Characterization of fungal microbiome structure in leaf litter compost through metagenomic profiling for harnessing the bio-organic fertilizer potential.

Sustainable waste management through composting has gain renewed attention since it could upcycle organic waste into valuable bio-organic fertiliser. This study explored the composition of fungal communities in leaf litter and organic waste composts ecosystems by employing advanced internal transcribed spacer (ITS) metagenomic profiling. This approach provides insights into the diversity, composition, and potential functions of these fungi, offering practical implications for optimising composting processes and enhancing sustainable waste management practices. Various organic composts were collected, including leaf litter composts, from different sources in Delhi-National Capital Region, India, and fungal microbiome composition were characterised through ITS profiling. Results revealed that leaf litter composts and cow dung manure had the highest fungal read counts, while kitchen waste compost had the lowest. Alpha diversity indices, including Chao1 and Shannon, exhibited differences in species richness and diversity among composts, though statistical significance was limited. The leaf composts had relatively higher alpha diversity than the other organic waste composts analysed. The study also identified dominant fungal genera specifically, Wallemia, Geotrichum, Pichia, Mycothermus, Mortierella, Aspergillus, Fusarium, and Basidiobolus, across the compost samples. The presence of beneficial fungal genera like Pichia, Geotrichum, Trichoderma, Mortierella, Basidiobolus, Aspergillus, and others were detected in leaf waste compost and the other organic waste composts. There was also presence of some pathogenic genera viz. Alternaria, Fusarium, and Acremonium, in these composts which underscored the need for proper composting practices and source selection to optimise soil fertility and minimise disease risks in agriculture. Remarkably, leaf compost has highest proportion of beneficial genera with least observed abundance of pathogens. On the other hand, the municipal organic waste compost has least proportion of beneficial genera with higher abundance of pathogens. Overall, these findings contributed to characterisation of composting processes, advancing waste management practices, and enhancing the use of leaf compost as a bio-organic fertiliser. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-04028-0.

Health Risk Assessment for Potentially Toxic Elements Accumulation in Amaranthaceae Family Cultivars and their Correlation with Antioxidants and Antinutrients.

Delhi's agricultural hub, nestled along the Yamuna floodplains, faces soil and water contamination issues. Utilizing organic waste composts is gaining traction to improve soil quality, but uncertainties remain about their efficacy in reducing harmful elements. The study examined three Amaranthaceae cultivars, comparing organic waste composts with chemical fertilizer to evaluate correlations between heavy metals, antioxidants, and antinutrients to assess their bioremediation potential. "Heavy metals" or "potentially toxic elements (PTE)" levels in soil and leaves were measured by ICP-MS, while antioxidants and antinutrients were analyzed with UV-VIS spectroscopy. The study revealed higher PTE levels in floodplain soil, with Cr, Ni, and Cd exceeding safe limits in all cultivars. Compost amendments reduced these pollutants by 28% compared to chemical fertilizers, decreasing bioaccumulation by 20%. Health risk assessments showed lower risks in compost-amended cultivars. Additionally, compost amendment displayed a stronger negative correlation between PTE and antioxidants, suggesting effective bioremediation. Overall, compost amendments offer promise for mitigating PTE in metropolitan floodplains.

16S rRNA metagenomic profiling of red amaranth grown organically with different composts and soils.

In recent years organic food is gaining popularity as it is believed to promote better human health and improve soil sustainability, but there are apprehensions about pathogens in organic produces. This study was designed to understand the effect of different composts and soils on the status of the microbiome present in organically grown leafy vegetables. 16S rRNA metagenomic profiling of the leaves was done, and data were analyzed. It was found that by adding composts, the OTU of the microbiome in the organic produce was higher than in the conventional produce. The beneficial genera identified across the samples included plant growth promoters (Achromobacter, Paenibacillus, Pseudomonas, Sphingobacterium) and probiotics (Lactobacillus), which were higher in the organic produce. Some pathogenic genera, viz., plant pathogenic bacteria (Cellvibrio, Georgenia) and human pathogenic bacteria (Corynebacterium, Acinetobacter, Streptococcus, Streptomyces) were also found but with relatively low counts in the organic produce. Thus, the present study highlights that organic produce has lesser pathogen contamination than the conventional produce. KEY POINTS: • 16S rRNA metagenomics profiling done for organic red amaranth cultivar • Microbial richness varied with respect to the soil and compost type used • The ratio of beneficial to pathogenic genera improves with the addition of compost.

Formation and characterization of leaf waste into organic compost.

