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Background: Piper chaba has been traditionally using for its potential health benefits. The principal goal of this investigation was to assess the pharmacological characteristics of the EEPCS, focusing on investigating its potential antihyperuricemic effects, ability to inhibit xanthine oxidase (XO) enzyme, and antioxidant properties. Methods: The antioxidant capability of the extract was assessed using the DPPH radical assay. The extract was evaluated in vitro aimed at its xanthine oxidase inhibitory (XOI) action and further experimented for its antihyperuricemic effect on the Potassium oxonate (PO) induced hyperuricemia murine model. Besides, molecular docking techniques were employed to interpret the inhibitory mechanism of the compounds identified through GCMS analysis of the EEPCS. Results: There was no safety concern at doses up to 3000 mg/kg of the extract as confirmed by mice model studies. The extract displayed potent the antioxidant properties during quantitative antioxidant test (IC50=104.9 ± 0.99 µg/mL). In XOI test, the extract was found to be effective with an IC50 value of 26.14 μg/mL. In addition, the extract was found to decrease the Serum uric acid (SUA) levels by 36.72 and 61.84 % at 100 and 200 mg/kg body weight respectively, compared to the hyperurecaemic animals. The GCMS analysis of the extract revealed eighteen distinct compounds, which were docked against human peroxiredoxin 5 and xanthin oxidase proteins. Selina-3,7(11)-diene (CID: 6429221) was the most effective phytochemical for the human peroxiredoxin 5 protein having binding score −6.2 Kcal/mol compared to standard ascorbic acid (5.6 Kcal/mol). Besides, gamma-muurolene (-7.9 Kcal/mol) was the most effective phytochemical for the XO protein compared to standard allopurinol (−6.4 Kcal/mol). Conclusion: Our experimental data showed that the EEPCS has potentiality as an antioxidant and hypouricemic agent that could be contributed to treat and manage oxidative stress (OS), gout and hyperuricemia.

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Downscaling global climate models (GCMs) provides crucial high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g. bias, spatial pattern, annual cycle and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured, i.e. the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests of model independence, data availability and future climate change spread are conducted, resulting in a final subset of two independent models that align with our a priori expectations for downscaling over the Coordinated Regional Climate Downscaling Experiment –Southeast Asia (CORDEX-SEA).

The effect of climate change on water availability and agriculture water demand is crucial for assessing agricultural productivity and economic development in semi-arid regions. The present study examines the crop water requirement (CWR) and irrigation water requirement (IWR) of the Bhadra and Tungabhadra (TB) command areas, with a focus on forecasting future irrigation water needs. Using the CROPWAT 8.0 software, CWR and IWR were estimated for the base period (1975–2010) and three future periods: near future (2023–2048), middle future (2049–2074), and far future (2075–2099). Five best-performing Global Climate Models (GCMs) were utilized under two shared socioeconomic pathways (SSPs) (i.e., SSP-245 and SSP-585). The results indicate that in the Bhadra command area, CWR increases during the kharif season under both SSPs. However, monthly IWR for the kharif season experiences a significant decrease, except for June. In the TB command area, CWR shows a decreasing trend, while monthly IWR increases for both seasons in future periods. The SSP-585 scenario exhibits a more pronounced increment in CWR and IWR for both command areas. The results enhance comprehension of water demand dynamics in agricultural areas, assisting policymakers and stakeholders in devising effective strategies to address climate change impacts on agriculture and encourage sustainable practices. HIGHLIGHTS Future crop water and Irrigation water requirements are evaluated for Bhadra and Tungabhadra command areas.; Compromise programming for selection of best performing GCMs and CROPWAT 8.0 for modeling variation in future CWR and IWR under changing climate for SSP-245 and SSP-585 scenarios.; The study enhances understanding water demand dynamics in command areas, assisting policymakers and stakeholders in developing strategies to address climate change impact and promote sustainable practices.;

Abstract In the realm of fruit processing, karonda juice stands out for its elevated moisture levels, posing challenges in storage and transportation. This study presents a pioneer effort for concentration of karonda juice through optimizing the process parameters viz., temperature and time using central composite design (CCD) of response surface methodology (RSM) within rotary evaporator setup. Through ten experimental runs, variations were introduced, adjusting temperatures from 45 to 55 °C and duration from 90 to 210 min. The results showcased the efficacy of vacuum concentration, reducing moisture content to 16.43% and significantly elevating total soluble solids (TSS) from 9.02 to 89˚B. Moreover, key nutrients experienced substantial increase: ascorbic acid surged from 3.79 to 14.66 mg per 100 g, total phenolic content (TPC) soared from 100.74 to 386.97 mg GAE per 100 g, total antioxidant activity (mg AAE/100 g) escalated from 76.74 to 328.10 mg AAE per 100 g, anthocyanin content (mg/100 g) increased from 10.9 to 129.34 mg per 100 g, and iron content rose from 39 to 150.54 mg per 100 g. GC–MS profile elucidated compounds like Tetrahydrofuran, 3-Hydroxy-3-methylvaleric acid, 3-Hexen-2-one, Hydroperoxide and Octane. The optimization process, guided by RSM revealed the ideal parameters: 55 °C for 150 min, marking a significant advancement in karonda concentration techniques.

