Unveiling antibiotic contamination in surface water: A study of the Huaihe River Basin's Huaibei Plain, a significant Chinese herbal medicine planting region.

The abuse of antibiotics has caused the accumulation of antibiotic residues in environmental media, threatening the ecosystem and human health. Many studies on the distribution of aqueous antibiotics have been reported. However, the pollution status of antibiotics in the environment in Chinese herbal medicine planting areas is rarely comprehensively clarified, resulting in the lack of updated pollution data and conducive suggestions for ecological cultivation and sustainable development of Chinese herbal medicine. Thus, we comprehensively investigated the distribution, profiles, sources, and risks of the antibiotics in the surface water of an important tributary of the Huaihe River Basin, located in Bozhou City, a significant Chinese herbal medicine planting region. Solid-phase extraction coupled with an ultra-performance liquid chromatography-tandem mass spectrometer (SPE-UPLC-MS) was utilized to detect the antibiotics in the water. 27 kinds of antibiotics were identified with total concentrations ranging from 75.01 to 1737.99 ng·L-1, with doxycycline (DC) and doxycycline hydrochloride (DCH) possessed the highest concentration. And DC, DCH, oxilinic acid (OA), sulfamethoxazole (SMZ), clarithromycin (CLA), and roxithromycinum (ROX) were the main antibiotics detected in this basin. Correlation analysis and principal component analysis (PCA) indicated that animal husbandry was the primary source of antibiotics. Furthermore, the ecological risk assessment revealed that certain antibiotics could seriously threaten the survival of aquatic organisms, implying that local Chinese herbal medicines might be at similar growth risk. The drinking risk assessment showed that antibiotics in the water posed low risks for human, and children faced a greater drinking risk than adults. The study can help to facilitate the management of aqueous antibiotic pollution for the ecological cultivation and safe production of Chinese herbal medicine.

Do Added Microplastics, Native Soil Properties, and Prevailing Climatic Conditions Have Consequences for Carbon and Nitrogen Contents in Soil? A Global Data Synthesis of Pot and Greenhouse Studies.

Microplastics threaten soil ecosystems, strongly influencing carbon (C) and nitrogen (N) contents. Interactions between microplastic properties and climatic and edaphic factors are poorly understood. We conducted a meta-analysis to assess the interactive effects of microplastic properties (type, shape, size, and content), native soil properties (texture, pH, and dissolved organic carbon (DOC)) and climatic factors (precipitation and temperature) on C and N contents in soil. We found that low-density polyethylene reduced total nitrogen (TN) content, whereas biodegradable polylactic acid led to a decrease in soil organic carbon (SOC). Microplastic fragments especially depleted TN, reducing aggregate stability, increasing N-mineralization and leaching, and consequently increasing the soil C/N ratio. Microplastic size affected outcomes; those <200 µm reduced both TN and SOC contents. Mineralization-induced nutrient losses were greatest at microplastic contents between 1 and 2.5% of soil weight. Sandy soils suffered the highest microplastic contamination-induced nutrient depletion. Alkaline soils showed the greatest SOC depletion, suggesting high SOC degradability. In low-DOC soils, microplastic contamination caused 2-fold greater TN depletion than in soils with high DOC. Sites with high precipitation and temperature had greatest decrease in TN and SOC contents. In conclusion, there are complex interactions determining microplastic impacts on soil health. Microplastic contamination always risks soil C and N depletion, but the severity depends on microplastic characteristics, native soil properties, and climatic conditions, with potential exacerbation by greenhouse emission-induced climate change.

Patterns and drivers of plant carbon, nitrogen, and phosphorus stoichiometry in a novel riparian ecosystem.

