Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

A novel deep learning identifier for promoters and their strength using heterogeneous features.

Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.

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.

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 .

Chronic Alcohol Intake Compromises Lung Immunity by Altering Immunometabolism in Humans and Mouse Models.

Chronic alcohol consumption disrupts lung immunity and host defense mechanisms, rendering individuals with alcohol use disorder more susceptible to developing inflammatory lung conditions with poor prognoses. Here, we focused on investigating the molecular and cellular effects of alcohol ingestion on lung immunity in male and female subjects using population-based human lung transcriptomics analysis and an experimental mouse model of chronic alcohol drinking using the NIAAA alcohol feeding model. Flow cytometry and transcriptomics analyses in lungs revealed a sexually dimorphic effect of chronic alcohol drinking on lung immunity of both human and mouse. The male lungs were more sensitive to chronic alcohol drinking-induced dysregulation of lung immunity compared to the females. Furthermore, comparative transcriptomics analysis using lungs and liver samples from matched human and mouse subjects exhibited that lungs were more sensitive than the liver to the effects of alcohol in down-regulating immune-related genes and pathways. Furthermore, the transcriptomics analysis provided evidence that immunometabolic change is a central driver in lung alteration by downregulating the immune pathways and upregulating metabolic pathways. Chronic alcohol consumption resulted in reduced mTOR signaling and decreased immune cell populations. mTOR signaling axis may serve as an upstream regulator of alcohol-induced dysregulation in lung immunity.

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 .

The proximate composition of vegetables enriched by incorporation of municipal solid waste into fertilizers and its impacts on environment and human health.

The recent over production of municipal solid waste (MSW) poses a significant threat to both the ecosystem and human health. Utilizing MSW for agricultural purposes has emerged as a promising strategy to reduce solid waste disposal while simultaneously increasing soil fertility. To explore this potential solution further, an experiment was designed to assess the impact of varying concentrations of MSW (25%, 50%, and 75%) on the proximate composition of 15 different vegetable species. The experiment, conducted between 2018 and 2019, involved treating soil with different levels of solid waste and analyzing the proximate components, such as crude protein, dry matter, crude fiber, crude fat, and moisture content, in the 15 selected crops. The results indicate that the application of 25% MSW significantly increased the levels of crude protein, crude fiber, dry matter, and fat in Spinacia oleracea, Solanum tuberosum, Solanum melongena, and Abelmoschus esculentus. Conversely, the addition of 75% MSW notably elevated the moisture and ash content in Cucumis sativus. Correlation and scatter matrix analyses were conducted to elucidate the relationships between the protein, fiber, dry matter, ash, and fat contents. Principal component analysis and clustering confirmed the substantial impact of Treatment_1 (25% MSW) and Treatment_3 (75% MSW) on the proximate composition of the aforementioned vegetables, leading to their categorization into distinct groups. Our study highlights the efficacy of using 25% MSW to enhance the proximate composition and nutritional value of vegetables. Nonetheless, further research is warranted to investigate the mineral, antioxidant, vitamin, and heavy metal contents in the soil over an extended period of MSW application.

Multivalent Sulfur Vacancy-Rich NiCo2S4@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 NiCo2S4@MnO2 heterostructures are efficaciously fabricated via a facile hydrothermal approach followed by heat treatment. The urchin-like Sv-NiCo2S4@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-NiCo2S4 and MnO2. Density functional theory (DFT) simulation further verifies that Co-sites of urchin-like Sv-NiCo2S4@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.

Molecular characterization, genetic divergence, expression of encapsidiation and synergism by a bipartite begomovirus; Tomato leaf curl Palampur virus (ToLCPMV) infecting bitter gourd (Momordica charantia).

