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Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.

This paper explores the impacts of health pandemics on foreign direct investment (FDI) using the new world pandemic uncertainty index (WPUI). We investigate the effects of pandemics, including COVID-19, on FDI based on a sample of 142 economies and sub-samples (incomes and regions) from 1996 to 2019. The two-step system Generalised Method of Moments estimation of linear dynamic panel-data model (DPDGMM) is used in this study. The estimation results are robust with the results of the two-step sequential (two-stage) estimation of linear panel-data models (SELPDM) and the two-step system Generalised Method of Moments estimation (BBGMM). The results show that health pandemics have negative impacts on FDI. Significantly, the uncertainty caused by pandemics creates adverse shocks on FDI net inflows in Asia-Pacific countries and emerging economies.

Triple-negative breast cancer (TNBC) is unresponsive to typical hormonal treatments, causing it to be one of the deadliest forms of breast cancer. Investigating alternative therapies to increase survival rates for this disease is essential. The goal of this study was to assess cytotoxicity and apoptosis mechanisms of prenylated stilbenoids in TNBC cells. The prenylated stilbenoids arachidin-1 (A-1) and arachidin-3 (A-3) are analogs of resveratrol (RES) produced in peanut upon biotic stress. The anticancer activity of A-1 and A-3 isolated from peanut hairy root cultures was determined in TNBC cell lines MDA-MB-231 and MDA-MB-436. After 24 h of treatment, A-1 exhibited higher cytotoxicity than A-3 and RES with approximately 11-fold and six-fold lower IC50, respectively, in MDA-MB-231 cells, and nine-fold and eight-fold lower IC50, respectively, in MDA-MB-436 cells. A-1 did not show significant cytotoxicity in the non-cancerous cell line MCF-10A. While A-1 blocked cell division in G2-M phases in the TNBC cells, it did not affect cell division in MCF-10A cells. Furthermore, A-1 induced caspase-dependent apoptosis through the intrinsic pathway by activating caspase-9 and PARP cleavage, and inhibiting survivin. In conclusion, A-1 merits further research as a potential lead molecule for the treatment of TNBC.

Celastrus hindsii is a potential source of flavonoids with biological activities. This study aimed to develop an ultrasound-assisted technique for extracting flavonoids from leaves of C. hindsii. Response surface methodology was employed to optimize the extraction conditions for maximizing the total flavonoid content (TFC). A maximum TFC of 23.6 mg QE/g was obtained under the extraction conditions of ultrasonic power of 130 W, extraction temperature of 40°C, extraction time of 29 min, and ethanol concentration of 65%. The flavonoid-rich extracts were then studied for their antioxidant and anticancer activities. The results showed that the C. hindsii leaf extract exhibited potent radical scavenging activities against DPPH (IC50 of 164.85 μg/mL) and ABTS (IC50 of 89.05 μg/mL). The extract also significantly inhibited the growth of 3 cancer cell lines MCF7, A549, and HeLa with the IC50 values of 88.1 μg/mL, 120.4 μg/mL, and 118.4 μg/mL, respectively. Notably, the extract had no cytotoxicity effect on HK2 normal kidney cell line. This study suggests that flavonoid-rich extract is a promising antioxidant and anticancer agent and that ultrasound-assisted extraction is an efficient method for extracting flavonoids from C. hindsii leaves.

We estimate and compare the impact of the coronavirus pandemic (COVID-19) on the performance of Artificial Intelligence (AI) and conventional listed firms using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the news of COVID-19 spread and the pandemic entered the first, second, third, and fourth months (24 February 2020, 23 March 2020, 20 April 2020, and 18 May 2020, respectively). The results show that the negative impact of COVID-19 on the AI stock market was less severe than on the conventional stock market in the first month of the pandemic. The performance of the AI stock market recovered quicker than the conventional stock market when the pandemic went into its third month. The results suggest that the AI stocks were more resilient than conventional stocks when the financial market was exposed to uncertainty caused by the COVID-19 pandemic. The deployment of AI in firms serves as a resilient, crucial driver for sustainable performance in challenging environments. Observing the performance of AI-adopted firms is an interesting direction for technical and fundamental analysts. Investors and portfolio managers should consider an AI market index to minimize risk or invest in stocks of AI-adopted listed firms to maximize excess returns.

Triple-negative breast cancer (TNBC) is one of the deadliest forms of breast cancer. Investigating alternative therapies to increase survival rates for this disease is essential. To this end, the cytotoxic effects of the prenylated stilbenoids arachidin-1 (A-1) and arachidin-3 (A-3), and non-prenylated resveratrol (RES) were evaluated in human TNBC cell lines as potential adjuvants for paclitaxel (Pac). A-1, alone or in combination with Pac, showed the highest cytotoxicity in TNBC cells. Apoptosis was further evaluated by measuring key apoptosis marker proteins, cell cycle arrest, and intracellular reactive oxygen species (ROS) generation. Furthermore, the cytotoxic effect of A-1 combined with Pac was also evaluated in a 3D spheroid TNBC model. The results showed that A-1 decreased the Pac IC50 approximately 2-fold in TNBC cells. The synergistic combination of A-1 and Pac arrested cells in G2/M phase and activated p53 expression. In addition, the combined treatment increased intracellular ROS generation and induced apoptosis. Importantly, the combination of A-1 with Pac inhibited TNBC spheroid growth. Our results demonstrated that A-1 in combination with Pac inhibited cell proliferation, induced apoptosis through mitochondrial oxidative stress, and reduced TNBC spheroid growth. These findings underscore the impactful effects of the prenylated stilbenoid A-1 as a novel adjuvant for Pac chemotherapy in TNBC treatment.