Carbon (C), nitrogen (N), and phosphorus (P) stoichiometry serve as valuable indices for plant nutrient utilization and biogeochemical cycling within ecosystems. However, the allocation of these nutrients among different plant organs and the underlying drivers in dynamic riparian ecosystems remain inadequately understood. In this study, we gathered plant samples from diverse life forms (annuals and perennials) and organs (leaves, stems, and roots) in the riparian zone of the Three Gorges Reservoir Region (TGRR) in China-a novel ecosystem subject to winter flooding. We used random forest analysis and structural equation modeling to find out how flooding, life forms, plant communities, and soil variables affect organs C, N, and P levels. Results showed that the mean concentrations of plant C, N, and P in the riparian zone of the TGRR were 386.65, 19.31, and 5.27 mg/g for leaves respectively, 404.02, 11.23, and 4.81 mg/g for stems respectively, and 388.22, 9.32, and 3.27 mg/g for roots respectively. The C:N, C:P and N:P ratios were 16.15, 191.7 and 5.56 for leaves respectively; 26.98, 273.72 and 4.6 for stems respectively; and 16.63, 223.06 and 4.77 for roots respectively. Riparian plants exhibited nitrogen limitation, with weak carbon sequestration, low nutrient utilization efficiency, and a high capacity for nutrient uptake. Plant C:N:P stoichiometry was significantly different across life forms and organs, with higher N and P concentrations in leaves than stems and roots, and higher in annuals than perennials. While flooding stress triggered distinct responses in the C, N, and P concentrations among annual and perennial plants, they maintained similar stoichiometric ratios along flooding gradients. Furthermore, our investigation identified soil properties and life forms as more influential factors than plant communities in shaping variations in C:N:P stoichiometry in organs. Flooding indirectly impacts plant C:N:P stoichiometry primarily through alterations in plant community composition and soil factors. This study underscores the potential for hydrologic changes to influence plant community composition and soil nutrient dynamics, and further alter plant ecological strategies and biogeochemical cycling in riparian ecosystems.

The effect of turmeric and black pepper powder incorporated in breakfast on postprandial glycemia, appetite, palatability, and gastrointestinal well-being in normal-weight adults.

Culinary herbs and spices are primarily known as flavor enhancers, research suggests that black pepper (Piper nigrum) and turmeric (Curcuma longa) have now been proven to prevent many non-communicable chronic diseases such as diabetes. Bioactive components of black pepper and turmeric ameliorate glucose metabolism and appetite regulation. The present research was designed to investigate the impact of turmeric and black pepper on blood glycemia, gastrointestinal well-being, appetite, and palatability. In a randomized crossover study, four iso-caloric experimental meals each having 50 g of available carbohydrates were subjected to healthy human participants (N = 20). Turmeric and black pepper were incorporated in the breakfast meal, 1 g black pepper (BP), 1 g turmeric (TR), and combination of the (BP + TR) was added in the breakfast. Standard questionnaires were used to evaluate palatability, subjective appetite, and gastrointestinal well-being. Blood glycemia, subjective gastrointestinal well-being, and appetite were measured at 0, 30, 60, 120, and 180 min. Experimental meals BP and BP + TR resulted in lower blood glycemia (p < .05) significantly compared to control meal. A decrease in perceived eating ability and hunger, and an increase in satiety after BP + TR and BP meal was observed. No significant changes were observed after consuming test meals on gastrointestinal well-being. Compared to control and BP + TR meals, BP and TR meals had considerably lower palatability. Results showed that compared to the control intake of starchy meals supplemented with black pepper and turmeric reduced postprandial glycemia, hunger, and perceived eating ability without affecting gastrointestinal well-being.

Microorganism-mediated biodegradation for effective management and/or removal of micro-plastics from the environment: a comprehensive review.

