A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Carbon nanotubes in plant dynamics: Unravelling multifaceted roles and phytotoxic implications.

Carbon nanotubes (CNTs) have emerged as a promising frontier in plant science owing to their unique physicochemical properties and versatile applications. CNTs enhance stress tolerance by improving water dynamics and nutrient uptake and activating defence mechanisms against abiotic and biotic stresses. They can be taken up by roots and translocated within the plant, impacting water retention, nutrient assimilation, and photosynthesis. CNTs have shown promise in modulating plant-microbe interactions, influencing symbiotic relationships and mitigating the detrimental effects of phytopathogens. CNTs have demonstrated the ability to modulate gene expression in plants, offering a powerful tool for targeted genetic modifications. The integration of CNTs as sensing elements in plants has opened new avenues for real-time monitoring of environmental conditions and early detection of stress-induced changes. In the realm of agrochemicals, CNTs have been explored for their potential as carriers for targeted delivery of nutrients, pesticides, and other bioactive compounds. CNTs have the potential to demonstrate phytotoxic effects, detrimentally influencing both the growth and developmental processes of plants. Phytotoxicity is characterized by induction of oxidative stress, impairment of cellular integrity, disruption of photosynthetic processes, perturbation of nutrient homeostasis, and alterations in gene expression. This review aims to provide a comprehensive overview of the current state of knowledge regarding the multifaceted roles of CNTs in plant physiology, emphasizing their potential applications and addressing the existing challenges in translating this knowledge into sustainable agricultural practices.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

Development and evaluation of a pH-responsive Mimosa pudica seed mucilage/ß- cyclodextrin-co-poly(methacrylate) hydrogel for controlled drug delivery: In vitro and in vivo assessment.

In this comprehensive investigation, a novel pH-responsive hydrogel system comprising mimosa seed mucilage (MSM), ß-cyclodextrin (ß-CD), and methacrylic acid (MAA) was developed via free radical polymerization technique to promote controlled drug delivery. The hydrogel synthesis involved strategic variations in polymer, monomer, and crosslinker content in fine-tuning its drug-release properties. The resultant hydrogel exhibited remarkable pH sensitivity, selectively liberating the model drug (Capecitabine = CAP) under basic conditions while significantly reducing release in an acidic environment. Morphological, thermal, and structural analyses proved that CAP has a porous texture, high stability, and an amorphous nature. In vitro drug release experiments showcased a sustained and controlled release profile. Optimum release (85.33 %) results were recorded over 24 h at pH 7.4 in the case of MMB9. Pharmacokinetic evaluation in healthy male rabbits confirmed bioavailability enhancement and sustained release capabilities. Furthermore, rigorous toxicity evaluations and histopathological analyses ensured the safety and biocompatibility of the hydrogel. This pH-triggered drug delivery system can be a promising carrier system for drugs involving frequent administrations.

Advancing Diabetes Management: The Future of Enzyme-Less Nanoparticle-Based Glucose Sensors-A Review.

BACKGROUND: Glucose is vital for biological processes, requiring blood sugar levels to be maintained between 3.88 and 6.1 mmol/L, especially during fasting. Elevated levels signal diabetes, a global concern affecting 537 million people, necessitating effective glucose-monitoring devices. METHOD: Enzyme-based sensors, though selective, are sensitive to environmental factors. Nonenzymatic sensors, especially those with nanoparticles, offer stability, high surface area, and cost-effectiveness. Existing literature supports their immediate glucose oxidation, showcasing exceptional sensitivity. RESULTS: This review details nonenzymatic sensors, highlighting materials, detection limits, and the promise of nanoparticle-based designs, which exhibit enhanced sensitivity and selectivity in glucose detection. CONCLUSION: Nanoparticle-based sensors, as reviewed, show potential for glucose monitoring, overcoming enzyme-based limitations. The conclusion suggests future directions for advancing these sensors, emphasizing ongoing innovation in this critical research area.

