Role of Higher Education Institutions in Environmental Conservation and Sustainable Development: A case study of Shivaji University, Maharashtra, India.

The ever increasing population and changing lifestyles are making the environmental problems more critical. Higher educational institutions can be the best solution to solve this situation. Higher education can play a crucial role in sustainable development of any nation. As environmental sustainability is becoming an increasingly important issue for the world, the role of higher educational institutions in relation to environmental sustainability is more prevalent. Universities are the apex bodies in higher education system and can provide environmental education through its curricular design, research and collaborative efforts with NGO’s working in those areas. They can provide trained manpower and knowledgeable expertise to solve critical environmental problems. They can also act as a good networking system and data collector. Shivaji University is one of the significant higher education institution located in heart of Western Ghats working with the same goal of environmental sustainability through various activities. The paper examines the efforts taken by higher education in environmental development in the areas of creating healthy environment and conservation of resources. Key words: Role of Higher education, Environmental protection, Universities, sustainable developmen

Screening and Isolation of Polypropylene Degrading Fungi from Waste Dumping Site, Kolhapur, India

Polypropylene (PP) and other plastic wastes are found to accumulate in the environment, creating significant ecological issues. They are determined to be considered non-biodegradable, It has been established that once it enters the environment, it stays there permanently. The present investigation aims to biodegrade PP without physical treatment and exposing it to UV light and sunlight exposed to potential fungi isolated from the soil of solid waste dumping site based on 18SrRNA analysis and the isolated strains were identified as 98.54% similar to Cladosporium sp. The fungal strain was submitted with Gene Bank accession number ON024632 and registered as a Cladosporium halotolerans strain SUK PRAKASH. The degradation was performed for 8 months of incubation in the aqueous medium. The biodegradation of polypropylene FTIR spectroscopy was performed to further examine the sheets, and the results indicated that perhaps the bonds between the sheets were weakening and breaking. The biodegraded samples of without treated PP sheets, UV-exposed PP sheets, and sunlight-exposed PP sheets exhibit weight loss of 4.2%, 6.1%, and 8.6% respectively

ISOLATION AND CHARACTERIZATION OF BIOSURFACTANT PRODUCING BACTERIA FROM GARAGE SOIL

The increasing demand for eco-friendly and sustainable solutions has led to a growing interest in bio-surfactants due to their biodegradability and low toxicity. This study focuses on the isolation and characterization of bio-surfactant producing bacteria from garage soil. Soil samples were collected from various garages and screened for bio-surfactant production using haemolytic activity and oil displacement tests. Positive isolates were further characterized through biochemical assays. The results revealed the presence of several potent bio-surfactant producing strains, predominantly belonging to the Staphylococcus. These strains exhibited significant emulsification activity and surface tension reduction, highlighting their potential applications in bioremediation and industrial processes. This study underscores the importance of garage soil as a reservoir of bio-surfactant producing bacteria and provides a foundation for future research on their industrial exploitation

Comprehensive in Vitro Analysis of Masticatory Muscle Activity During Orthodontic Treatment with Clear Aligners: A Detailed Electromyographic (EMG) Simulation Study

Background: Clear aligners are a popular orthodontic treatment option due to their aesthetic appeal and comfort. However, the impact of clear aligners on masticatory muscle activity remains underexplored. Understanding these effects is crucial for optimizing treatment outcomes and ensuring patient comfort. Objective: This in vitro study aimed to evaluate masticatory muscle activity during orthodontic treatment with clear aligners using advanced electromyographic (EMG) simulation techniques. Materials and Methods: Thirty artificial mandibular models, produced using 3D printing from biocompatible resin, were used. These models were mounted on a mechanical setup to simulate masticatory functions. Clear aligners were fitted according to standardized protocols. Surface EMG electrodes were placed on the masseter, temporalis, and lateral pterygoid muscles. EMG data were recorded before and after aligner application and after 2 weeks of simulated treatment. Data were analyzed using root mean square (RMS) values and statistical tests, including paired t-tests and ANOVA. Machine learning models, such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), were employed for pattern recognition. Results: Significant increases in muscle activity were observed for the masseter (21.7%), temporalis (22.6%), and lateral pterygoid (16.1%) muscles following aligner application (p < 0.05). Custom aligners showed higher RMS values compared to standard aligners. CNN achieved 95.8% accuracy, and SVM achieved 92.3% accuracy in predicting muscle activity changes. ANOVA confirmed significant effects of aligner types on muscle activity. Conclusion: The study highlights significant changes in masticatory muscle activity associated with clear aligners. Advanced EMG techniques and machine learning models provided detailed insights, underscoring the need to consider muscle activity in orthodontic treatment planning