Micro- plastics (MPs) pose significant global threats, requiring an environment-friendly mode of decomposition. Microbial-mediated biodegradation and biodeterioration of micro-plastics (MPs) have been widely known for their cost-effectiveness, and environment-friendly techniques for removing MPs. MPs resistance to various biocidal microbes has also been reported by various studies. The biocidal resistance degree of biodegradability and/or microbiological susceptibility of MPs can be determined by defacement, structural deformation, erosion, degree of plasticizer degradation, metabolization, and/or solubilization of MPs. The degradation of microplastics involves microbial organisms like bacteria, mold, yeast, algae, and associated enzymes. Analytical and microbiological techniques monitor microplastic biodegradation, but no microbial organism can eliminate microplastics. MPs can pose environmental risks to aquatic and human life. Micro-plastic biodegradation involves fragmentation, assimilation, and mineralization, influenced by abiotic and biotic factors. Environmental factors and pre-treatment agents can naturally degrade large polymers or induce bio-fragmentation, which may impact their efficiency. A clear understanding of MPs pollution and the microbial degradation process is crucial for mitigating its effects. The study aimed to identify deteriogenic microorganism species that contribute to the biodegradation of micro-plastics (MPs). This knowledge is crucial for designing novel biodeterioration and biodegradation formulations, both lab-scale and industrial, that exhibit MPs-cidal actions, potentially predicting MPs-free aquatic and atmospheric environments. The study emphasizes the urgent need for global cooperation, research advancements, and public involvement to reduce micro-plastic contamination through policy proposals and improved waste management practices.

Improving Protein-Protein Interaction Prediction Using Protein Language Model and Protein Network Features.

Interactions between proteins are ubiquitous in a wide variety of biological processes. Accurately identifying the protein-protein interactions (PPI) is of significant importance for understanding the mechanisms of protein functions and facilitating drug discovery. Although the wet-lab technological methods are the best way to identify PPI, their major constraints are their time-consuming nature, high cost, and labor-intensiveness. Hence, lots of efforts have been made towards developing computational methods to improve the performance of PPI prediction. In this study, we propose a novel hybrid computational method (called KSGPPI) that aims at improving the prediction performance of PPI via extracting the discriminative information from protein sequences and interaction networks. The KSGPPI model comprises two feature extraction modules. In the first feature extraction module, a large protein language model, ESM-2, is employed to exploit the global complex patterns concealed within protein sequences. Subsequently, feature representations are further extracted through CKSAAP, and a two-dimensional convolutional neural network (CNN) is utilized to capture local information. In the second feature extraction module, the query protein acquires its similar protein from the STRING database via the sequence alignment tool NW-align and then captures the graph embedding feature for the query protein in the protein interaction network of the similar protein using the algorithm of Node2vec. Finally, the features of these two feature extraction modules are efficiently fused; the fused features are then fed into the fully connected neural networks to predict PPI. The results of five-fold cross-validation on the used benchmarked datasets demonstrate that KSGPPI achieves an average prediction accuracy of 88.96%. Additionally, the average Matthews correlation coefficient value (0.781) of KSGPPI is significantly higher than that of those state-of-the-art PPI prediction methods. The standalone package of KSGPPI is freely downloaded at https://github.com/rickleezhe/KSGPPI.

DPI_CDF: druggable protein identifier using cascade deep forest.

BACKGROUND: Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory. METHODS: In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. RESULTS: The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. AVAILABILITY: The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Fractional epidemic model of coronavirus disease with vaccination and crowding effects.

Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh-Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if R 0 < 1 and endemic equilibrium is locally asymptotically stable if R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if R 0 < 1 and endemic equilibrium is globally asymptotically stable if R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.

Could soil microplastic pollution exacerbate climate change? A meta-analysis of greenhouse gas emissions and global warming potential.