Chylolymphatic mesenteric cyst: A rare differential diagnosis of cystic mass in abdomen: A case report.

INTRODUCTION: Mesenteric cysts are the rare benign intraperitoneal tumor, which are common in the mesentery of the terminal ileum. Though mesenteric cysts are frequently seen, chylolymphatic mesenteric cysts in children are rare entities. CASE PRESENTATION: Our case is a case of a 3-year-old female child presenting to our center with a complain of abdominal pain. Ultrasonography (USG) of the abdomen revealed a cystic lesion in the right upper abdomen, and computed tomography (CT) scan showed a large homogenous fluid density lesion in the right upper abdomen, giving a provisional diagnosis of mesenteric cyst. She was managed surgically: the cyst was excised, the involved segment of bowel was resected and the remaining was anastomosed. On histopathology, the final diagnosis of a chylolymphatic mesenteric cyst was made. DISCUSSION: Clinical presentations may vary from asymptomatic abdominal lump to features of acute abdomen. Imaging modalities like ultrasonography and computed tomography scans are the mainstay of investigations. Surgical management with excision of cyst, and resection of the involved bowel segment is the treatment of choice, as done in our patient. The final diagnosis is made after the histopathologic examination of the excised specimen. CONCLUSION: Chylolymphatic mesenteric cysts are rare, and have varied clinical presentations. Although rare, chylolymphatic mesenteric cysts should be considered a differential diagnosis in patients presenting with cystic masses in the abdomen.

Unraveling the impact of digital transformation on green innovation through microdata and machine learning.

How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the "IT productivity paradox." Exploring the influence of DT on green innovation, we analyze panel data encompassing A-share listed companies in Shanghai and Shenzhen spanning the period from 2010 to 2018. It tests the DT's non-linear impact, employing a random-forest and mediation effect models. The results reveal that (i) DT can promote green innovation; (ii) regarding heterogeneity, the promotion effect is mainly manifested in enterprises in non-state-owned and highly competitive industries; (iii) based on mechanism testing, DT relies on two routes to encourage green innovation: improving environmental information disclosure and reducing environmental uncertainty; and (iv) random-forest analysis shows that DT exhibits an inverted U-shaped non-linear effect on green innovation, including the "IT productivity paradox." This study enhances the existing discourse on DT and green innovation by furnishing empirical substantiation for the non-linear influence exerted by DT on green innovation. Furthermore, it imparts insights into the mechanisms and contextual limitations governing this association.

Comprehensive Investigation of Cu2+ Adsorption from Wastewater Using Olive-Waste-Derived Adsorbents: Experimental and Molecular Insights.

This study investigates the efficacy of adsorbents from locally sourced olive waste-encompassing olive skins, leaves, and pits, recovered from the initial centrifugation of olives (OWP)-and a composite with sodium alginate (OWPSA) for the removal of Cu2+ ions from synthetic wastewater. Experimental analyses conducted at room temperature, with an initial Cu2+ concentration of 50 mg/L and a solid/liquid ratio of 1 g/L, showed that the removal efficiencies were approximately 79.54% and 94.54% for OWP and OWPSA, respectively, highlighting the positive impact of alginate on adsorption capacity. Utilizing statistical physics isotherm models, particularly the single-layer model coupled to real gas (SLMRG), allowed us to robustly fit the experimental data, providing insights into the adsorption mechanisms. Thermodynamic parameters affirmed the spontaneity and endothermic nature of the processes. Adsorption kinetics were interpreted effectively using the pseudo-second-order (PSO) model. Molecular modeling investigations, including the conductor-like screening model for real solvents (COSMO-RS), density functional theory (DFT), and atom-in-molecule (AIM) analysis, unveiled intricate molecular interactions among the adsorbent components-cellulose, hemicellulose, lignin, and alginate-and the pollutant Cu2+, confirming their physically interactive nature. These findings emphasize the synergistic application of experimental and theoretical approaches, providing a comprehensive understanding of copper adsorption dynamics at the molecular level. This methodology holds promise for unraveling intricate processes across various adsorbent materials in wastewater treatment applications.

