Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns.

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

The Effective Components, Core Targets, and Key Pathways of Ginseng against Alzheimer's Disease.

Background: Panax ginseng C. A. Mey (ginseng) is a traditional Chinese medicinal herb used for the treatment of nervous system disorders, such as Alzheimer's disease (AD). However, the pharmacological mechanisms of ginseng involved in AD have not been systematically investigated. Here, a network pharmacology approach was adopted to explore the effective components, core targets, and key pathways of ginseng against AD. Methods: TCMSP database was used to screen the active ingredients of ginseng. Prediction of the targets of ginseng and AD-related genes was performed using online public databases. "Compound-Target," "Compound-Target-Disease," "Protein-Protein Interaction (PPI)," "Compound-Target-Pathway," and "Compound-Target-GO-Pathway" networks were constructed with Cytoscape 3.7.2 software. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed by using the DAVID database. Results: A total of 22 bioactive compounds were identified from ginseng, and 481 targets of ginseng and 763 AD-related targets were obtained from public databases. The PPI network screened out 19 hub genes of ginseng against AD. According to GO function enrichment, ginseng influenced cell proliferation, death, the nitric oxide biosynthetic process, hypoxia response, and synaptic transmission. Neuroactive ligand-receptor interaction, serotonergic synapse, calcium signaling, cAMP signaling, FoxO signaling, Ras signaling, and PI3K-AKT signaling were among the most key regulatory pathways. The compound-target-GO-route network found EGFR, MAPK1, MAPK14, AKT1, CASP3, and PRKACA as key genes, with PI3K-AKT signaling being the most important pathway for ginseng's anti-AD activity. Conclusion: Ginseng exerts neuroprotective effects in AD patients through multicomponent, multitarget, and multipathway modes, providing novel insight into the pharmacological and experimental research on ginseng against AD.

Tributyl phosphate degradation and phosphorus immobilization by MnO2: Reaction condition optimization and mechanism exploration.

The treatment of tributyl phosphate (TBP) extractant waste from specific industry, eg., nuclear industry, is a great challenge due to its stability and high environmental risk of phosphorus-containing species releasing. Inspired by chemical looping combustion (CLC) technology, a MnO2-assisted thermal oxidation strategy is proposed for TBP degradation and simultaneously P immobilization. Under recommended reaction conditions of 220 °C, 10 g MnO2 mL-1 TBP and 3 h reaction duration, a high P immobilization efficiency of 93.99% is achieved. Material characterization results indicate that P is mainly immobilized in the form of Mn2P2O7, which greatly reduces the environmental risk of P-containing species. TBP degradation intermediates are further identified by thermogravimetric-gas chromatography-mass spectrometry (TG-GC-MS), liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS), which facilitates understanding of reaction mechanisms as well as proposing possible pathways of TBP degradation. It is suggested that MnO2 provides essential oxygen as oxygen carrier for flameless combustion. Meantime, MnO2 reduction leads to the generation of Mn(III) species. The existence of oxygen vacancy in MnO2 also facilitates •O2- radical generation. Under flameless combustion and attacks of Mn(III) and •O2-, TBP is firstly degraded into intermediates and finally mineralized into CO2 and H2O, while P is mainly immobilized as pyrophosphate.

Applications of gray-level variation detection method to intracellular ice formation.

Intracellular ice formation (IIF) is the major cause of death in cells subjected to freezing. The occurrence of intracellular ice prevents the penetration of light into the camera and makes the image dark. Therefore, the gray-level variation can reflect the IIF. However, cell deformation is accompanied with IIF, especially for larger cells. It is necessary to account this entire phenomenon together in a single method. In this paper, the normalized parameter C defined by the gray-level variation depending on the displacement was defined to reflect the gray-level change of each pixel point in the region of interest of the image. The process of IIF of onion epidermal cells and 293T cells was analyzed by this method.