Comparative Analysis of Thresholding Algorithms for Microarray-derived Gene Correlation Matrices

The thresholding problem is important in today’s data-rich research scenario. A threshold is a well-defined point in the data distribution beyond which the data is highly likely to have scientific meaning. The selection of threshold is crucial since it heavily influences any downstream analysis and inferences made there from. A legitimate threshold is one that is not arbitrary but scientifically well grounded, data-dependent and best segregates the information-rich and noisy sections of data. Although the thresholding problem is not restricted to any particular field of study, little research has been done. This study investigates the problem in context of network-based analysis of transcriptomic data. Six conceptually diverse algorithms – based on number of maximal cliques, correlations of control spots with genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values and statistical power – are used to threshold the gene correlation matrices of three time-series microarray datasets and tested for stability and validity. Stability or reliability of the first four algorithms towards thresholding is tested upon block bootstrapping of arrays in the datasets and comparing the estimated thresholds against the bootstrap threshold distributions. Validity of thresholding algorithms is tested by comparison of the estimated thresholds against threshold based on biological information. Thresholds based on the modular basis of gene networks are concluded to perform better both in terms of stability as well as validity. Future challenges to research the problem have been identified. Although the study utilizes transcriptomic data for analysis, we assert its applicability to thresholding across various fields

Antimicrobial activity with special reference to antimycobacterial activity of the coral, Junceella delicata (Grasshoff,1999) collected from Madh island, West coast of Mumbai, India

The world now needs new antimicrobial drugs because many infections are resistant to existing treatments. Many microbial infections pose a significant global health challenge and a critical need for new, effective treatment. Soft corals are being explored for their potential as sources of new antimicrobial drugs. The present study aimed to determine the antimicrobial, antifungal, and antimycobacterial properties of the crude extract of coral Junceella delicata, collected from Madh island, Mumbai. The crude extract was prepared by adding an equal volume of methanol: dichloromethane (1:1) for 24 hours in the water bath at 45 ⁰C. The sample was filtered through Whatman filter paper No. 1 and was subjected to concentrate in a rotary vacuum evaporator at 45⁰ C.  Further, antimycobacterial drugs viz. Isoniazid, Ethambutol, Pyrazinamide, Rifampicin, and Streptomycin were used to compare the activity with crude extract of the coral. It was observed that Coral J. delicata extract showed a zone of inhibition against all the bacterial strains viz., Klebsiella pneumonia (15 mm), Escherichia coli (15 mm), Salmonella typhi (14 mm), Pseudomonas aeruginosa (09 mm), Sarcina lutea (15 mm), Streptococcus pyogenes (12 mm), Corynebacterium diphtheria (27 mm), Staphylococcus aureus (28 mm), whereas antifungal activity was observed in Penicillium sp.(10 mm), Candida albicans (14 mm) and no activity was observed in Aspergillus sp. The Mycobacterium tuberculosis (MTB) strain showed sensitivity at (25µg/ml of coral extract, nearly close to the standard antituberculosis drug pyrazinamide (3.2µg/ml). The above results indicated that the crude extract of J. delicata had antibacterial activity nearly against the standard antituberculosis drug Pyrazinamide.The study suggests the crude extract of coral J. delicata can be used as an antimicrobial agent to cure different diseases, including tuberculosis.

WIRELESS REAL TIME PROPORTIONAL CONTROL SYSTEM

This system provides low power consuming and low cost wireless sensor network. This system provides a real time temperature and humidity. It also gives proportional control action. This system consists of TI’s MSP430 microcontroller which consumes ultra low power and improves the overall system performance. The Sensorion’s SHT 11 sensor is used to measure temperature and humidity. Sensor SHT 11 consumes low power and gives the fully calibrated digital output. Zigbee technology is used for wireless communication. Zigbee is low power consuming transceiver module. It operates within the ISM 2.4 GHz frequency band. AT and API command modes configure module parameters. RF data rate is 250 kbps. To achieve the proportional control triac and MOC 3022 are used. The star network topology is implemented. The temperature of earth goes on increasing due to global warming, deforestation, pollution, etc. Due to this the temperature of atmosphere also increases which is harmful and dangerous for many systems. This system provides precise control of temperature and humidity in Green House, Art Galleries and Industries