Bentonite SDBS-loaded composite for methylene blue removal from wastewater: An experimental and theoretical investigation.

This study addresses the urgent need for practical solutions to industrial water contamination. Utilizing Algerian Bentonite as an adsorbent due to its regional prevalence, we focused on the efficiency of the Bentonite/Sodium dodecylbenzene sulfonate (SDBS) matrix in Methylene Blue (MB) removal. The zero-charge point and IR spectroscopy characterized the adsorbent. Acidic pH facilitated SDBS adsorption on Bentonite, achieving equilibrium in 30 min with a pseudo-second-order model. The UPAC and Freundlich model indicated a qmax of 25.97 mg/g. SDBS adsorption was exothermic at elevated temperatures. The loaded Bentonite exhibited excellent MB adsorption (pH 3-9) with PSOM kinetics. Maximum adsorption capacity using IUPAC and GILES-recommended isotherms was qmax = 23.54 mg/g. The loaded Bentonite's specific surface area was 70.01 m2/g, and the Sips model correlated well with experimental data (R2 = 0.98). This study highlights adsorption, mainly Bentonite/SDBS matrices, as a promising approach for remediating polluted areas by efficiently capturing and removing surfactants and dyes, contributing valuable insights to address industrial water contamination challenges.

Chitosan-based intelligent polymeric networks for site-specific colon medication delivery: A comprehensive study on controlled release of diloxanide furoate and network formation dynamics.

Oral medications are prone to gastric degradation and enzymatic inactivation, diminishing their efficacy. This study investigates a solution by developing intelligent polymeric networks, incorporating chitosan, methacrylic acid, N, N, methylene bisacrylamide, and montmorillonite clay, to enable the controlled release of Diloxanide Furoate (DF), an anti-protozoal drug. Employing a swelling-assisted diffusion technique, drug loading percentages varied from 63.96 % to 76.82 % among different formulations. Increased chitosan and methacrylic acid content enhanced drug loading, while N, N, methylene bisacrylamide and montmorillonite clay demonstrated an inverse relationship affecting diffusion and swelling. Equilibrium swelling studies unveiled formulation-dependent behaviors, with chitosan reducing swelling and methacrylic acid promoting it. Higher N, N, methylene bisacrylamide concentrations decreased swelling, indicating a denser cross-linked structure, while montmorillonite clay reduced hydrophilicity and swelling capacity. Further analyses confirmed successful gel formation, particularly in formulations with higher chitosan, methacrylic acid, and N, N, methylene bisacrylamide content, while montmorillonite clay limited gel fraction due to restricted polymer chain mobility. Techniques such as Fourier transform infrared spectroscopy, Differential scanning calorimetry, and thermal gravimetric analyses supported network development, enhancing thermal stability and cross-linking density. This research underscores the flexibility of polymeric networks for precise drug delivery, offering potential advancements in targeted therapies for various medical conditions.

A review on fluoride contamination in groundwater and human health implications and its remediation: A sustainable approaches.

Contamination of drinking water due to fluoride (F-) is a major concern worldwide. Although fluoride is an essential trace element required for humans, it has severe human health implications if levels exceed 1.5 mg. L-1 in groundwater. Several treatment technologies have been adopted to remove fluoride and reduce the exposure risk. The present article highlights the source, geochemistry, spatial distribution, and health implications of high fluoride in groundwater. Also, it discusses the underlying mechanisms and controlling factors of fluoride contamination. The problem of fluoride-contaminated water is more severe in India's arid and semiarid regions than in other Asian countries. Treatment technologies like adsorption, ion exchange, precipitation, electrolysis, electrocoagulation, nanofiltration, coagulation-precipitation, and bioremediation have been summarized along with case studies to look for suitable technology for fluoride exposure reduction. Although present technologies are efficient enough to remove fluoride, they have specific limitations regarding cost, labour intensity, and regeneration requirements.