The Tomato leaf curl Palampur virus (ToLCPMV) is a bipartite begomovirus that poses a substantial risk to agriculture by infecting a variety of crops, including cucurbitaceous group. This study examines the manifestation of encapsidation and synergism by ToLCPMV in bitter gourd (Momordica charantia) and focuses on its epidemiological approaches and implications of managing this virus in tomatoes growing areas. Through the utilization of molecular and biological techniques, we have successfully ascertained the epidemiology of this highly destructive virus, highlighting the vital roles played by its two genetic components. An analysis was conducted to identify the mechanism by which the virus clusters its DNA into virions, known as the encapsidation process. Additionally, the impact of synergism with other viral or environmental factors over the degree of infection was examined. The evolutionary rate differences among sites were modeled deploying a discrete Gamma distribution with 5 categories and a [+G] parameter. The results of this study provide important and unique information about synergism, encapsidiation and host-virus interactions. Sequencing study revealed that the bipartite ToLCPMV is linked to the occurrence of leaf curl disease in bitter gourd. The DNA-A and DNA-B of the ToLCPMV isolates infecting bitter gourd (SP1-4) showed 89 %, 93 %, 95 %, and 98 % similarity respectively. Mean evolutionary rates in these categories were 0.19, 0.47, 0.79, 1.24, 2.31 substitutions per site. Unexpectedly, the DNA-A sequences of ToLCPMV that infect this particular host seemed to be an amalgamation of sequences that are closely associated with tomato leaf curl New Delhi virus (ToLCNDV). Additionally, reiterate cropping of tomatoes with vegetables expanded the virus's host geographic region. This understanding will create some specific ways to regulate the dissemination of ToLCPMV and minimize its adverse impacts in tomato growing regions. Through the implementation of these strategies, the ability of crops to withstand and recover from adverse conditions can be enhanced, so encouraging the adoption of sustainable farming practices in affected regions.

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 interaction (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 multilayer perceptron 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.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism.

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

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.

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.

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.

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.

Dam inundation reduces ecosystem multifunctionality following riparian afforestation in the Three Gorges Reservoir Region.

Afforestation is an acknowledged method for rehabilitating deteriorated riparian ecosystems, presenting multiple functions to alleviate the repercussions of river damming and climate change. However, how ecosystem multifunctionality (EMF) responds to inundation in riparian afforestation ecosystems remains relatively unexplored. Thus, this article aimed to disclose how EMF alters with varying inundation intensities and to elucidate the key drivers of this variation based on riparian reforestation experiments in the Three Gorges Reservoir Region in China. Our EMF analysis encompassed wood production, carbon storage, nutrient cycling, decomposition, and water regulation under different inundation intensities. We examined their correlation with soil properties and microbial diversity. The results indicated a substantial reduction in EMF with heightened inundation intensity, which was primarily due to the decline in most individual functions. Notably, soil bacterial diversity (23.02%), soil properties such as oxidation-reduction potential (ORP, 11.75%), and temperature (5.85%) emerged as pivotal variables elucidating EMF changes under varying inundation intensities. Soil bacterial diversity and ORP declined as inundation intensified but were positively associated with EMF. In contrast, soil temperature rose with increased inundation intensity and exhibited a negative correlation with EMF. Further insights gleaned from structural equation modeling revealed that inundation reduced EMF directly and indirectly by reducing soil ORP and bacterial diversity and increasing soil temperature. This work underscores the adverse effects of dam inundation on riparian EMF and the crucial role soil characteristics and microbial diversity play in mediating EMF in response to inundation. These insights are pivotal for the conservation of biodiversity and functioning following afforestation in dam-induced riparian habitats.

Can urea-coated fertilizers be an effective means of reducing greenhouse gas emissions and improving crop productivity?