Microplastics pollution and climate change are primarily investigated in isolation, despite their joint threat to the environment. Greenhouse gases (GHGs) are emitted during: the production of plastic and rubber, the use and degradation of plastic, and after contamination of environment. This is the first meta-analysis to assess underlying causal relationships and the influence of likely mediators. We included 60 peer-reviewed empirical studies; estimating GHGs emissions effect size and global warming potential (GWP), according to key microplastics properties and soil conditions. We investigated interrelationships with microbe functional gene expression. Overall, microplastics contamination was associated with increased GHGs emissions, with the strongest effect (60%) on CH4 emissions. Polylactic-acid caused 32% higher CO2 emissions, but only 1% of total GWP. Phenol-formaldehyde had the greatest (175%) GWP via 182% increased N2O emissions. Only polystyrene resulted in reduced GWP by 50%, due to N2O mitigation. Polyethylene caused the maximum (60%) CH4 emissions. Shapes of microplastics differed in GWP: fiber had the greatest GWP (66%) whereas beads reduced GWP by 53%. Films substantially increased emissions of all GHGs: 14% CO2, 10% N2O and 60% CH4. Larger-sized microplastics had higher GWP (125%) due to their 9% CO2 and 63% N2O emissions. GWP rose sharply if soil microplastics content exceeded 0.5%. Higher CO2 emissions, ranging from 4% to 20%, arose from soil which was either fine, saturated or had high-carbon content. Higher N2O emissions, ranging from 10% to 95%, arose from soils that had either medium texture, saturated water content or low-carbon content. Both CO2 and N2O emissions were 43%-56% higher from soils with neutral pH. We conclude that microplastics contamination can cause raised GHGs emissions, posing a risk of exacerbating climate-change. We show clear links between GHGs emissions, microplastics properties, soil characteristics and soil microbe functional gene expression. Further research is needed regarding underlying mechanisms and processes.

Investigating the teratogenic potential of diclofenac sodium on chick embryos: A warning for pregnant women.

Teratogenic and embryotoxic effect of diclofenac sodium (DS) on different developmental stages of the chick-embryos was investigated by examining different parameters such as its mortality rate, hatching, morphological measurements, weighing its internal organs and calculation of different indices. Experiment was divided into four trials with different dose (0.1 mL, 0.2 mL, 0.3 mL in groups A, B, and C, respectively and group D received 0.3 mL saline solution (0.9% NaCl) and group E remained un-injected) administration and observation. Results of first and second trial showed statistically (p<0.01) significant difference in bodyweight, body-length, forelimb and hindlimb length between experimental and control groups. In third trial, diclofenac sodium administration showed a statistically (p<0.01) significant difference in the bodyweight, body-length, forelimb, hindlimb length, liver weight, egg weight (EE ratio) and kidney somatic index (KSI). The beak-size, heart weight, kidney weight, cardiac somatic index (CSI) and hepato somatic index (HSI) were not significant (p>0.05) when compared with the control groups. In trial 4, forelimb, hindlimb length, heart weight, CSI and HSI were statistically (p<0.01) significant. Body-length and liver weight were significant (p<0.05). While bodyweight, beak size, kidney weight and KSI were non-significant (p>0.05). The mortality rate was increased with increase dose of DS and also affected the hatching. DS effect on chick embryos can be applied to humans because the early development of mammals and birds are closely related. So, it was concluded that DS should be used with caution during pregnancy especially during first trimester of pregnancy.

Toxicological assessment of dietary exposure of polyethylene microplastics on growth, nutrient digestibility, carcass and gut histology of Nile Tilapia (Oreochromis niloticus) fingerlings.

This study was conducted to ascertain the negative effects of dietary low-density polyethylene microplastics (LDPE-MPs) exposure on growth, nutrient digestibility, body composition and gut histology of Nile tilapia (Oreochromis niloticus). Six sunflower meal-based diets (protein 30.95%; fat 8.04%) were prepared; one was the control (0%) and five were incorporated with LDPE-MPs at levels of 2, 4, 6, 8 and 10% in sunflower meal-based diets. A total of eighteen experimental tanks, each with 15 fingerlings, were used in triplicates. Fish were fed at the rate of 5% biomass twice a day for 60 days. Results revealed that best values of growth, nutrient digestibility, body composition and gut histology were observed by control diet, while 10% exposure to LDPE-MPs significantly (P < 0.05) reduced weight gain (WG%, 85.04%), specific growth rate (SGR%, 0.68%), and increased FCR (3.92%). The findings showed that higher level of LDPE-MPs (10%) exposure in the diet of O. niloticus negatively affects nutrient digestibility. Furthermore, the results revealed that the higher concentration of LDPE-MPs (10%) had a detrimental impact on crude protein (11.92%) and crude fat (8.04%). A high number of histological lesions were seen in gut of fingerlings exposed to LDPE-MPs. Hence, LDPE-MPs potentially harm the aquatic health.

