 identified EDCs as key bioconcentratable EDCs on consideration of their great abundance, bioconcentration potentials, and anti-androgenic potencies. The established methodology could support the evaluation and identification of bioconcentratable contaminants.

A Novel Steganography-Based Pattern for Print Matter Anti-Counterfeiting by Smartphone Cameras.

Print matter authentication based on anti-counterfeiting techniques has received continuously increasing concern from academia and industry. However, the existing printing anti-counterfeiting solutions often have the defects of poor identification experience, high cost, or weak anti-counterfeiting ability, and cannot achieve pre-sale anti-counterfeiting. Therefore, a novel steganography-based pattern for print matter anti-counterfeiting by smartphone cameras is proposed in this study. Firstly, every pixel in the original binary message image (such as QR code) is replaced by a square pixel block with the same binary gray value of 0 or 255 (the first-level expansion). Secondly, the obtained image is encrypted based on the logistic chaotic sequence, and then scrambled by Arnold transform. Lastly, once again every pixel in the generated image is replaced with a square pixel block (the second-level expansion), the size and gray value of which can be set to control the semi-fragile ability to distinguish an originally printed pattern from its illegitimate copy. If the message extracted from the printed pattern through the inverse procedure is complete enough to decode and read, the pattern is assumed to be an original print. Experimental results verify the advancement and effectiveness of the proposed scheme in distinguishing the copied pattern.

Study of dead time estimation method based on pulse interval distribution.

In nuclear radiation detection, some measured radiation counts are lost due to dead time. Estimation of dead time is necessary to restore the counting information in quantitative analysis. This study aims to propose a method for estimating dead time at high counting rate. First of all, a measurement system of pulse interval distribution was used to estimate the true input counting rate of the detection system and implemented in FPGA. Then, a digital pole-zero cancellation technique was introduced in the measurement system to decrease the decay time constant of pulse. This was aimed at mitigating the impact of pile-up on the pulse interval distribution spectrum, especially at high counting rate. The feasibility of this measurement system at high counting rate was verified by the experimental platform of EDXRF. The dead time was calculated using the measured counting rate and the estimated true input counting rate. The accuracy of the dead time relies on the precision of the estimated true count rate. When the counting rate reaches 600 kCPS, the relative error between the theoretical counting rate and the estimated true input counting rate is less than 5%.

The microbiota regulates hematopoietic stem and progenitor cell development by mediating inflammatory signals in the niche.

The commensal microbiota regulates the self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs) in bone marrow. Whether and how the microbiota influences HSPC development during embryogenesis is unclear. Using gnotobiotic zebrafish, we show that the microbiota is necessary for HSPC development and differentiation. Individual bacterial strains differentially affect HSPC formation, independent of their effects on myeloid cells. Early-life dysbiosis in chd8-/- zebrafish impairs HSPC development. Wild-type microbiota promote HSPC development by controlling basal inflammatory cytokine expression in kidney niche, and chd8-/- commensals elicit elevated inflammatory cytokines that reduce HSPCs and enhance myeloid differentiation. We identify an Aeromonas veronii strain with immuno-modulatory activities that fails to induce HSPC development in wild-type fish but selectively inhibits kidney cytokine expression and rebalances HSPC development in chd8-/- zebrafish. Our studies highlight the important roles of a balanced microbiome during early HSPC development that ensure proper establishment of lineal precursor for adult hematopoietic system.

Identification of high-concern organic pollutants in tap waters from the Yangtze River in China based on combined screening strategies.

Recently, numerous organic pollutants have been detected in water environment. The safety of our drinking water has attracted widespread attention. Effective methods to screen and identify high-concern substances are urgently needed. In this study, the combined workflow for the detection and identification of high-concern organic chemicals was established and applied to tap water samples from the Yangtze River Basin. The solid phase extraction (SPE) sorbents were compared and evaluated and finally the HLB cartridge was selected as the best one for most of the contaminants. Based on target, suspect and non-target analysis, 3023 chemicals/peaks were detected. Thirteen substances such as diundecyl phthalate (DUP), 2-hydroxyatrazine, dioxoaminopyrine and diethyl-2-phenylacetamide were detected in drinking water in the Yangtze River Basin for the very first time. Based on three kinds of prioritization principles, 49 ubiquitous, 103 characteristic chemicals and 13 inefficiently removed chemicals were selected as high-concern substances. Among them, 8, 31, 9, 3, 4 substances overlapped with the toxic, risky or high-concern chemicals lists in China, America, European Union, Japan, Korea, respectively. Specific management and removal strategies were further recommended. The workflow is efficient for identification of key pollutants.

Mechanistic in silico modeling of bisphenols to predict estrogen and glucocorticoid disrupting potentials.

Endocrine disrupting chemicals (EDCs) can act as agonists, antagonists or mixed agonists/antagonists toward estrogen receptor α (ERα) and glucocorticoid receptor (GR) in a tissue- and cell-specific manner. However, the activation/inhibition mechanism by which structurally different chemicals induce various types of disruption remain ambiguous. This unrevealed theory limited the in silico modeling of EDCs and the prioritization of potential EDCs for experimental testing. As a kind of chemical widely used in manufacture, bisphenols (BPs) have attracted great attentions on their potential endocrine disrupting effects. BPs used in this study exhibited pure agonistic, pure antagonistic or mixed agonistic/antagonistic activities toward ERα and/or GR. According to the mechanistic modeling, the pure agonistic and pure antagonistic activities were attributed to a single type of protein conformation induced by BPs-ERα and/or BPs-GR interactions, whereas the mixed agonistic/antagonistic activities were attributed to multiple conformations that concomitantly exist. After interacting with BPs, the active conformation recruits coactivator to induce agonistic activity and the blocked conformation inhibits coactivator to induce antagonistic activity, whereas the concomitantly-existing multiple conformations (active, blocked and competing conformations) recruit coactivator, recruit corepressor and/or inhibit coactivator to dually induce the agonistic and antagonistic activities. Therefore, the in silico modeling in this study can not only predict ERα and GR disrupting activities but also, especially, identify the potential mechanisms. This mechanistic study breaks the current bottleneck of computational toxicology and can be widely used to prioritize potential estrogen/glucocorticoid disruptor for experimental testing in both pre-clinic and clinic studies.