Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations.

Proactive management of pumping stations using artificial intelligence (AI) technology is vital for effectively mitigating the impacts of flood events caused by climate change. Accurate water level forecasts are pivotal in advancing the intelligent operation of pumping stations. This study proposed a novel Transformer-LSTM model to offer accurate multi-step-ahead forecasts of the flood storage pond (FSP) and river water levels for the Zhongshan pumping station in Taipei, Taiwan. A total of 19,647 ten-minute-based datasets of pumping operation and storm sewer, FSP, and river water levels were collected between 2014 and 2020 and further divided into training (70 %), validation (10 %), and test (20 %) datasets for model construction. The results demonstrate that the proposed model dramatically outperforms benchmark models by producing more accurate and reliable water level forecasts at 10-minute (T + 1) to 60-minute (T + 6) horizons. The proposed model effectively enhances the connections between input factors through the Transformer module and increases the connectivity across consecutive time series using the LSTM module. This study reveals interconnected dynamics among pumping operation and storm sewer, FSP, and river water levels, enhancing flood management. Understanding these dynamics is crucial for effective execution of management strategies and infrastructure revitalization against climate impacts. The Transformer-LSTM model's forecasts encourage water practices, resilience, and disaster risk reduction for extreme weather events.

Sodium sulfate addition increases the bioresource of biologically active sulfated polysaccharides from Antrodia cinnamomea.

Fungal sulfated polysaccharides (SPS) have been used in the pharmaceutical industry. In this study, sodium sulfate was employed as an elicitor to induce stress on the mycelia of Antrodia cinnamomea for the biosynthesis of SPS with high sulfate content. Sodium sulfate treatments increased the yield of SPS to 4.46 % and increased the sulfate content to 6.8 mmol/g of SPS. SPS were extracted from A. cinnamomea cultured with 500 mM sodium sulfate; these SPSs are denoted as Na500. Na500 exhibited the highest sulfate content and dose-dependent inhibitory activity against LPS-induced production of macrophage interleukin 6 (IL-6), tumor necrosis factor α (TNF-α), and interleukin 1ß (IL-1ß). Mechanistically, Na500 hindered the phosphorylation of transforming growth factor-ß receptor II (TGFRII), extracellular signal-regulated kinases (ERK), and protein kinase B (AKT) expression. A purified 7.79 kDa galactoglucan, Na500 F3, augmented the anti-inflammation activity by inhibiting LPS-induced TGFß release. Additionally, Na500 F3 restrained the LPS-induced phosphorylation of p-38, ERK, AKT, and TGFRII in RAW264.7 cells. Na500 F3 impeded the proliferation of lung cancer H1975 cells by inhibiting the phosphorylation of focal adhesion kinase, ERK, and Slug. The anti-inflammation and anticancer properties of Antrodia SPS contribute to its health benefits, suggesting its utility in functional foods.

Develop a hybrid machine learning model for promoting microbe biomass production.

Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid machine learning approach (i.e., ANFIS-NM) to identify the potent factors and optimize the cultivation conditions of A. cinnamomea based on a 32 fractional factorial design with seven factors. The results indicate that the ANFIS-NM approach successfully identified three key factors (i.e., glucose, potato dextrose broth, and agar) and significantly boosted mycelia yield. The interpretability of ANFIS rules made the cultivation conditions visually interpretable. Subsequently, a three-factor five-level central composite design was used to probe the optimal yield. This study demonstrates the proposed hybrid machine learning approach could significantly reduce the time consumption in laboratory cultivation and increase mycelia yield that meets SDGs 7 and 12, hitting a new milestone for biomass production.

Production, characterization, and functions of sulfated polysaccharides from zinc sulfate enriched cultivation of Antrodia cinnamomea.

This research utilized zinc sulfate enriched cultural conditions to produce sulfated polysaccharides from Antrodia cinnamomea (denoted as ZnFSPS) and physiochemically characterize functional and mechanical investigations of ZnFSPS. The maximum SPS yield reached a value of 6.68% when A. cinnamomea was fed zinc sulfate with 250 mM (denoted as Zn250). Zn250 had a maximal inhibitory effect on LPS-induced tumor necrosis factor (TNF-α) release in RAW264.7 macrophage. Zn250 contained the highest area percentage of molecular weight of 178.5, 105.1, and 1.56 kDa at values of 19.08, 15.09, and 5.04. Zn250 contained three times the sulfate content as compared with the control. Mechanism studies revealed a novel finding that Zn250 inhibited the LPS-induced RAW264.7 macrophage inflammation and selectively blocked pAKT, pERK and p38. Zn250 also attenuated the LPS-induced IkB-α degradation. In addition, ZnFSPS interfered with lung cancer cell H1975 TGFRI/FAK/Slug signaling. These results suggest ZnFSPS plays roles in regulating inflammatory and anti-lung cancer activity.

Physiochemical changes and mechanisms of anti-inflammation effect of sulfated polysaccharides from ammonium sulfate feeding of Antrodia cinnamomea.

Antrodia cinnamomea is an important medicinal fungus in Taiwan. This study demonstrates changes of complex sulfated polysaccharides (SPS) by fungus A. cinnamomea after ammonium sulfate-feeding and evaluates its anti-inflammatory activities. The addition of 1 mM ammonium sulfate showed maximal sulfate content of SPS in value of 1.82 mmol/g. Ammonium sulfate changes the physiochemical properties of SPS in that area percentage of SPSs (361 kDa) was increased for 1 mM ammonium sulfate to the value of 26 percentage area. SPS of 1 mM ammonium sulfate-fed A. cinnamomea (AM-SPS) had maximal inhibition of LPS-induced tumor necrosis factor (TNF-α) release in RAW264.7 macrophage. Iκ-B degradation induced by LPS in macrophages was reversed by AM-SPS. Suppression of NF-κB activation might have been responsible for the anti-inflammatory effects. Meanwhile, the inhibition was also due to suppressing the AKT, and ERK signaling pathway. Our finding suggests that ammonium sulfate is a useful nutrient for production of SPS for neutraceutical and pharmaceutical applications.

Effects of biocides on gene expression in the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough.

Although sulfate-reducing bacteria (SRB), such as Desulfovibrio vulgaris Hildenborough (DvH) are often eradicated in oil and gas operations with biocides, such as glutaraldehyde (Glut), tetrakis (hydroxymethyl) phosphonium sulfate (THPS), and benzalkonium chloride (BAC), their response to these agents is not well known. Whole genome microarrays of D. vulgaris treated with biocides well below the minimum inhibitory concentration showed that 256, 96, and 198 genes were responsive to Glut, THPS, and BAC, respectively, and that these three commonly used biocides affect the physiology of the cell quite differently. Glut induces expression of genes required to degrade or refold proteins inactivated by either chemical modification or heat shock, whereas BAC appears to target ribosomal structure. THPS appears to primarily affect energy metabolism of SRB. Mutants constructed for genes strongly up-regulated by Glut, were killed by Glut to a similar degree as the wild type. Hence, it is difficult to achieve increased sensitivity to this biocide by single gene mutations, because Glut affects so many targets. Our results increase understanding of the biocide's mode of action, allowing a more intelligent combination of mechanistically different agents. This can reduce stress on budgets for chemicals and on the environment.