Multivalent Sulfur Vacancy-Rich NiCo2 S4 @MnO2 Urchin-Like Heterostructures for Ambient Electrochemical N2 Reduction to NH3.

Innovative advances in the exploitation of effective electrocatalytic materials for the reduction of nitrogen (N2 ) to ammonia (NH3 ) are highly required for the sustainable production of fertilizers and zero-carbon emission fuel. In order to achieve zero-carbon footprints and renewable NH3 production, electrochemical N2 reduction reaction (NRR) provides a favorable energy-saving alternative but it requires more active, efficient, and selective catalysts. In current work, sulfur vacancy (Sv)-rich NiCo2 S4 @MnO2 heterostructures are efficaciously fabricated via a facile hydrothermal approach followed by heat treatment. The urchin-like Sv-NiCo2 S4 @MnO2 heterostructures serve as cathodes, which demonstrate an optimal NH3 yield of 57.31 µg h-1  mgcat -1 and Faradaic efficiency of 20.55% at -0.2 V versus reversible hydrogen electrode (RHE) in basic electrolyte owing to the synergistic interactions between Sv-NiCo2 S4 and MnO2 . Density functional theory (DFT) simulation further verifies that Co-sites of urchin-like Sv-NiCo2 S4 @MnO2 heterostructures are beneficial to lowering the energy threshold for N2 adsorption and successive protonation. Distinctive micro/nano-architectures exhibit high NRR electrocatalytic activities that might motivate researchers to explore and concentrate on the development of heterostructures for ambient electrocatalytic NH3 generation.

Exploring microgel adsorption: synthesis, classification, and pollutant removal dynamics.

Microgels have gained significant importance for the removal of pollutants owing to their stimulus-responsive behavior, high stability, and reusable capacity. However, despite these advantages, several hurdles need to be overcome to fully maximize their potential as effective adsorbents for eradicating various contaminants from the environment, such as metallic cations, organic compounds, anions, harmful gases, and dyes. Therefore, a critical review on the adsorption of pollutants by microgels is needed. In this regard, this review presents the latest developments in the adsorptive properties of microgels. The synthetic methods, architectural structures, and stimulus-responsive behavior of microgels are explained in detail. In addition, this review explores various factors that directly influence the adsorption of pollutants by microgels, such as pH, feed composition, content of pollutants, content of comonomers, agitation time, temperature, microgel dose, nature of both adsorbates (pollutants) and adsorbents (microgels), nature of the medium, and ionic strength. Various adsorption isotherms are also explored together with the kinetic aspects of the adsorption process to provide a comprehensive understanding.

Unified mRNA Subcellular Localization Predictor based on machine learning techniques.

BACKGROUND: The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost. METHODS: In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER). RESULTS: The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. AVAILABILITY: We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

Steroids and myocardial infarction: Investigating safety and short-term mortality in critical post-myocardial infarction patients.