Microbial synthesis of titanium dioxide nanoparticles and their importance in wastewater treatment and antimicrobial activities: a review.

Nanotechnology (NT) and nanoparticles (NPs) have left a huge impact on every field of science today, but they have shown tremendous importance in the fields of cosmetics and environmental cleanup. NPs with photocatalytic effects have shown positive responses in wastewater treatment, cosmetics, and the biomedical field. The chemically synthesized TiO2 nanoparticles (TiO2 NPs) utilize hazardous chemicals to obtain the desired-shaped TiO2. So, microbial-based synthesis of TiO2 NPs has gained popularity due to its eco-friendly nature, biocompatibility, etc. Being NPs, TiO2 NPs have a high surface area-to-volume ratio in addition to their photocatalytic degradation nature. In the present review, the authors have emphasized the microbial (algae, bacterial, fungi, and virus-mediated) synthesis of TiO2 NPs. Furthermore, authors have exhibited the importance of TiO2 NPs in the food sector, automobile, aerospace, medical, and environmental cleanup.

Deeper insight into ferroptosis: association with Alzheimer's, Parkinson's disease, and brain tumors and their possible treatment by nanomaterials induced ferroptosis.

Ferroptosis is an emerging and novel type of iron-dependent programmed cell death which is mainly caused by the excessive deposition of free intracellular iron in the brain cells. This deposited free iron exerts a ferroptosis pathway, resulting in lipid peroxidation (LiPr). There are mainly three ferroptosis pathways viz. iron metabolism-mediated cysteine/glutamate, and LiPr-mediated. Iron is required by the brain as a redox metal for several physiological activities. Due to the iron homeostasis balance disruption, the brain gets adversely affected which further causes neurodegenerative diseases (NDDs) like Parkinson's and Alzheimer's disease, strokes, and brain tumors like glioblastoma (GBS), and glioma. Nanotechnology has played an important role in the prevention and treatment of these NDDs. A synergistic effect of nanomaterials and ferroptosis could prove to be an effective and efficient approach in the field of nanomedicine. In the current review, the authors have highlighted all the latest research in the field of ferroptosis, specifically emphasizing on the role of major molecular key players and various mechanisms involved in the ferroptosis pathway. Moreover, here the authors have also addressed the correlation of ferroptosis with the pathophysiology of NDDs and theragnostic effect of ferroptosis and nanomaterials for the prevention and treatment of NDDs.

Identification of the Contamination Sources by PCBs Using Multivariate Analyses: The Case Study of the Annaba Bay (Algeria) Basin.

Persistent Organic Pollutants (POPs), particularly the indicator polychlorinated biphenyls (PCBs), were first quantified in water and sediments of two wadis, Boujemaâ and Seybouse, as well as in the effluents from a fertilizer and phytosanitary production industrial plant (Fertial). Since these contaminated discharges end in Annaba Bay (Algeria) in the Mediterranean Sea, with a significant level of contamination, all the potential sources should be identified. In this work, this task is conducted by a multivariate analysis. Liquid-liquid extraction and gas chromatography/mass spectrometry (GC-MS) methods were applied to quantify seven PCB congeners, usually taken as indicators of contamination. The sum of the PCB concentrations in the sediments ranged from 1 to 6.4 µg/kg dw (dry weight) and up to 0.027 µg/L in waters. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for the multivariate analysis, indicating that the main sources of PCB emissions in the bay are urban/domestic and agricultural/industrial. The outfalls that mostly contribute to the pollution of the gulf are the Boujemaâ wadi, followed by the Seybouse wadi, and finally by the Fertial cluster and more precisely the annex basin of the plant. Although referring to a specific site of local importance, the work aims to present a procedure and a methodological analysis that can be potentially applicable to further case studies all over the world.

Use of biogenic silver nanoparticles on the cathode to improve bioelectricity production in microbial fuel cells.