In Vitro Simulation of Airway Volume Changes Following Rapid Maxillary Expansion: A 3D CBCT and Machine Learning Analysis

Background: Rapid Maxillary Expansion (RME) is a prevalent orthodontic procedure designed to widen the maxillary arch to enhance occlusion and airway function. While prior research has shown varied effects of RME on airway volume, a comprehensive analysis utilizing advanced imaging and analytical methods remains limited. This study aims to address this gap by employing 3D Cone-Beam Computed Tomography (CBCT) and machine learning algorithms to thoroughly assess airway volume changes following RME. Objective: To evaluate the impact of RME on airway volume through detailed in vitro simulations using 3D CBCT imaging and to apply machine learning techniques for an in-depth analysis of these changes. Methods: This study involved 30 pre-treatment and post-treatment CBCT scans of patients who underwent RME. The scans were processed using DentAnalyser (Version 3.2) for volumetric analysis and machine learning models, including Convolutional Neural Networks (CNN) and AIForecast (Version 2.1), were employed to predict airway volume changes. Statistical analysis was performed using paired t-tests and analysis of variance (ANOVA) to determine the significance of the changes observed. Results: The average airway volume increased significantly from 15.3 cm³ (± 2.5 cm³) before RME to 18.7 cm³ (± 2.7 cm³) after RME, reflecting a mean increase of 22.3% (p < 0.001). Machine learning models exhibited high predictive accuracy, with CNN achieving 95.8% and AIForecast achieving 92.3%. These findings were consistent across different patient demographics and treatment conditions. Conclusion: The study confirms that RME significantly enhances airway volume, as shown by 3D CBCT imaging and machine learning analysis. The use of advanced analytical techniques provides a reliable method for assessing airway changes and offers valuable insights into the clinical benefits of RME. These results underscore the effectiveness of RME in improving airway dimensions, with implications for optimizing orthodontic treatment planning and patient management

Artificial Intelligence and 3D Imaging in Orthodontics: Predictive Analysis of Soft Tissue Changes and Treatment Outcomes

Background: The integration of artificial intelligence (AI) and three-dimensional (3D) imaging technologies has significantly advanced orthodontic diagnostics and treatment planning. This study evaluates the predictive accuracy of AI models in forecasting soft tissue changes and treatment outcomes using 3D imaging data, aiming to enhance treatment precision. Methods: An in vitro experimental study was conducted using 3D-printed orthodontic models based on anonymized cone-beam computed tomography (CBCT) scans. The study involved the fabrication of orthodontic appliances and the application of simulated orthodontic forces using a Tensometer 5000 Universal Testing Machine.  AI algorithms, including deep learning models, were trained on pre-treatment 3D images and treatment plans to predict post-treatment soft tissue outcomes. The predictive models accounted for tooth movement, facial growth, and soft tissue response. The study, conducted from January 3, 2024, to May 17, 2024, involved generating high-resolution orthodontic models, applying simulated orthodontic Results: AI models demonstrated high predictive accuracy, with the DeepConvNet model achieving a mean absolute error (MAE) of 0.42 mm and a root mean square error (RMSE) of 0.53 mm. The correlation coefficient between predicted and actual post-treatment outcomes indicated a strong positive relationship. Soft tissue changes averaged 0.30 mm across key facial regions. The Activator appliance resulted in the highest mean change of 0.35 mm, while force application showed a linear relationship with displacement, where higher forces produced greater tissue movement. The Force Sensor Pro exhibited superior accuracy and precision compared to the Tensometer 5000. Conclusions: The study highlights the potential of AI and 3D imaging technologies to improve the prediction of soft tissue changes and treatment outcomes in orthodontics. The DeepConvNet model provided the most accurate predictions, and the Activator appliance showed the greatest efficacy in inducing soft tissue changes. These findings suggest that AI-driven predictive models and advanced imaging can lead to more precise and individualized orthodontic treatments, enhancing patient satisfaction and clinical outcomes. Further research with larger datasets and clinical trials is recommended to validate and refine these models

A unique influenza A (H5N1) virus causing a focal poultry outbreak in 2007 in Manipur, India

<p>Abstract</p> <p>Background</p> <p>A focal H5N1 outbreak in poultry was reported from Manipur, a north-eastern state, of India, in 2007. The aim of this study was to genetically characterize the Manipur isolate to understand the relationship with other H5N1 isolates and to trace the possible source of introduction of the virus into the country.</p> <p>Results</p> <p>Characterization of the complete genome revealed that the virus belonged to clade 2.2. It was distinctly different from viruses of the three EMA sublineages of clade 2.2 but related to isolates from wild migratory waterfowl from Russia, China and Mongolia. The HA gene, had the cleavage site GERRRRKR, earlier reported in whooper swan isolates from Mongolia in 2005. A stop codon at position 29 in the PB1-F2 protein could have implications on the replication efficiency. The acquisition of polymorphisms as seen in recent isolates of 2005–07 from distinct geographical regions suggests the possibility of transportation of H5N1 viruses through migratory birds.</p> <p>Conclusion</p> <p>Considering that all eight genes of the earlier Indian isolates belonged to the EMA3 sublineage and similar strains have not been reported from neighbouring countries of the subcontinent, it appears that the virus may have been introduced independently.</p

Synergistic Impact of Sonophotocatalytic Degradation of Acephate Over Ag@CeO2 Nanocomposite Catalysts

Noble metal decorated metal oxide composites have proved to have Surface plasmon resonance (SPR) as a notable approach for efficient light absorption. Herein present work, a new sonochemical method is proposed for in-situ synthesis of noble metal-based CeO2 composites for the sonophotocatalytic degradation of commercial Acephate solution. Pristine CeO2 and Ag@CeO2 with different Ag contents viz. 4, 6 and 8 wt. % were successfully synthesized by a facile in-situ sonochemical approach. The as-synthesized CeO2 and Ag@CeO2 nanocomposites were characterized by various physicochemical characterization techniques, including XRD, FTIR, UV-Vis spectroscopy, BET, and FESEM-EDS. Further, these CeO2 and Ag@CeO2 nanocomposites were employed for photocatalytic, sonocatalytic, and sonophotocatalytic degradation of commercial Acephate solution. Experimental results revealed that the photocatalytic and sonocatalytic processes follow a pseudo-first-order model, whereas the sonophotocatalytic process had a more substantial rate constant compared to the photocatalytic and sonocatalytic one. Further, the kinetics of the study were examined by the Langmuir-Hinshelwood model. Overall, the sonophotocatalytic degradation involving as-synthesized Ag@CeO2 with 6 wt. % Ag content has shown to be the most effective method for the effective degradation of a commercial acephate solution

An avian influenza A(H11N1) virus from a wild aquatic bird revealing a unique Eurasian-American genetic reassortment

Influenza surveillance in different wild bird populations is critical for understanding the persistence, transmission and evolution of these viruses. Avian influenza (AI) surveillance was undertaken in wild migratory and resident birds during the period 2007–2008, in view of the outbreaks of highly pathogenic AI (HPAI) H5N1 in poultry in India since 2006. In this study, we present the whole genome sequence data along with the genetic and virological characterization of an Influenza A(H11N1) virus isolated from wild aquatic bird for the first time from India. The virus was low pathogenicity and phylogenetic analysis revealed that it was distinct from reported H11N1 viruses. The hemagglutinin (HA) gene showed maximum similarity with A/semipalmatedsandpiper/Delaware/2109/2000 (H11N6) and A/shorebird/Delaware/236/2003(H11N9) while the neuraminidase (NA) gene showed maximum similarity with A/duck/Mongolia/540/2001(H1N1). The virus thus possessed an HA gene of the American lineage. The NA and other six genes were of the Eurasian lineage and showed closer relatedness to non-H11 viruses. Such a genetic reassortment is unique and interesting, though the pathways leading to its emergence and its future persistence in the avian reservoir is yet to be fully established

Treatment of persistent organic pollutants in wastewater using hydrodynamic cavitation in synergy with advanced oxidation process

Persistent organic pollutants (POPs) are very tenacious wastewater contaminants. The consequences of their existence have been acknowledged for negatively affecting the ecosystem with specific impact upon endocrine disruption and hormonal diseases in humans. Their recalcitrance and circumvention of nearly all the known wastewater treatment procedures are also well documented. The reported successes of POPs treatment using various advanced technologies are not without setbacks such as low degradation efficiency, generation of toxic intermediates, massive sludge production, and high energy expenditure and operational cost. However, advanced oxidation processes (AOPs) have recently recorded successes in the treatment of POPs in wastewater. AOPs are technologies which involve the generation of OH radicals for the purpose of oxidising recalcitrant organic contaminants to their inert end products. This review provides information on the existence of POPs and their effects on humans. Besides, the merits and demerits of various advanced treatment technologies as well as the synergistic efficiency of combined AOPs in the treatment of wastewater containing POPs was reported. A concise review of recently published studies on successful treatment of POPs in wastewater using hydrodynamic cavitation technology in combination with other advanced oxidation processes is presented with the highlight of direction for future research focus