Explore the vast range of titles available.

Accurate estimates of groundwater modeling in arid regions have a crucial role in reaching a sustainable management of groundwater sources. However, groundwater modeling has been faced with different uncertainty sources; besides our imperfect knowledge, it is difficult to derive a proper prediction that can lead to reliable planning. This study aimed to improve the groundwater numerical simulations using different Model Averaging Techniques (MATs). For this, three numerical models, such as Finite Difference (FD), Finite Element (FE), and Meshfree (Mfree), were developed and their performance was verified in a real-world case study. Then various MATs including Simple Model Average (SMA), Weighted Average Method (WAM), Multi Model Super Ensemble (MMSE), Modified MMSE (M3SE) and Bayesian Model Averaging (BMA) were employed to improve the simulated groundwater level Fluctuations (outputs of three numerical models). The findings of this study demonstrated that the numerical model uncertainty is considerable and should not be neglected in the uncertainty analysis of groundwater modeling. In terms of RMSE, the lowest value of 0.148 m was obtained by Mfree while higher values of 1.355 m and 0.287 m are calculated for FD and FE respectively. In addition, the performance assessment of MATs showed a capacity to generate a skillful simulation compared to numerical predictions. Although the MMSE and M3SE (with RMSE values of 0.088 and 0.103 m) generated a desirable prediction in the majority of piezometers, they suffer from a main deficiency, such as the multicollinearity issue. From this perspective, it was concluded that the BMA produced a more reliable and reasonable prediction than other MATs.

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate.

This study intends to investigate the impacts of scheme type, time step, and error threshold on the stability of numerical simulation in the groundwater modeling. Hence, a two-dimensional finite element (FE) was implemented to simulate groundwater flow in a synthetic test case and a real-world study (Birjand aquifer). To verify the proposed model in both cases, the obtained results were compared with analytical solutions and observed values. The stability of numerical results was analyzed through different schemes and time-step sizes. Besides, the effect of the error threshold was examined by considering different threshold values. The results confirmed that the FE model has a good capacity to simulate groundwater fluctuations even for the real problem with more complexities. Examination of implicit outputs indicated that groundwater simulations based on this scheme have good accuracy, stability, and proper convergence in all time intervals. However, in the explicit and Crank–Nicolson schemes the time interval should be less than or equal to 0.001 and 0.1 day, respectively. Also, results reveal that for making stability in all schemes the value of the error threshold should not be more than 0.0001 m. Moreover, it derived that the boundary conditions of the aquifer influence the stability of numerical outputs. Finally, it was comprehended that as time interval and error threshold increases, the oscillation rate propagated.

The evaporation loss is a key component that affects managing the water resources of arid and semi-arid regions, where the resources are not uniformly distributed. Due to the climate condition and the physical characteristics of arid regions, a major proportion of precipitation is often unavailable through the flash floods, while merely a small fraction recharges the groundwater aquifers. Therefore, to achieve sustainable development, managing the water resources based on rainwater harvesting systems is inevitable. The main aim of this study was to assess the reliability of the rainwater harvesting systems designed for a future period (2017–2030). Thus, monthly climate data (e.g., precipitation) were simulated by using the outputs of general circulation models (GCMs) of the newest generation in the coupled model intercomparison project phase 5 (CMIP5) as the first step under two representative concentration pathways (RCPs), i.e., RCP2.6 and RCP8.5. Then, the data were downscaled spatially through bias-correction spatial disaggregation (BCSD) method for 20 grid points surrounding Birjand rain gauge station, east of Iran. Monthly precipitation of Birjand rain gauge station was interpolated automatically by means of the ordinary Kriging method during a future period, between 2017 until 2030. Data pre-processing in geostatistical methods, including investigating the normality, isotropic, trend analysis, and semi-variogram selection, was also carried out automatically through coding in MATLAB. Finally, using the interpolated monthly precipitation time series, the reliability of the precipitation harvesting systems was assessed for a different range of rooftop areas and storage tank capacities. Results indicated that this process can meet a significant volume of the household non-potable water demand by RWHS. Similar reliability values based on the projected monthly precipitation due to two GCMs and two RCPs were extracted for future period. The reliability of RWHS acquired by RCP2.6 will not widely diverse from RCP8.5. Totally, for semi-arid region, it is possible to supply about 20% of non-potable water demand in the future periods. Although this amount seems to be a low value, it should be noted that RWHS may prevent extra groundwater withdrawal and thus enhance the sustainability of the water resources.

Regenerative stem cell–like memory (T SCM ) CD8 + T cells persist longer and produce stronger effector functions. We found that MEK1/2 inhibition (MEKi) induces T SCM that have naive phenotype with self-renewability, enhanced multipotency and proliferative capacity. This is achieved by delaying cell division and enhancing mitochondrial biogenesis and fatty acid oxidation, without affecting T cell receptor-mediated activation. DNA methylation profiling revealed that MEKi-induced T SCM cells exhibited plasticity and loci-specific profiles similar to bona fide T SCM isolated from healthy donors, with intermediate characteristics compared to naive and central memory T cells. Ex vivo, antigenic rechallenge of MEKi-treated CD8 + T cells showed stronger recall responses. This strategy generated T cells with higher efficacy for adoptive cell therapy. Moreover, MEKi treatment of tumor-bearing mice also showed strong immune-mediated antitumor effects. In conclusion, we show that MEKi leads to CD8 + T cell reprogramming into T SCM that acts as a reservoir for effector T cells with potent therapeutic characteristics. Stem cell–like memory (T SCM ) CD8 + T cells are beneficial in antitumor responses, in part due to their ability to self-renew. Khleif and colleagues demonstrate that inhibition of the kinase MEK in CD8 + T cells favors induction of T SCM and superior antitumor responses.