To date, research on microbial fuel cells (MFCs) has. focused on the production of cost-effective, high-performance electrodes and catalysts. The present study focuses on the synthesis of silver nanoparticles (AgNPs) by Pseudomonas sp. and evaluates their role as an oxygen reduction reaction (ORR) catalyst in an MFC. Biogenic AgNPs were synthesized from Pseudomonas aeruginosa via facile hydrothermal synthesis. The physiochemical characterization of the biogenic AgNPs was conducted via scanning electron microscopy (SEM), X-ray diffraction (XRD), and UV-visible spectrum analysis. SEM micrographs showed a spherical cluster of AgNPs of 20-100 nm in size. The oxygen reduction reaction (ORR) ability of the biogenic AgNPs was studied using cyclic voltammetry (CV). The oxygen reduction peaks were observed at 0.43 V, 0.42 V, 0.410 V, and 0.39 V. Different concentrations of biogenic AgNPs (0.25-1.0 mg/cm2) were used as ORR catalysts at the cathode in the MFC. A steady increase in the power production was observed with increasing concentrations of biogenic AgNPs. Biogenic AgNPs loaded with 1.0 mg/cm2 exhibited the highest power density (PDmax) of 4.70 W/m3, which was approximately 26.30% higher than the PDmax of the sample loaded with 0.25 mg/cm2. The highest COD removal and Coulombic efficiency (CE) were also observed in biogenic AgNPs loaded with 1.0 mg/cm2 (83.8% and 11.7%, respectively). However, the opposite trend was observed in the internal resistance of the MFC. The lowest internal resistance was observed in a 1.0 mg/cm2 loading (87 Ω), which is attributed to the high oxygen reduction kinetics at the surface of the cathode by the biogenic AgNPs. The results of this study conclude that biogenic AgNPs are a cost-effective, high-performance ORR catalyst in MFCs.

Delayed presentation of traumatic diaphragmatic hernia complicated by bowel obstruction and perforation: a case report.

Post-traumatic diaphragmatic hernia is a rare but life-threatening condition resulting from a traumatic injury to the diaphragm. We present a case of a 48-year-old man with a history of fall injury and a delayed presentation of a right-sided diaphragmatic hernia with subsequent bowel obstruction and perforation in a patient with a history of trauma and tuberculosis. The patient underwent exploratory laparotomy with manual reduction of the herniated bowel, primary repair of the diaphragmatic defect, and the creation of a double-barrel ileostomy. This case highlights the importance of considering traumatic diaphragmatic hernia in patients with a history of trauma presenting with abdominal symptoms. Timely diagnosis and surgical intervention are crucial in preventing serious complications associated with this condition.

Insight into the environmental fate, hazard, detection, and sustainable degradation technologies of chlorpyrifos-an organophosphorus pesticide.

Pesticides play a critical role in terms of agricultural output nowadays. On top of that, pesticides provide economic support to our farmers. However, the usage of pesticides has created a public health issue and environmental hazard. Chlorpyrifos (CPY), an organophosphate pesticide, is extensively applied as an insecticide, acaricide, and termiticide against pests in various applications. Environmental pollution has occurred because of the widespread usage of CPY, harming several ecosystems, including soil, sediment, water, air, and biogeochemical cycles. While residual levels in soil, water, vegetables, foodstuffs, and human fluids have been discovered, CPY has also been found in the sediment, soil, and water. The irrefutable pieces of evidence indicate that CPY exposure inhibits the choline esterase enzyme, which impairs the ability of the body to use choline. As a result, neurological, immunological, and psychological consequences are seen in people and the natural environment. Several research studies have been conducted worldwide to identify and develop CPY remediation approaches and its derivatives from the environment. Currently, many detoxification methods are available for pesticides, such as CPY. However, recent research has shown that the breakdown of CPY using bacteria is the most proficient, cost-effective, and sustainable. This current article aims to outline relevant research events, summarize the possible breakdown of CPY into various compounds, and discuss analytical summaries of current research findings on bacterial degradation of CPY and the potential degradation mechanism.

