Heavy metals in the environment cause adverse effects on living organisms. Agro-wastes have the potential to remove heavy metals from aqueous solutions. In this study, the orange peel cellulose (OPC) beads were utilized as adsorbents to remove metals from wastewater. The surface of the adsorbent was studied by Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy coupled with energy dispersive x-ray spectroscopy (SEM-EDS). The concentrations of the metals before and after adsorption were measured using inductively coupled plasma mass spectrometry. The removal of the metal ions (i.e., Cr⁶⁺, Cd²⁺, and Pb²⁺) using the OPC was investigated by varying the pH, contact time, and adsorbent dosages parameters. The maximum removal efficiency obtained for the metal ions occurred at pHs 4–8. The use of the Langmuir isotherm and Freundlich isotherm models demonstrated the statistical significance of the heavy metal adsorption processes (R² > 0.96). At a neutral pH, the OPC adsorption order was Pb²⁺ > Cd²⁺ > Cr⁶⁺ with % removal values of 98.33, 93.91, and 33.50, respectively. The adsorption equilibrium for Cr⁶⁺ was reached after 36 h. For Cd²⁺ and Pb²⁺, equilibrium was reached after 8 and 12 h, respectively. The FT-IR and SEM-EDS confirmed the presence of many functional groups and elements on the adsorbent. The adsorption of heavy metals using the OPC is a low-cost, eco-friendly, and innovative method for the removal of metals in aquatic environments. The findings of this study will be highly significant for the public in the affected areas worldwide that have credible health concerns due to water contamination with heavy metals.

The orphan nuclear receptor TLX is a master regulator of postnatal neural stem cell (NSC) self-renewal and neurogenesis; however, it remains unclear how TLX expression is precisely regulated in these tissue-specific stem cells. Here, we show that a highly conserved cis-element within the Tlx locus functions to drive gene expression in NSCs. We demonstrate that the transcription factors SOX2 and MYT1 specifically interact with this genomic element to directly regulate Tlx enhancer activity in vivo. Knockdown experiments further reveal that SOX2 dominantly controls endogenous expression of TLX, whereas MYT1 only plays a modulatory role. Importantly, TLX is essential for SOX2-mediated in vivo reprogramming of astrocytes and itself is also sufficient to induce neurogenesis in the adult striatum. Together, these findings unveil functional genetic interactions among transcription factors that are critical to NSCs and in vivo cell reprogramming.

Recently, a new formulation containing metformin HCl (MFH) and remogliflozin etabonate (RGE) has been approved for the management of diabetes mellitus. However, only one analytical method has been reported for the simultaneous determination of both the analytes. Therefore, the current study was designed to develop simple UV derivative spectroscopic and rapid RP-HPLC methods for simultaneous determination of MFH and RGE. The chromatographic separation of MFH and RGE was performed using a monolithic C18 column with an optimized chromatographic conditions carried out by full factorial Box–Behnken design model. The spectroscopic technique was based on the determination of peak amplitude of second-order derivative UV spectra at zero crossings. Further, both the methods were validated and compared statistically using Student’s-t-test and F-test, and employed for the concurrent estimation of MFH and RGE in laboratory mixed solutions and formulations. Perturbation plots and response surface models showed the effect of chromatographic parameters and the final chromatographic condition was selected from 47 solutions suggested by the desirability function. Further, UV spectroscopic and HPLC procedures showed good linearity in the range of 1–24 µg/mL and 2–150 µg/mL for RGE and 2–30 µg/mL and 5–200 µg/mL for MFH, respectively. The average percent assay was found to be 99.51% and 99.80% for MFH and 99.60% and 100.07% for RGE by spectroscopic and HPLC methods, respectively. The proposed methods were simple, accurate, precise, and rapid. Therefore, they can be used for regular quality control of MFH and RGE formulations and dissolution studies as well.

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R²) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO₂e) and mean absolute percentage error (0.29 GtCO₂e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO₂ equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.

The Kingdom of Saudi Arabia has been experiencing consistent growth in industrial processes and product use (IPPU). The IPPU’s emission has been following an increasing trend. This study investigated time-series and cross-sectional analyses of the IPPU sector. Petrochemical, iron and steel, and cement production are the leading source categories in the Kingdom. In recent years, aluminum, zinc, and titanium dioxide production industries were established. During the last ten years, a significant growth was observed in steel, ethylene, direct reduce iron (DRI), and cement production. The growth of this sector depends on many factors, including domestic and international demand, socioeconomic conditions, and the availability of feedstock. The emissions from IPPU without considering energy use was 78 million tons of CO₂ equivalent (CO₂eq) in 2020, and the cement industry was the highest emitter (35.5%), followed by petrochemical (32.3%) and iron and steel industries (16.8%). A scenario-based projection analysis was performed to estimate the range of emissions for the years up to 2050. The results show that the total emissions could reach between 199 and 426 million tons of CO₂eq in 2050. The Kingdom has started initiatives that mainly focus on climate change adaptation and economic divergence with mitigation co-benefits. In general, the focus of such initiatives is the energy sector. However, the timely accomplishment of the Saudi Vision 2030 and Saudi Green Initiative will affect mitigation scenarios significantly, including in the IPPU sector. The mitigation opportunities for this sector include (i) energy efficiency, (ii) emissions efficiency, (iii) material efficiency, (iv) the re-use of materials and recycling of products, (v) intensive and longer use of products, and (vi) demand management. The results of this study will support the Kingdom in developing an appropriate climate change mitigation roadmap.