Folate deficiency is a public health concern affecting all age groups worldwide. The available evidence reveals that adding probiotic bacteria to the yoghurt starter cultures during yoghurt production process under fermentation conditions increases the folate content of yoghurt. The present study was conducted to measure two folate derivatives, i.e., 5-methyltetrahydrofolate and 5-formyltetrahydrofolate, in bio-fortified yoghurt samples including (1) yoghurt containing Streptococcus thermophilus and Lactobacillus bulgaricus , (2) probiotic yoghurt containing Lactobacillus acidophilus LA-5 and Bifidobacterium lactis BB-12, (3) probiotic yoghurt containing native strains of Lactobacillus plantarum 15HN, (4) probiotic yoghurt containing native strains of Lactococcus lactis 44Lac, and (5) probiotic yoghurt containing commercial strains of Lactobacillus plantarum LAT BY PL. During storage at 4 °C for 21 days, the highest levels of 5-methyltetrahydrofolate and 5-formyltetrahydrofolate, which were statistically significant, were detected in the yoghurt made using Lact . plantarum 15HN. Moreover, the highest total folate concentration (1487 ± 96.42 μg/L) was specified in the yoghurt containing Lact . plantarum 15HN on the 7th day. It can be conjectured that this product can be suggested as a proper alternative to synthetic folic acid and may not have the side effects of using synthetic folic acid overdoses.

Cerebral ischemia and subsequent reperfusion, leading to reduced blood supply to specific brain areas, remain significant contributors to neurological damage, disability, and mortality. Among the vulnerable regions, the subcortical areas, including the hippocampus, are particularly susceptible to ischemia-induced injuries, with the extent of damage influenced by the different stages of ischemia. Neural tissue undergoes various changes and damage due to intricate biochemical reactions involving free radicals, oxidative stress, inflammatory responses, and glutamate toxicity. The consequences of these processes can result in irreversible harm. Notably, free radicals play a pivotal role in the neuropathological mechanisms following ischemia, contributing to oxidative stress. Therefore, the function of antioxidant enzymes after ischemia becomes crucial in preventing hippocampal damage caused by oxidative stress. This study explores hippocampal neuronal damage and enzymatic antioxidant activity during ischemia and reperfusion’s early and late stages. Graphical Abstract

Climate change, the emission of greenhouse gases, and air pollution are some of the most important and challenging environmental issues. One of the main sources of such problems is the field of transportation, which leads to the emission of greenhouse gases. An efficient way to deal with such problems is carrying out sustainable transportation to reduce the amount of air pollution in an efficient way. The evaluation of sustainable vehicles can be considered a multi-criteria decision-making (MCDM) method due to the existence of several criteria. In this paper, we aim to provide an approach based on MCDM methods and the spherical fuzzy set (SFS) concept to evaluate and prioritize sustainable vehicles for a transportation system in Tehran, Iran. Therefore, we have developed a new integrated approach based on the stepwise weight assessment ratio analysis (SWARA) and the measurement of alternatives and ranking according to the compromise solution (MARCOS) methods in SFS to assess the sustainable vehicles based on the criteria identified by experts. The evaluation results show that the main criterion of the environment has a high degree of importance compared to other criteria. Moreover, autonomous vehicles are the best and most sustainable vehicles to reduce greenhouse gas emissions. Finally, by comparing the ranking results with other decision-making methods, it was found that the proposed approach has high validity and efficiency.

Ovarian cancer (OC) remains a leading cause of gynaecological cancer deaths due to late diagnosis and the emergence of resistance to platinum‐based chemotherapy, like cisplatin (Cis). Here, we investigated the potential of metformin (Met), a drug commonly used for type 2 diabetes, to overcome Cis resistance in OC. Our findings revealed a synergistic effect of Met with Cis in inhibiting cell viability, proliferation and colony/sphere formation capacity in both cisplatin‐sensitive (A2780) and ‐resistant (A2780/CDDP) ovarian cancer cell lines. This synergistic action triggered apoptosis through DNA damage, S‐phase cell cycle arrest and modulation of autophagy. Met also significantly decreased the expression of pluripotency transcription factors (Oct‐4, Sox2 and Nanog), indicating its potential to target cancer stem cells (CSCs). Furthermore, the combination therapy downregulated multidrug resistance protein 1 (MDR1) and excision repair cross‐complementation group 1 (ERCC1) expression, thereby sensitising resistant cells to Cis‐induced cytotoxicity. Additionally, the combination treatment suppressed the Hedgehog (Hh) signalling pathway, which is an important factor in inhibiting CSCs. Our study highlights the potential of the Met signalling pathway to synergise with Cis, overcoming therapeutic resistance in OC by targeting diverse cellular processes, including CSCs, and warrants further investigation in preclinical models.

Metformin, a widely prescribed antihyperglycemic agent, plays a crucial role in the management of Type 2 diabetes by reducing hepatic glucose production and increasing insulin sensitivity. Its effectiveness in glucose regulation has made it a cornerstone in diabetes care, necessitating precise monitoring of drug levels to optimize therapeutic outcomes and minimize potential adverse effects. This review explores contemporary biosensor technologies designed for the sensitive detection of metformin, emphasizing their significance in clinical and pharmaceutical settings. We analyze various biosensor platforms, including electrochemical, optical, and piezoelectric systems, highlighting their principles, advantages, and challenges. Additionally, we discuss the integration of nanomaterials to enhance detection sensitivity and specificity. Given the rising prevalence of diabetes globally, the development of innovative biosensing strategies for metformin detection is paramount in ensuring effective patient management and improving treatment adherence. The insights gained from this review aim to propel further research and development in this vital area of biomedical engineering.