BACKGROUND: Conventionally, in the pre-percutaneous intervention era, free wall rupture is reported to be a major concern for using steroids in myocardial infarction (MI) patients. Therefore, the aim of this study was to evaluate the safety of the use of steroids in critically ill post-MI patients in terms of hospital course and short-term (up to 180-day) mortality. METHODS: We included patients admitted to CCU diagnosed with MI, undergone revascularization, critically ill, and requiring mechanical ventilator (MV) support. The hospital course and short-term (up to 180-day) mortality were independently compared between steroid and non-steroid cohorts and propensity-matched non-steroid cohorts. RESULTS: A total of 312 patients were included, out of which steroids were used in 93(29.8%) patients during their management. On periodic bedside echocardiography, no free wall rupture was documented in the steroid or non-steroid cohort. When compared steroids with a propensity-matched non-steroid cohort, MV duration >24 h was 66.7% vs. 59.1%; p = 0.288, major bleeding was 6.5% vs. 3.2%; p = 0.305, need for renal replacement therapy was 9.7% vs. 8.6%; p = 0.799, in-hospital mortality was 35.5% vs. 23.7%; p = 0.077, and 180-day mortality was 48.4% vs. 41.9%; p = 0.377, respectively. The hazard ratio was 1.22 [95% CI: 0.80 to 1.88] compared to the propensity-matched non-steroid cohort. The ejection fraction (%) was found to be the independent predictor of 180-day mortality with an adjusted odds ratio of 0.92 [95% CI: 0.86 to 0.98]. CONCLUSIONS: In conclusion, using steroids is safe in post-MI patients with no significant increase in short-term mortality risk.

Identification of provenance of Basmati rice grown in different regions of Punjab through multivariate analysis.

High-priced Basmati rice is vulnerable to deliberate mislabeling to increase profits. This type of fraud may lower consumers' confidence as inferior products can affect brand reputation. To address this problem, there is a need to devise a method that can efficiently distinguish Basmati rice grown in regions that are famous versus the regions that are not suitable for their production. Therefore, in this investigation, thirty-six samples of Basmati rice were collected from two zones of Punjab province (one known for Basmati rice) of Pakistan which is the major producer of Basmati rice. The elemental composition of rice samples was assessed using inductively coupled plasma-optical emission spectrometry and an organic elemental analyzer, whereas data on δ13C was acquired using isotopic ratio-mass spectrometry. Regional clustering of samples based on their respective cultivation zones was observed using multivariate data analysis techniques. Partial least squares-discriminant analysis was found to be effective in grouping rice samples from the different locations and identifying unknown samples belonging to these two regions. Further recommendations are presented to develop a better model for tracing the origin of unidentified rice samples.

Biofilm producing plant growth promoting bacteria in combination with glycine betaine uplift drought stress tolerance of maize plant.

Introduction: The escalating threat of drought poses a significant challenge to sustainable food production and human health, as water scarcity adversely impacts various aspects of plant physiology. Maize, a cornerstone in staple cereal crops, faces the formidable challenge of drought stress that triggers a series of transformative responses in the plant. Methods: The present study was carried out in two sets of experiments. In first experiment, drought stress was applied after maintaining growth for 45 days and then irrigation was skipped, and plant samples were collected at 1st, 3rd and 6th day of drought interval for evaluation of changes in plant growth, water relation (relative water content) and antioxidants activity by inoculating indigenously isolated drought tolerant biofilm producing rhizobacterial isolates (Bacillus subtilis SRJ4, Curtobacterium citreum MJ1). In the second experiment, glycine betaine was applied as osmoregulator in addition to drought tolerant PGPR to perceive modulation in photosynthetic pigments (Chlorophyll a and b) and plant growth under varying moisture stress levels (100, 75 and 50% FC). Results and discussion: Results of the study revealed upsurge in root and shoot length, fresh and dry biomass of root and shoot besides increasing chlorophyll contents in water stressed inoculated plants compared to uninoculated plants. Glycine betaine application resulted in an additional boost to plant growth and photosynthetic pigments, when applied in combination with bacterial inoculants. However, both bacterial inoculants behaved differently under drought stress as evident from their biochemical and physiological attributes. Isolate SRJ4 proved to be superior for its potential to express antioxidant activity, leaf water potential and relative water contents and drought responsive gene expression while isolate MJ1 showed exclusive increase in root dry biomass and plant P contents. Though it is quite difficult to isolate the bacterial isolates having both plant growth promoting traits and drought tolerance together yet, such biological resources could be an exceptional option to be applied for improving crop productivity and sustainable agriculture under abiotic stresses. By exploring the combined application of PGPR and glycine betaine, the study seeks to provide insights into potential strategies for developing sustainable agricultural practices aimed at improving crop resilience under challenging environmental conditions.