Given the significance of nitrogen (N) as the most constraining nutrient in agro-ecosystems, it is crucial to develop an updated model for N fertilizers management to achieve higher crop yields while minimizing the negative impacts on the environment. Coated urea is touted as one of the most important controlled-release N fertilizers used in agriculture to reduce cropland emissions and improve nitrogen use efficiency (NUE) for optimal crop yields. The sustainability of coated urea depends on the trade-offs between crop productivity, NUE and greenhouse gas emissions (CO2, CH4 and N2O); however, role of various agro-edaphic factors in influencing these trade-offs remains unclear. To determine the effects of soil properties, climatic conditions, experimental conditions, and type of coated urea on greenhouse gas emissions, NH3 losses, crop productivity, and NUE, we conducted a meta-analysis using data from 76 peer-reviewed studies. Our results showed that the application of coated urea under field conditions contributed to a greater reduction in N2O emissions (-48.67%) and higher NUE (58.72%), but crop yields were not significant. Across different climate regions, subtropical monsoon climate showed a perceptible mitigation for CO2, CH4 and NH3 (-78.38%; -83.33%; -27.46%), while temperate climate reduced N2O emissions by -70.36%. For different crops, only rice demonstrated reduction in CO2, CH4, N2O and NH3 losses. On the other hand, our findings revealed a mitigating trade-off between CO2 and CH4 emissions on medium-textured soils and N2O emissions on fine-textured soils. A significant reduction in N2O and NH3 losses was evident when coated urea was applied to soils with a pH > 5.5. Interestingly, application of coated urea to soils with higher C/N ratios increased NH3 losses but showed a noticeable N2O reduction. We found that polymer-coated urea reduced CH4 and N2O emissions and NH3 losses at the expense of higher CO2 emissions. Moreover, application of a lower dose of coated urea (0-100 kg N ha-1) enhanced CO2 and CH4 mitigation, while N2O mitigation increased linearly with increasing dose of coated urea. Most importantly, our results showed that the application of coated urea leads to a large mismatch between NUE, crop yields and greenhouse gas mitigation. By and large, the application of coated urea did not correspond with higher crop yields despite significant reduction in the emissions and improved NUE. Overall, these results suggest that site-specific agro-edaphic conditions should be considered when applying coated urea to reduce these emissions and N volatilization losses for increasing NUE and crop yields.

Influence of probiotic strains on growth performance, carcass characteristics and seasonal changes of growing New Zealand white rabbits.

This experiment was designed to explore how different types of probiotics affect the growth, carcass traits, and seasonal variations in growing New Zealand White rabbits (NZW). Two parallel experiments using the same strain of NZW during winter and summer, each alone from 5 to 13 weeks of age. Each experiment uses a total of 125 unsexed rabbits. These rabbits are separated into 5 groups of 25 rabbits each. Each group has five replicates, with five rabbits in every replicate. In each experiment, 1st group acting as the control group did not receive any probiotics. The 2nd was given a dose of 1 ml of Bifidobacterium bifidum, the 3rd received a dose of 1 ml of Lactobacillus acidophilus, and the 4th was treated with a 1 ml blend of both Bifidobacterium bifidum and Lactobacillus acidophilus, and 5th group was treated with 1 ml of Saccharomyces cerevisiae. Results indicated that the Bifidobacterium bifidum group had the best live body weight (LBW) values and daily weight gain (DWG). Meanwhile, during summer, the Lactobacillus acidophilus group had the best feed conversion ratio (FCR) and performance index (PI) values. Also, growing rabbits fed Lactobacillus acidophilus and Bifidobacterium bifiduim had significantly increased carcass traits during the summer and winter seasons. Furthermore, seasonal changes indicated that the Bifidobacterium bifiduim group improved LBW, DWG, and PI during summer than winter. So, it could be concluded that using Bifidobacterium bifidum can enhance rabbit growth by improving feed utilization and carcass traits, making it an effective addition to hot weather diets.

In vivo evaluation of medicinal effects of Myristica fragrans, Cinnamomum zeylanicum, Mucuna pruriens on male murine fertility.

Herbal remedies are used for managing different ailments including male sexual abnormalities. Mucuna pruriens, Cinnamomum zeylanicum and Myristica fragrans, are some of the important herbs of these remedies for male sexual disorders. This study has been conducted to evaluate the effects of these drugs, individually and in combination on fertility parameters in mice. The study was carried out on male and female albino mice of BALB/c strain bearing weight of 20-25 g and age 12 to 13 weeks. Animals were divided into control and test batches (n=10). Drugs were given to the male mice test groups daily for 52 days by oral route and on 53rd day the fertility parameters were measured. Afterwards, histopathological analysis was also done. One-way analysis of variance (ANOVA) followed by post hoc was applied for statistical analysis. Important contrast was found in fertility parameters, including pregnancy outcome, serum testosterone, luteinizing hormone, follicle stimulating hormone and histological examination of tested batches as compared to control. The fertility enhancing effect of the drugs were found in the tested doses used in this study in male albino mice of BALB/c strain. However further preclinical and clinical studies are necessary to determine the safety of these drugs.