Countries are confronting climate change using climate-related regulations that require firms and investors to disclose their green strategies and activities. Using the Meta-Analysis Structural Equation Modeling (MASEM) technique, this study evaluates the relationship between climate-related regulations and financial markets. The meta-regression analysis is conducted based on the outcomes of 52 empirical studies screened from 143 relevant articles. The results show the predictive power of the climate-related disclosure (CRD) laws and environmental regulations (ERs) on financial performance across all studies. ERs create mixed impacts on the equity market and support the debt market. Firm value is affected by ERs either negatively or positively. Methodologies and risk-related factors (market, industry, and firm risks) are important in explaining the relationships between ER/CRD and financial performance. The more developed the market, the less the impact of ERs and CRD on the equity market. Considering industry risk is recommended because different industries are exposed to changes in policies differently. The ER/CRD–firm value relationship is affected by all market, industry, and firm risks. The downside effect of mandatory CRD on the equity market suggests that policy makers, firms, and investors should be cautious in passing a new CRD regulation for transformation towards a sustainable economy.

Spores of the bacterium ( ) have been shown to carry a number of properties potentially beneficial for vaccination. Firstly, as vehicles enabling mucosal delivery of heterologous antigens and secondly, as stimulators of innate immunity. Here, we have examined the specificity of protection conferred by the spore-induced innate response, focusing on influenza H1N1, respiratory syncytial virus (RSV), and coronavirus-2 (SARS-CoV-2) infections. viral challenge murine models were used to assess the prophylactic anti-viral effects of spores delivered by intranasal instilling, using an optimised three-dose regimen. Multiple nasal boosting doses following intramuscular priming with SARS-CoV-2 spike protein was also tested for the capability of spores on enhancing the efficacy of parenteral vaccination. To determine the impact of spores on immune cell trafficking to lungs, we used intravascular staining to characterise cellular participants in spore-dosed pulmonary compartments (airway and lung parenchyma) before and after viral challenge. We found that mice pre-treated with spores developed resistance to all three pathogens and, in each case, exhibited a significant improvement in both survival rate and disease severity. Intranasal spore dosing expanded alveolar macrophages and induced recruitment of leukocyte populations, providing a cellular mechanism for the protection. Most importantly, virus-induced inflammatory leukocyte infiltration was attenuated in spore-treated lungs, which may alleviate the associated collateral tissue damage that leads to the development of severe conditions. Remarkably, spores were able to promote the induction of tissue-resident memory T cells, and, when administered following an intramuscular prime with SARS-CoV-2 spike protein, increased the levels of anti-spike IgA and IgG in the lung and serum. Taken together, our results show that spores are able to regulate both innate and adaptive immunity, providing heterologous protection against a variety of important respiratory viruses of high global disease burden.

Background: Influenza is a respiratory infection that continues to present a major threat to human health, with ~500,000 deaths/year. Continued circulation of epidemic subtypes in humans and animals potentially increases the risk of future pandemics. Vaccination has failed to halt the evolution of this virus and next-generation prophylactic approaches are under development. Naked, “heat inactivated”, or inert bacterial spores have been shown to protect against influenza in murine models. Methods: Ferrets were administered intranasal doses of inert bacterial spores (DSM 32444K) every 7 days for 4 weeks. Seven days after the last dose, the animals were challenged with avian H7N9 influenza A virus. Clinical signs of infection and viral shedding were monitored. Results: Clinical symptoms of infection were significantly reduced in animals dosed with DSM 32444K. The temporal kinetics of viral shedding was reduced but not prevented. Conclusion: Taken together, nasal dosing using heat-stable spores could provide a useful approach for influenza prophylaxis in both humans and animals.

Introduction: Acinetobacter baumannii (A. baumannii) is a major cause of hospital-acquired infections and frequently harbors antibiotic-resistance genes that complicate treatment. Rapid identification and resistance gene detection are essential for effective antibiotic use and infection control. This study developed a multiplex polymerase chain reaction (PCR) assay to identify A. baumannii and detect key β-lactam resistance genes for clinical isolates. Methodology: The assay targeted the recA gene and the 16S-23S ribosomal RNA internal transcribed spacer region for A. baumannii identification. In addition, five β-lactamase genes (blaOXA-51-like, ampC, blaCTX-M, blaTEM, and blaSHV) were targeted. Antimicrobial susceptibility testing and phenotypic extended-spectrum beta-lactamase detection were performed to confirm resistance profiles. Results: Of the 49 Acinetobacter isolates, 46 were identified as A. baumannii by multiplex PCR. All 46 isolates contained ampC, and 45 harbored blaOXA-51-like. blaTEM was detected in 34 isolates, whereas blaCTX-M and blaSHV were absent. Phenotypic tests showed general agreement with the PCR results. High resistance rates were observed for multiple antibiotic classes, including carbapenems, cephalosporins, and aminoglycosides. Conclusions: The multiplex PCR assay developed here provides a rapid and reliable method for A. baumannii identification and resistance gene detection, outperforming conventional methods in terms of speed and accuracy. The high resistance rates observed highlight the urgent need for effective diagnostic tools and infection control strategies to combat multidrug-resistant A. baumannii.