Abstract Indian mustard (Brassica juncea; Brassicaceae) is an edible, oilseeds-yielding crop widely consumed as a food spice owing to its richness in nutrients with several health benefits. The current study aims to dissect the B. juncea metabolome heterogeneity among its different organs including leaf, stem, flower, and seed. Moreover, assessing the metabolome differences between two different varieties RH-725 and RH-761 grown at the same conditions. Gas chromatography-mass spectrometry (GC–MS) post-silylation was used to dissect the composition of nutrient metabolites coupled to multivariate data analysis. Variation in sulphur aglycones was measured using headspace-solid phase-microextraction HS-SPME coupled to GC–MS. A total of 101 nutrient metabolites were identified with the abundance of sugars represented by monosaccharides in all organs, except for seeds which were enriched in disaccharides (sucrose). α-Linolenic acid was detected as a marker fatty acid in leaf from RH-725 at 12.5 µg/mg. Malic acid was detected as a significant variant metabolite between the two varieties as detected in the leaf from the RH-725 variety at ca. 128.2 µg/mg compared to traces in RH-761. 7 Volatile sulphur compounds were detected at comparable levels in RH-725 and RH-761, with 3-butenyl isothiocyanate was the most abundant at 0.8–2 ng/mg.

We present a new routine for calculating the non-local thermodynamic equilibrium (non-LTE) 15 µm CO2 cooling–heating of mesosphere and lower thermosphere in general circulation models. It uses the optimized models of the non-LTE in CO2 for day and night conditions and delivers cooling–heating with an error not exceeding 1 K d−1 even for strong temperature disturbances. The routine uses the accelerated lambda iteration and opacity distribution function techniques for the exact solution of the non-LTE problem and is about 1000 times faster than the standard matrix and line-by-line solution. It has an interface for feedbacks from the model and is ready for implementation. It may use any quenching rate coefficient of the CO2(ν2)+O(3P) reaction, handles large variations in O(3P), and allows the user to vary the number of vibrational levels and bands to find a balance between the calculation speed and accuracy. The suggested routine can handle the broad variation in CO2 both below and above the current volume mixing ratio, up to 4000 ppmv. This allows the use of this routine for modeling Earth's ancient atmospheres and the climate changes caused by increasing CO2.

The selection of an appropriate subset of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) for long-term hydrological simulation at the basin scale is a necessity. This study selected high-performing GCMs among 32 available CMIP6 GCMs on the basis of reproducing the observed precipitation over the main watershed in South Korea during the historical period. An integrated selection approach based on four spatial performance assessment metrics was proposed to better estimate changes in precipitation using the simulated GCMs precipitation. Results revealed that the spatial performance over different GCMs could provide an effective means of selecting GCMs for hydrological study over the major river watersheds in South Korea. The four top-ranked GCMs are FIO-ESM-2-0, CESM2-WACCM, CESM2, and CMCC-ESM2, and they overestimated precipitation in South Korea during the historical period, with a bias of 10–25%. However, this study confirmed that a formal bias correction for these GCMs is not recommended prior to model selection, and the ranking of GCMs under the bias correction could be problematic. The proposed approach in this study can be applied to numerous GCMs, climate variables, and other regions to select representative GCMs to reduce the uncertainties in terms of the spatial patterns observed. HIGHLIGHTS The goodness-of-fit measures are proposed for the selection of climate models.; A comprehensive rating metric approach is proposed to evaluate the overall ranking of the GCMs.; Exploration of the spatial performance of different GCMs can effectively select GCMs.; A formal bias correction for these GCMs is not recommended prior to model selection, and the ranking of GCMs under the bias correction could be problematic.;