In solid waste management, pollution-free disposal of leaf waste in urban areas is still not standardized and adopted. According to the World Bank report, 57% of wastes generated in South East Asia are consisted of food and green waste, which can be recycled into valuable bio-compost. The present study shows a method of leaf litter waste management by composting it using essential microbe (EM) method. Different parameters, such as pH, electrical conductivity, macronutrients, micronutrients, and potentially toxic elements (PTE) were measured at zero to 50 days of composting using appropriate methods. The microbial composting was shown to mature within 20 to 40 days, and its maturity could be evaluated by the attainment of stable pH (8), electrical conductivity (0.9 mS/cm), and C:N ratio ≥ 20. The analysis was also performed on other bio-composts viz. kitchen waste compost, vermicompost, cow dung manure, municipal organic waste compost, and neem cake compost. The fertility index (FI) was evaluated based on six parameters viz. total carbon, total nitrogen, N ratio, phosphorus, potassium, and sulphur contents. The PTE values were used to calculate their clean index (CI). The results showed that leaf waste compost has a higher fertility index (FI = 4.06) than other bio-composts, except the neem cake compost (FI = 4.44). The clean index of the leaf waste compost (CI = 4.38) was also higher than other bio-composts. This indicates that leaf waste compost is a valuable bio-resource with high nutritive value and low PTE contamination, with a favourable prospective to be used in organic farming.

DNA numerical encoding schemes for exon prediction: a recent history.

Bioinformatics in the present day has been firmly established as a regulator in genomics. In recent times, applications of Signal processing in exon prediction have gained a lot of attention. The exons carry protein information. Proteins are composed of connected constituents known as amino acids that characterize the specific function. Conversion of the nucleotide character string into a numerical sequence is the gateway before analyzing it through signal processing methods. This numeric encoding is the mathematical descriptor of nucleotides and is based on some statistical properties of the structure of nucleic acids. Since the type of encoding extremely affects the exon detection accuracy, this paper is devised for the review of existing encoding (mapping) schemes. The comparative analysis is formulated to emphasize the importance of the genetic code setting of amino acids considered for application related to computational elucidation for exon detection. This work covers much helpful information for future applications.

Heat stress induced oxidative damage and perturbation in BDNF/ERK1/2/CREB axis in hippocampus impairs spatial memory.

Heat exposure is an environmental stress that causes diverse heat related pathophysiological changes under extreme conditions. The brain including hippocampal region which is associated with learning and memory is significantly affected by heat stress resulting in memory impairment. However, the effect of heat on the spatial memory remains unclear. The present study aimed to explore the effect of heat stress on hippocampus and spatial memory in rats. Rat model of acute heat stress was used which was divided into two groups, viz. moderate heat stress (MHS) and severe heat stress (SHS). Redox parameters evaluation revealed that MHS and SHS exposure markedly increase the production of malondialdehyde (MDA), oxidised glutathione (GSSG), reactive oxidative species (ROS), protein oxidation level and decrease the reduced glutathione (GSH) levels in the hippocampal tissue. Furthermore, Cresyl Violet (CV) staining of hippocampal region showed higher pyknosis in rats exposed to SHS. Pronounced increase of caspase3 expression and Fluoro Jade-C (FJ-C) positive cells were observed in SHS resulting in neuronal injury and apoptosis in CA3 region of hippocampus culminating in spatial memory deficit. Our data also suggest that heat stress induces phospho Extracellular signal-regulated kinases (pERK)1/2 activation induced by Brain-derived neurotrophic factor (BDNF) leading to further activation of phospho cAMP-response element binding protein (pCREB) under MHS. However, during SHS, BDNF and pCREB expression were completely dysregulated and not sufficient to rescue cognitive decline in rats. In conclusion, SHS induces pathological alterations that include oxidative damage and apoptosis of hippocampal neurons, disturbing BDNF/ERK1/2/CREB axis that may affect spatial memory.

Detection of exon location in eukaryotic DNA using a fuzzy adaptive Gabor wavelet transform.

The existing model-independent methods for the detection of exons in DNA could not prove to be ideal as commonly employed fixed window length strategy produces spectral leakage causing signal noise The Modified-Gabor-wavelet-transform exploits a multiscale strategy to deal with the issue to some extent. Yet, no rule regarding the occurrence of small and large exons has been specified. To overcome this randomness, scaling-factor of GWT has been adapted based on a fuzzy rule. Due to the nucleotides' genetic code and fuzzy behaviors in DNA configuration, this work could adopt the fuzzy approach. Two fuzzy membership functions (large and small) take care of the variation in the coding regions. The fuzzy-based learning parameter adaptively tunes the scale factor for fast and precise prediction of exons. The proposed approach has an immense plus point of being capable of isolating detailed sub-regions in each exon efficiently proving its efficacy comparing with existing techniques.

Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach.

Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As diseased genes are more random than the healthy ones, in this work, a comparison of the diseased gene is made against the healthy ones. An adaptive signal processing method like functional link artificial neural network-based Levenberg-Marquardt filter has been proposed in this regard. For parameter upgradation, the algorithm is modified using particle swarm optimization. Here, disease genes are discriminated from healthy ones based on the magnitude of mean square error (MSE), which is calculated through the adaptive filter. The performance of the algorithm is inspected by computing some evaluation parameters. Since accuracy is the prime concern, authors in this work have taken an attempt to improve the accuracy level compared to the existing methods. Taking the reference gene as healthy, the overall process is accomplished by categorizing the diseased and healthy targets with MSE value at a threshold of 0.012. The proposed technique predicts the test gene sets successfully.

An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window.

Identification of exon location in a DNA sequence has been considered as the most demanding and challenging research topic in the field of Bioinformatics. This work proposes a robust approach combining the Trigonometric mapping with Adaptive tuned Kaiser Windowing approach for locating the protein coding regions (EXONS) in a genetic sequence. For better convergence as well as improved accurateness, the side lobe height control parameter (ß) of Kaiser Window in the proposed algorithm is made adaptive to track the changing dynamics of the genetic sequence. This yields better tracking potential of the anticipated Adaptive Kaiser algorithm as it uses the recursive Gauss Newton tuning which in turn utilizes the covariance of the error signal to tune the ß factor which has been shown through numerous simulation results under a variety of practical test conditions. A detailed comparative analysis with the existing mapping schemes, windowing techniques, and other signal processing methods like SVD, AN, DFT, STDFT, WT, and ST has also been included in the paper to focus on the strength and efficiency of the proposed approach. Moreover, some critical performance parameters have been computed using the proposed approach to investigate the effectiveness and robustness of the algorithm. In addition to this, the proposed approach has also been successfully applied on a number of benchmark gene sets like Musmusculus, Homosapiens, and C. elegans, etc., where the proposed approach revealed efficient prediction of exon location in contrast to the other existing mapping methods.

Heat stress-induced neuroinflammation and aberration in monoamine levels in hypothalamus are associated with temperature dysregulation.

Heat Stress (HS) induces diverse pathophysiological changes, which include brain ischemia, oxidative stress and neuronal damage. The present study was undertaken with the objective to ascertain whether neuroinflammation in Hypothalamus (HTH) caused under HS affects monoamine levels and hence, its physiological role in thermoregulation. Rats were exposed to HS in a heat simulation environmental chamber (Ambient temperature, Ta=45±0.5°C and Relative Humidity, RH=30±10%) with real-time measurement of core temperature (Tc) and skin temperature (Ts). Animals were divided into two subgroups: Moderate HS (MHS) (Tc=40°C) and Severe HS (SHS)/Heat stroke (Tc=42°C). Rats with MHS showed an increase in Mean Arterial Pressure (MAP) and Heart Rate (HR) while fall in MAP and rise in HR was observed in rats with SHS. In addition, oxidative stress and an increase in pyknotic neurons were observed in HTH. High levels of Adrenocorticotropic-hormone (ACTH), Epinephrine (EPI), Norepinephrine (NE) and Dopamine (DA) in the systemic circulation and progressive increase in EPI and DA levels in HTH were recorded after the thermal insult. Moreover, a substantial increase in Glutamate (Glu) level was observed in HTH as well as in systemic circulation of heat stroke rats. We found a rise in NE whereas a fall in Serotonin (5-HT) level in HTH at MHS, without perturbing inflammatory mediators. However, rats with SHS exhibited significant elevations in NF-kB, IL-1ß, COX2, GFAP and Iba1 protein expression in HTH. In conclusion, the data suggest that SHS induces neuroinflammation in HTH, which is associated with monoamines and Glu imbalances, leading to thermoregulatory disruption.

Thermostable alpha-amylase conjugated antibodies as probes for immunodetection in ELISA.

Thermostable alpha-amylase from B. licheniformis has been conjugated with high efficiency to goat antibodies against human, mouse, and rabbit immunoglobulins to prepare second-step reagents which can be used in Enzyme Linked Immunosorbent Assays (ELISA). Various conjugation methods, such as one- and two-step glutaraldehyde coupling and cross-linking, using heterobifunctional reagents such as sulfosuccinimidyl-4-(N-maleimidomethyl)-cyclohexane-1-carbonate (sulfo-SMCC) and N-succinimidyl-S-acetylthioacetate (SATA), yielded active alpha-amylase labeled second antibodies. Such conjugates had molecular sizes ranging between 200-300 kDa. Filter sterilized solutions of conjugates, when stored at 37 degrees C for two weeks, retained 32% of their biological activity and were thermostable even after keeping for 1 h at 90 degrees C.