Bacillus subtilis ER-08, a multifunctional plant growth-promoting rhizobacterium, promotes the growth of fenugreek (Trigonella foenum-graecum L.) plants under salt and drought stress.

Introduction: Sustainable agriculture and meeting the world's food needs face considerable obstacles from abiotic stresses such as soil salinity and drought. This critical issue was addressed by our current study, which sought to uncover multi-trait bioinoculants from hostile ecosystems that could help mitigate salinity and drought stresses at the same time. Methods: The Bacillus subtilis ER-08 (BST) strain was isolated from the halotolerant plant Fagonia cretica which was collected from the Little Rann of Kachchh, India. Various biochemical and molecular approaches were applied for the detailed characterization of the BST isolate. Results and discussion: The BST isolate demonstrated notable plant growth-promoting qualities. Fenugreek seed biopriming was performed using the BST isolate. The effect of BST seed treatment on fenugreek developmental indices as well as abiotic alleviation was examined under greenhouse conditions. The BST produced 83.7 g ml-1 gibberellins (GA3) and 176.1 g ml-1 indole-3 acetic acid. Moreover, hydrogen cyanide, siderophore, exopolysaccharides (EPS), ammonia, cellulase, protease, pectinase, and chitinase were also produced by the BST strain. Interestingly, 52% of Fusarium oxysporum mycelial growth was suppressed by the BST isolate under in vitro conditions. Furthermore, BST isolates functioned well under several abiotic stress conditions, for instance, salinity (4 and 6 ds m-1), pH (5, 7, and 9), drought (PEG6000 at 10%, 20%, and 30%), and temperature (25°C, 35°C, 37°C, and 55°C). This study indicates that the BST strain might serve as an effective bio-inoculant for minimizing the detrimental effects of abiotic stresses.

A comprehensive appraisal of mechanism of anti-CRISPR proteins: an advanced genome editor to amend the CRISPR gene editing.

The development of precise and controlled CRISPR-Cas tools has been made possible by the discovery of protein inhibitors of CRISPR-Cas systems, called anti-CRISPRs (Acrs). The Acr protein has the ability to control off-targeted mutations and impede Cas protein-editing operations. Acr can help with selective breeding, which could help plants and animals improve their valuable features. In this review, the Acr protein-based inhibitory mechanisms that have been adopted by several Acrs, such as (a) the interruption of CRISPR-Cas complex assembly, (b) interference with target DNA binding, (c) blocking of target DNA/RNA cleavage, and (d) enzymatic modification or degradation of signalling molecules, were discussed. In addition, this review emphasizes the applications of Acr proteins in the plant research.

Seasonal influence on physicochemical properties of the sediments from Bay of Bengal coast with statistical approach.

The current study aims to investigate the influence of seasonal changes on the pollution loads of the sediment of a coastal area in terms of its physicochemical features. The research will focus on analyzing the nutrients, organic carbon and particle size of the sediment samples collected from 12 different sampling stations in 3 different seasons along the coastal area. Additionally, the study discusses about the impact of anthropogenic activities such as agriculture and urbanization and natural activities such as monsoon on the sediment quality of the coastal area. The nutrient changes in the sediment were found to be: pH (7.96-9.45), EC (2.89-5.23 dS/m), nitrogen (23.98-57.23 mg/kg), phosphorus (7.75-11.36 mg/kg), potassium (217-398 mg/kg), overall organic carbon (0.35-0.99%), and sediment proportions (8.91-9.3%). Several statistical methods were used to investigate changes in sediment quality. According to the three-way ANOVA test, the mean value of the sediments differs significantly with each season. It correlates significantly with principal factor analysis and cluster analysis across seasons, implying contamination from both natural and man-made sources. This study will contribute to developing effective management strategies for the protection and restoration of degraded coastal ecosystem.