Thermoluminescence (TL) analysis for otoliths of the wild carps (cyprinoid) from Baiyangdian Lake and Miyun Reservoir: Some implications for monitoring water environment

Otolith is a typical biomineral carrier growing on insides of fish skull with prominent zoning structure formed by alternating layers of protein and calcium carbonate growing around the nucleus. Even though thermoluminescence (TL) analysis on biomineral has been widely used to measure the radiation exposure in the recent twenty years, the TL characteristics of the fish otolith have not yet been reported in literature. TL characteristics of otoliths from the wild carps (cyprinoid) living in the Baiyangdian Lake, Hebei Province and Miyun Reservoir, Beijing City was first studied, and the differences of energy gap (E) between the fish otoliths in the two waters have also been discussed in this paper. The experimental results indicated that TL curve parameters: peak temperature (Tp), luminous intensity (I), integrated intensity (S) and middle width (Wm) for the glow curves of the cyprinoid otoliths from Baiyangdian Lake are greater than those from Miyun reservoir, and the stability of the formers’ TL curve parameters value and energy gap (E) was weaker than the latter. In comparison to the Miyun Reservoir, the analysis manifested that the electrons and vacancies trapped in the otoliths from Baiyangdian Lake are more likely to escape. According to the investigation, the contaminative degree and eutrophication in the water of Baiyangdian Lake was heavier than that of Miyun Reservoir. Therefore, the characteristics of TL growth curves of the cyprinoid otoliths is quite sensitive to heavier contaminated and less contaminated water, and this could be regarded as an important typomorphic biomineral for monitoring the contaminative degree and environment change of the water.Keywords: Cyprinoid otoliths, thermoluminescence, water environment, typomorphic minera

Explore the potential molecular mechanism of polycystic ovarian syndrome by protein–protein interaction network analysis

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders prevailing in reproductive age women, present in 3–15% population of women worldwide. Although there are many studies on PCOS, its underlying mechanism remains to be determined. The present study was to construct protein–protein interaction networks based on the potential disease-causing genes for PCOS and characterize the underlying molecular mechanisms of PCOS using the networks. PCOS-associated genes were extracted from DisGeNet and the protein–protein interaction networks (PPIN) of PCOS were constructed using the String Database. Then we utilized MCODE algorithm to analyse the hub-gene modules from the PPIN. Finally, the major biological functions and signaling pathways involved in the hub modules were explored by functional enrichment analysis. A total of 522 candidate genes associated to PCOS were extracted from DisGeNET database. The PPIN constructed using the genes we have collected above included 488 genes and 2767 interaction relationships. Moreover, seven major gene modules were obtained after analyzing the PPIN with the use of MCODE plug-in. The major modules generated were enriched in certain biological functions such as cancer and cell proliferation and apoptosis, regulation of lipid and glucose metabolism, cell cycle and so on. The integrated analysis performed in the current study revealed that these hub modules and their related genes are closely associated to the pathogenesis of PCOS, which may probably provide novel insights for the treatment of PCOS and the study of its latent pathogenic mechanism. The relationship between several of the key genes including ALB, TOP2A, PTGER3, NPB and BRD2 in the modules and PCOS has not been investigated previously and it remains to be verified by further research of large sample, multi-center and multi-ethnic

<i>In situ</i> study of RSK2 kinase activity in a single living cell by combining single molecule spectroscopy with activity-based probes

FCS with the ABP strategy is a very promising method for studying endogenous protein kinases in living cells.</p

The influence of half-cycle rectified sinusoidal alternating current (AC) on corrosion of X80 pipeline steel in an acid bicarbonate solution

Purpose This paper aims to investigate the effect of alternating current (AC) on corrosion, it is not yet clear about the exact mechanism of the corrosion induced by AC. Previous reports indicated that AC corrosion was similar to the effect of continuous cathodic and anodic polarization on the corrosion process of the metals. Wan et al. studied the effect of negative half-wave AC on stress corrosion cracking behavior and mechanism of X80 pipeline steel in near-neutral solution. Design/methodology/approach This study attempted to understand the AC-induced corrosion by imposing the half-cycle AC on the X80 pipeline steel in an acid bicarbonate solution. The AC corrosion mechanism was determined by weight loss and potentiodynamic polarization curve measurements, as well as surface characterization. Findings The results show that the positive half-cycle AC accelerated the uniform corrosion in the NaHCO3 solution, the negative half-cycle AC would decrease the uniform corrosion and local corrosion was increased and some Ca and Mg deposited on the surface of X80 steel, so the corrosion rate decreased by negative half-cycle AC. The corrosion product was composed of α-FeOOH under the application of positive half-cycle AC. The oxygen reduction led to a local increase of pH near the electrode surface and led to the formation of α-FeOOH, which enhanced the protectability of corrosion products. Originality/value Researchers studied the effect of negative half-wave AC on stress corrosion cracking behavior and mechanism of X80 pipeline steel in near-neutral solution. However, the AC behavior and corrosion mechanism in acid solution are unknown. So to make clear about the corrosion behavior of metals in different polarization states and the mechanism involved, diode technology was used to research the AC corrosion, half-wave AC was applied on the metals after the full-wave rectified. </jats:sec

Field corrosion characterization of soil corrosion of X70 pipeline steel in a red clay soil

AbstractThe corrosion behavior of X70 pipeline steel buried in red soil environment has been studied. The surface morphology and elemental distribution were determined by scanning electron microscopy (SEM),energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). The corrosion kinetics was evaluated by weight loss measurement. The results show that in red soil, the corrosion rate of X70 steel decreases with time, and follows the exponential decay law. General corrosion with non-uniform and localized pitting occurred on the steel surface. α-FeOOH was the dominate products during corrosion in whole buried periods, and the corrosion products exhibited well protective properties. The potentiodynamic polarization tests revealed that icorr decreased with time, indicating the improvement of corrosion resistance. The results of Electrochemical impendence spectroscopy (EIS) are consistent with potentiodynamic polarization tests

Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis

Male infertility affects 10–15% of couples globally, with azoospermia — complete absence of sperm — accounting for 15% of cases. Traditional diagnostic methods for azoospermia are subjective and variable. This study presents a novel, noninvasive, and accurate diagnostic method using surface-enhanced Raman spectroscopy (SERS) combined with machine learning to analyze seminal plasma exosomes. Semen samples from healthy controls ([Formula: see text]) and azoospermic patients ([Formula: see text]) were collected, and their exosomal SERS spectra were obtained. Machine learning algorithms were employed to distinguish between the SERS profiles of healthy and azoospermic samples, achieving an impressive sensitivity of 99.61% and a specificity of 99.58%, thereby highlighting significant spectral differences. This integrated SERS and machine learning approach offers a sensitive, label-free, and objective diagnostic tool for early detection and monitoring of azoospermia, potentially enhancing clinical outcomes and patient management