Visualization and analysis of knowledge domains for recent developments in coal mechanical properties studies.

To better understand the research progress and trends in the mechanical properties of coal, and to promote theoretical research on the prevention and control of dynamic disasters, we employed the bibliometric method to analyze the research progress in this field. A total of 3450 documents from the Web of Science (WOS) core database were reviewed and analyzed. Our analysis focused on the annual distribution of literature, the distribution by country/region, organization, and author, as well as the distribution of significant source journals. We also identified research hotspots and frontiers. The results indicate a significant increase in the number of research papers on the mechanical properties of coal. China, America, Australia, India, Spain, Poland, England, Japan, South Korea, and Turkey were found to be the most active countries in this research area. The research results from China, America, and Australia were found to be the most influential, and C&BM, FUEL, INT J ROCK MECH MIN, INT J COAL GEOL, RM&RE, C&CR, and JCP were identified as the primary sources of research publications on the mechanical properties of coal. The basic theory and research system of coal mechanical properties investigation have been established, and there are numerous future research directions and areas to explore. Some current hotspots include the development of coal mechanical property models, permeability models related to mechanical properties, establishment and prediction of coal strength-temperature relationships, investigation of the proportioning scheme of granite and coal bottom ash in concrete mixes, and research on the improvement effect of fly ash on concrete manufacturing properties.

Renal Mitochondrial ATP Transporter Ablation Ameliorates Obesity-Induced CKD.

SIGNIFICANCE STATEMENT: This study sheds light on the central role of adenine nucleotide translocase 2 (ANT2) in the pathogenesis of obesity-induced CKD. Our data demonstrate that ANT2 depletion in renal proximal tubule cells (RPTCs) leads to a shift in their primary metabolic program from fatty acid oxidation to aerobic glycolysis, resulting in mitochondrial protection, cellular survival, and preservation of renal function. These findings provide new insights into the underlying mechanisms of obesity-induced CKD and have the potential to be translated toward the development of targeted therapeutic strategies for this debilitating condition. BACKGROUND: The impairment in ATP production and transport in RPTCs has been linked to the pathogenesis of obesity-induced CKD. This condition is characterized by kidney dysfunction, inflammation, lipotoxicity, and fibrosis. In this study, we investigated the role of ANT2, which serves as the primary regulator of cellular ATP content in RPTCs, in the development of obesity-induced CKD. METHODS: We generated RPTC-specific ANT2 knockout ( RPTC-ANT2-/- ) mice, which were then subjected to a 24-week high-fat diet-feeding regimen. We conducted comprehensive assessment of renal morphology, function, and metabolic alterations of these mice. In addition, we used large-scale transcriptomics, proteomics, and metabolomics analyses to gain insights into the role of ANT2 in regulating mitochondrial function, RPTC physiology, and overall renal health. RESULTS: Our findings revealed that obese RPTC-ANT2-/- mice displayed preserved renal morphology and function, along with a notable absence of kidney lipotoxicity and fibrosis. The depletion of Ant2 in RPTCs led to a fundamental rewiring of their primary metabolic program. Specifically, these cells shifted from oxidizing fatty acids as their primary energy source to favoring aerobic glycolysis, a phenomenon mediated by the testis-selective Ant4. CONCLUSIONS: We propose a significant role for RPTC-Ant2 in the development of obesity-induced CKD. The nullification of RPTC-Ant2 triggers a cascade of cellular mechanisms, including mitochondrial protection, enhanced RPTC survival, and ultimately the preservation of kidney function. These findings shed new light on the complex metabolic pathways contributing to CKD development and suggest potential therapeutic targets for this condition.