Abstract The quality of olive fruit (OF) is widely affected due to geographical variation, affecting OF's chemical composition and biological properties. It is a novel and first-time study to evaluate the quality variation of 42 olive samples from different geographical origins based on phytochemical profile and their biological activities. The study reports the presence of unique chemical markers responsible for the difference in quality and biological activity of the olive samples. Biological activity (cytotoxic and antimicrobial) with GCMS phytochemical profile was evaluated. GCMS analysis confirmed the presence of 111 volatile compounds from various chemical classes with range (%) and average (%): esters (21.61–60.49) and 44.62, alcohols (20.73–49.2) and 38.06, hydrocarbons (3–38.88) and 15.39, ketones (0.16–3.87) and 0.75, acids (0.07–2.62) and 0.27, and aldehydes (0.12–1.47) and 0.45. The predominant ester was 13-methyl-pentadecanoic acid methyl ester, a differentiation marker between these samples. Cytotoxicity assay showed a significant inhibitory effect against MCF7 (8–64%) and HCT116 (0.11–44%) cell lines, whereas the extracts with the highest cytotoxicity observed were O17 (52.00 ± 2.00) and O25 (64.00 ± 4.88). The antimicrobial activity exhibited a range of zones of inhibition (mm) against P. aeruginosa (0.00–17.00), E. coli (0.00–15.00), S. aureus (0.00–13), and resistant S. aureus, i.e., MRSA (0.00–12.00). The extracts with the highest antimicrobial activity, i.e., O8 and O39 had identical MIC and MBC of 12.5 and 25 µg/ml against P. aeruginosa. In contrast, an MIC (50 and 25) and MBC (100 and 50) against E. coli were determined for O39 and O8. The statistical PCA and K-mean cluster analysis (P

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Abstract Background There is an urge for traditional herbal remedies as an alternative to modern medicine in treating several diseases. A significant number of modern pharmaceutical drugs are based on or derived from medicinal plants or their extracts. These drugs derived from the plant origin have various antimicrobial, antioxidant, anticancer, anti-inflammatory activities. Alangium salviifolium belongs to Cornaceae family and is well known for its medicinal properties. The present study was carried out to evaluate the antibacterial, antioxidant effect and possible bioactive components present in the chloroform, acetone, ethanol, methanol and aqueous extract of Alangium salviifolium leaves. Methodology Dried leaves of Alangium salviifolium were subjected to serial solvent extraction using increasing polarity of solvents, i.e., chloroform, acetone, methanol, ethanol, and distilled water. Crude extracts were further tested for qualitative analysis of phytochemicals using standard procedure, while GCMS analysis was performed to identify the probable phytocompounds. Antibacterial activity was performed against bacterial pathogens using agar well method, whereas antioxidant activity was performed using in vitro PM, DPPH and FRAP assays. Results Phytochemical analysis of the extracts revealed the presence of key phytochemical classes. Using gas chromatography-mass spectrometry, several high and low molecular weight chemical compound kinds were discovered. These chemical substances are regarded as having significant biological and pharmacological effects. All crude extracts had considerable and comparable in vitro antioxidant and antibacterial properties. Conclusions According to the findings of this study, Alangium salviifolium leaves are a rich source of phytoconstituents that are crucial in stopping the advancement of numerous disorders.

Climate change significantly impacts the natural systems, accelerating the global water cycle, and impacting various ecosystem services. However, the expected effects of climate change on the frequency and severity of extreme events on hydrological systems vary significantly with location. The present study investigates the uncertainties engulfed in hydrological predictions for the Tungabhadra River Basin. The ensemble streamflow projections were generated using four hydrological models, five climate models, and four climate scenarios to illustrate the associated uncertainties. The uncertainty in hydrological components such as streamflow (QQ), water availability (WA), and potential evapotranspiration (PET) was analysed in the future period (2015–2100). The results suggest that, in the monsoon period, precipitation projections increase by about 10.43–222.5%, whereas QQ projections show an increment between 34.50 and 377.7%. The analysis of variance (ANOVA) technique is used to further quantify the contribution of different sources to the total uncertainty. Furthermore, the ensemble spread is optimized using quantile regression forests (QRF), and the post-processed flows are likely to decrease up to 7% in June and increase up to 70% in September. This study is envisaged to give insights into the quantification of uncertainties in the prediction of future streamflow for rational and sustainable policymaking. HIGHLIGHTS Hydrological assessment of the Tungabhadra Basin using CMIP6 GCMs and multiple hydrological models.; Diagnostic evaluation of performance of hydrological models were estimated.; Uncertainty in the ensemble flows decomposed using the analysis of variance (ANOVA) technique.; The ensemble spread is optimized using quantile regression forests (QRF), and the post-processed flows were generated using the QRF method.;

Abstract Idealized general circulation models (GCMs) suggest global‐mean precipitation ceases to increase with warming in hot climates because evaporation is limited by the available solar radiation at the surface. We investigate the extent to which this generalizes in comprehensive GCMs. We find that in the Community Atmosphere Model, global‐mean precipitation increases approximately linearly with global‐mean surface temperatures up to about 330 K, where it peaks at 5 mm day−1. Beyond 330 K, global‐mean precipitation decreases substantially despite increasing surface temperatures because of increased atmospheric shortwave absorption by water vapor, which decreases the shortwave radiation available for evaporation at the surface. Precipitation decreases in the tropics and subtropics but continues to increase in the extratropics because of continuously strengthening poleward moisture transport. Precipitable water increases everywhere, resulting in longer water‐vapor residence times and implying more episodic precipitation. Other GCMs indicate global‐mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures.