Search results for: N. Srinivas Murthy

Authors: K. V. Ramanaiah, R. Jagan, N. N. Murthy

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Trinuclear oxo-centered carboxylate-bridged iron complexes, [Fe3(µ3-O)(µ2-O2CR)L¬3]+/0 (where R = alkyl or aryl; L = H2O, ROH, Py, solvent) have attracted tremendous attention because of their interesting structural and magnetic properties, exhibit mixed-valent trapped and de-trapped states, and have bioinorganic relevance. The presence of a trinuclear iron binding center has been implicated in the formation of both bacterial and human iron storage protein, Ft. They are used as precursors for the synthesis of models for the active-site structures of non-heme proteins, hemerythrin (Hr), methane monooxygenase (MMO) and polyiron storage protein, ferritin (Ft). Used as important building blocks for the design and synthesis of supramolecules this can exhibit single molecular magnetism (SMM). Such studies have often employed simple and compact carboxylate ligands and the use of bulky carboxylates is scarce. In the present study, we employed two different type of sterically hindered carboxylates and synthesized a series of novel oxo-centered, carboxylate-bridged triiron complexes of general formula [Fe3(O)(O2CCPh3)6L3]X (L = H2O, 1; py, 2; 4-NMe2py, 3; X = ClO4; L = CH3CN, 4; X = FeCl4) and [Fe3(O)(O2C-anth)6L3]X (L = H2O, 5; X = ClO4; L = CH3OH, 6; X = Cl). Along with complex [Fe(OMe)2(O2CCPh3)]10, 7 was prepared by the self-assemble of anhydrous FeCl3, sodium triphenylacetate and sodium methoxide at ratio of 1:1:2 in CH3OH. The Electronic absorption spectra of these complexes 1-6, in CH2Cl2 display weak bands at near FTIR region (970-1135 nm, ε > 15M-1cm-1). For complex 7, one broad band centered at ~670nm and also an additional intense charge transfer (L→M or O→M) bands between 300 to 550nm observed for all the complexes. Paramagnetic 1H NMR is introduced as a good probe for the characterization of trinuclear oxo - cantered iron compounds in solution when the L ligand coordinated to iron varies as: H2O, py, 4-NMe2py, and CH3OH. The solution state magnetic moment values calculated by using Evans method for all the complexes and also solid state magnetic moment value of complex, 7 was calculated by VSM method, which is comparable with solution state value. These all magnetic moment values indicate there is a spin exchange process through oxo and carboxylate bridges in between two irons (d5). The ESI-mass data complement the data obtained from single crystal X-ray structure. Further purity of the compounds was confirmed by elemental analysis. Finally, structural determination of complexes 1, 3, 4, 5, 6 and 7 were unambiguously conformed by single crystal x-ray studies.

Keywords: decanuclear, paramagnetic NMR, trinuclear, uv-visible

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Authors: Srinivas Bathini, Duraichelvan Raju, Simona Badilescu, Muthukumaran Packirisamy

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A bio-sensing method, based on the plasmonic property of gold nano-islands, has been developed for detection of exosomes in a clinical setting. The position of the gold plasmon band in the UV-Visible spectrum depends on the size and shape of gold nanoparticles as well as on the surrounding environment. By adsorbing various chemical entities, or binding them, the gold plasmon band will shift toward longer wavelengths and the shift is proportional to the concentration. Exosomes transport cargoes of molecules and genetic materials to proximal and distal cells. Presently, the standard method for their isolation and quantification from body fluids is by ultracentrifugation, not a practical method to be implemented in a clinical setting. Thus, a versatile and cutting-edge platform is required to selectively detect and isolate exosomes for further analysis at clinical level. The new sensing protocol, instead of antibodies, makes use of a specially synthesized polypeptide (Vn96), to capture and quantify the exosomes from different media, by binding the heat shock proteins from exosomes. The protocol has been established and optimized by using a glass substrate, in order to facilitate the next stage, namely the transfer of the protocol to a microfluidic environment. After each step of the protocol, the UV-Vis spectrum was recorded and the position of gold Localized Surface Plasmon Resonance (LSPR) band was measured. The sensing process was modelled, taking into account the characteristics of the nano-island structure, prepared by thermal convection and annealing. The optimal molar ratios of the most important chemical entities, involved in the detection of exosomes were calculated as well. Indeed, it was found that the results of the sensing process depend on the two major steps: the molar ratios of streptavidin to biotin-PEG-Vn96 and, the final step, the capture of exosomes by the biotin-PEG-Vn96 complex. The microfluidic device designed for sensing of exosomes consists of a glass substrate, sealed by a PDMS layer that contains the channel and a collecting chamber. In the device, the solutions of linker, cross-linker, etc., are pumped over the gold nano-islands and an Ocean Optics spectrometer is used to measure the position of the Au plasmon band at each step of the sensing. The experiments have shown that the shift of the Au LSPR band is proportional to the concentration of exosomes and, thereby, exosomes can be accurately quantified. An important advantage of the method is the ability to discriminate between exosomes having different origins.

Keywords: exosomes, gold nano-islands, microfluidics, plasmonic biosensing

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Authors: S. Srinivas Rao, Pallavi Chitnis, Himanshu Prajapati

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The green building movement in India commenced in 2003. Since then, more than 4,300 projects have adopted green building concepts. For last 15 years, the green building movement has grown strong across the country and has resulted in immense tangible and intangible benefits to the stakeholders. Several success stories have demonstrated the tangible benefit experienced in green buildings. However, extensive data interpretation and qualitative analysis are required to report the intangible benefits in green buildings. The emphasis is now shifting to the concept of people-centric design and productivity, health and wellbeing of occupants are gaining importance. This research was part of World Green Building Council’s initiative on 'Better Places for People' which aims to create a world where buildings support healthier and happier lives. The overarching objective of this study was to understand the perception of users living and working in green buildings. The study was conducted in twenty-five IGBC certified green buildings across India, and a comprehensive questionnaire was designed to capture occupant’s perception and experience in the built environment. The entire research focussed on the eight attributes of healthy buildings. The factors considered for the study include thermal comfort, visual comfort, acoustic comfort, ergonomics, greenery, fitness, green transit and sanitation and hygiene. The occupant’s perception and experience were analysed to understand their satisfaction level. The macro level findings of the study indicate that green buildings have addressed attributes of healthy buildings to a larger extent. Few important findings of the study focussed on the parameters such as visual comfort, fitness, greenery, etc. The study indicated that occupants give tremendous importance to the attributes such as visual comfort, daylight, fitness, greenery, etc. 89% occupants were comfortable with the visual environment, on account of various lighting element incorporated as part of the design. Tremendous importance to fitness related activities is highlighted by the study. 84% occupants had actively utilised sports and meditation facilities provided in their facility. Further, 88% occupants had access to the ample greenery and felt connected to the natural biodiversity. This study aims to focus on the immense advantages gained by users occupying green buildings. This will empower green building movement to achieve new avenues to design and construct healthy buildings. The study will also support towards implementing human-centric measures and in turn, will go a long way in addressing people welfare and wellbeing in the built environment.

Keywords: health and wellbeing, green buildings, Indian green building council, occupant’s satisfaction

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Pulakesh Das, Mukunda Dev Behera, Manchiraju Sri Ramachandra Murthy

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Large numbers of people living in and around the Hindu Kush Himalaya (HKH) region, depends on this diverse mountainous region for ecosystem services. Following the global trend, this region also experiencing rapid population growth, and demand for timber and agriculture land. The eight countries sharing the HKH region have different forest resources utilization and conservation policies that exert varying forces in the forest ecosystem. This created a variable spatial as well altitudinal gradient in rate of deforestation and corresponding forest patch fragmentation. The quantitative relationship between fragmentation and demography has not been established before for HKH vis-à-vis along elevation gradient. This current study was carried out to attribute the overall and different nature in landscape fragmentations along the altitudinal gradient with the demography of each sharing countries. We have used the tree canopy cover data derived from Landsat data to analyze the deforestation and afforestation rate, and corresponding landscape fragmentation observed during 2000 – 2010. Area-weighted mean radius of gyration (AMN radius of gyration) was computed owing to its advantage as spatial indicator of fragmentation over non-spatial fragmentation indices. Using the subtraction method, the change in fragmentation was computed during 2000 – 2010. Using the tree canopy cover data as a surrogate of forest cover, highest forest loss was observed in Myanmar followed by China, India, Bangladesh, Nepal, Pakistan, Bhutan, and Afghanistan. However, the sequence of fragmentation was different after the maximum fragmentation observed in Myanmar followed by India, China, Bangladesh, and Bhutan; whereas increase in fragmentation was seen following the sequence of as Nepal, Pakistan, and Afghanistan. Using SRTM-derived DEM, we observed higher rate of fragmentation up to 2400m that corroborated with high human population for the year 2000 and 2010. To derive the nature of fragmentation along the altitudinal gradients, the Statistica software was used, where the user defined function was utilized for regression applying the Gauss-Newton estimation method with 50 iterations. We observed overall logarithmic decrease in fragmentation change (area-weighted mean radius of gyration), forest cover loss and population growth during 2000-2010 along the elevation gradient with very high R2 values (i.e., 0.889, 0.895, 0.944 respectively). The observed negative logarithmic function with the major contribution in the initial elevation gradients suggest to gap filling afforestation in the lower altitudes to enhance the forest patch connectivity. Our finding on the pattern of forest fragmentation and human population across the elevation gradient in HKH region will have policy level implication for different nations and would help in characterizing hotspots of change. Availability of free satellite derived data products on forest cover and DEM, grid-data on demography, and utility of geospatial tools helped in quick evaluation of the forest fragmentation vis-a-vis human impact pattern along the elevation gradient in HKH.

Keywords: area-weighted mean radius of gyration, fragmentation, human impact, tree canopy cover

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Authors: Megan L. Srinivas

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Individuals with underlying rheumatologic diseases such as dermatomyositis may not adequately respond to tuberculin (PPD) skin tests, creating false negative results. These illnesses are frequently treated with immunosuppressive therapy making proper identification of TB infection imperative. A 59-year-old Filipino man was diagnosed with dermatomyositis on the basis of rash, electromyography, and muscle biopsy. He was initially treated with IVIG infusions and transitioned to oral prednisone and mycophenolate. The patient’s symptoms improved on this regimen. Six months after starting mycophenolate, the patient began having fevers, night sweats, and productive cough without hemoptysis. He moved from the Philippines 5 years prior to dermatomyositis diagnosis, denied sick contacts, and was PPD negative both at immigration and immediately prior to starting mycophenolate treatment. A third PPD was negative following the onset of these new symptoms. He was treated for community-acquired pneumonia, but symptoms worsened over 10 days and he developed watery diarrhea and a growing non-tender, non-mobile mass on the left side of his neck. A chest x-ray demonstrated a cavitary lesion in right upper lobe suspicious for TB that had not been present one month earlier. Chest CT corroborated this finding also exhibiting necrotic hilar and paratracheal lymphadenopathy. Neck CT demonstrated the left-sided mass as cervical chain lymphadenopathy. Expectorated sputum and stool samples contained acid-fast bacilli (AFB), cultures showing TB bacteria. Fine-needle biopsy of the neck mass (scrofula) also exhibited AFB. An MRI brain showed nodular enhancement suspected to be a tuberculoma. Mycophenolate was discontinued and dermatomyositis treatment was switched to oral prednisone with a 3-day course of IVIG. The patient’s infection showed sensitivity to standard RIPE (rifampin, isoniazid, pyrazinamide, and ethambutol) treatment. Within a week of starting RIPE, the patient’s diarrhea subsided, scrofula diminished, and symptoms significantly improved. By the end of treatment week 3, the patient’s sputum no longer contained AFB; he was removed from isolation, and was discharged to continue RIPE at home. He was discharged on oral prednisone, which effectively addressed his dermatomyositis. This case illustrates the unreliability of PPD tests in patients with long-term inflammatory diseases such as dermatomyositis. Other immunosuppressive therapies (adalimumab, etanercept, and infliximab) have been affiliated with conversion of latent TB to disseminated TB. Mycophenolate is another immunosuppressive agent with similar mechanistic properties. Thus, it is imperative that patients with long-term inflammatory diseases and high-risk TB factors initiating immunosuppressive therapy receive a TB blood test (such as a quantiferon gold assay) prior to the initiation of therapy to ensure that latent TB is unmasked before it can evolve into a disseminated form of the disease.

Keywords: dermatomyositis, immunosuppressant medications, mycophenolate, disseminated tuberculosis

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Authors: Shilpa Murthy, Hazlina Binti Abu Bakar, Juliet Mathew, Chandrashekhar Thummala Hlly Sreerama Reddy, Pathiyil Ravi Shankar

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Simulation is defined as a technique that replaces or expands real experiences with guided experiences that interactively imitate real-world processes or systems. Simulation enables learners to train in a safe and non-threatening environment. For decades, simulation has been considered an integral part of clinical teaching and learning strategy in medical education. The several types of simulation used in medical education and the clinical environment can be applied to several models, including full-body mannequins, task trainers, standardized simulated patients, virtual or computer-generated simulation, or Hybrid simulation that can be used to facilitate learning. Simulation allows healthcare practitioners to acquire skills and experience while taking care of patient safety. The recent COVID pandemic has also led to an increase in simulation use, as there were limitations on medical student placements in hospitals and clinics. The learning is tailored according to the educational needs of students to make the learning experience more valuable. Simulation in the pre-clinical years has challenges with resource constraints, effective curricular integration, student engagement and motivation, and evidence of educational impact, to mention a few. As instructors, we may have more reliance on the use of simulation for pre-clinical students while the students’ confidence levels and perceived competence are to be evaluated. Our research question was whether the implementation of simulation-based learning positively influences preclinical medical students' confidence levels and perceived competence. This study was done to align the teaching activities with the student’s learning experience to introduce more low-fidelity simulation-based teaching sessions for pre-clinical years and to obtain students’ input into the curriculum development as part of inclusivity. The study was carried out at International Medical University, involving pre-clinical year (Medical) students who were started with low-fidelity simulation-based medical education from their first semester and were gradually introduced to medium fidelity, too. The Student Satisfaction and Self-Confidence in Learning Scale questionnaire from the National League of Nursing was employed to collect the responses. The internal consistency reliability for the survey items was tested with Cronbach’s alpha using an Excel file. IBM SPSS for Windows version 28.0 was used to analyze the data. Spearman’s rank correlation was used to analyze the correlation between students’ satisfaction and self-confidence in learning. The significance level was set at p value less than 0.05. The results from this study have prompted the researchers to undertake a larger-scale evaluation, which is currently underway. The current results show that 70% of students agreed that the teaching methods used in the simulation were helpful and effective. The sessions are dependent on the learning materials that are provided and how the facilitators engage the students and make the session more enjoyable. The feedback provided inputs on the following areas to focus on while designing simulations for pre-clinical students. There are quality learning materials, an interactive environment, motivating content, skills and knowledge of the facilitator, and effective feedback.

Keywords: low-fidelity simulation, pre-clinical simulation, students satisfaction, self-confidence

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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