Flood Risk and Recovery: Significance of Measures and Lenses. Case Studies from India

Flood risk and recovery are not static concepts; they vary depending on the lens and measures employed, with significant implications for aid distribution and disaster management decisions. While extensive research exists on understanding and measuring flood risk and recovery, this dissertation addresses three under-explored areas that are crucial for a more comprehensive understanding: 1) it moves beyond physical recovery towards a multidimensional assessment of disaster impacts and recovery; 2) it highlights the competing narratives of flood risk by different stakeholders; and 3) it elevates the perspective of small urban centers. The dissertation employs a mixed-methods approach, combining quantitative and qualitative methods across three chapters:Chapter 2 uses a nationwide panel dataset (n= 23,748) and difference-in-difference approach to examine the economic well-being impacts of flooding. The results reveal that flood-affected households experience significant adverse effects on total income, consumption expenditure, and assets compared to their unaffected counterparts. Furthermore, households below the poverty line and those from scheduled castes/scheduled tribes in flood-affected villages experienced more severe impacts on income and assets than their counterparts, highlighting the heterogeneity in impacts. Chapter 3 focuses on disaster recovery through a case study of the 2021 flooding in Chiplun, India. It conducts ordinal logistic regressions based on a primary survey of affected households (n = 389) to assess differences in recovery of property vs. economic well-being. Findings reveal that recovery of housing and household goods are lacking two years after the event. Marginalized caste households were more likely to report lower recovery levels across all dimensions, highlighting the caste-disparate impacts beyond the immediate aftermath. Additionally, income loss emerged as a critical factor, with households experiencing higher income loss likely to report greater reductions in consumption expenditure and lower recovery levels across multiple dimensions. Chapter 4 assesses how flood risk is defined and understood in policy making through qualitative narrative analysis of government documents, semi-structured interviews, news media, and letters written to state and elected representatives. It exposes competing narratives of flood risk, revealing a predominant focus on proximate causes, such as silt accumulation in river bodies and construction on riverbanks, rather than structural and sociopolitical issues that shape vulnerability and disatser risk. By elevating the perspective of a small urban center, this research highlights the unique challenges and dynamics of flood risk in rapidly growing yet often overlooked urban areas. Collectively, these studies contribute to a more comprehensive understanding of flood risk and recovery, challenging the conventional ways of understanding risk and recovery and moving beyond property damage assessments and technocratic solutions.

The role of carbon in life's blueprint and carbon cycle understanding earth's essential cycling system: benefits and harms to our planet

This abstract provides a concise overview of carbon, carbon dioxide, and the carbon cycle. Carbon is an essential element for life on Earth, serving as the building block of organic molecules found in living organisms. Carbon dioxide (CO2), a greenhouse gas, plays a dual role in supporting life through photosynthesis while also contributing to climate change when its concentration in the atmosphere increases due to human activities. The carbon cycle is a natural process that continuously cycles carbon between the atmosphere, oceans, land, and living organisms. It plays a vital role in regulating the Earth's climate, supporting plant growth through photosynthesis, sequestering carbon in natural sinks, and sustaining various ecosystems. However, human activities have disrupted the carbon cycle, leading to adverse effects such as climate change, ocean acidification, and ecosystem disturbances. Mitigating these harmful impacts requires global efforts to reduce carbon emissions, conserve forests, and adopt sustainable practices to restore the balance of the carbon cycle and ensure a more sustainable future

Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction

Comparison of threshold selection methods for microarray gene co-expression matrices

<p>Abstract</p> <p>Background</p> <p>Network and clustering analyses of microarray co-expression correlation data often require application of a threshold to discard small correlations, thus reducing computational demands and decreasing the number of uninformative correlations. This study investigated threshold selection in the context of combinatorial network analysis of transcriptome data.</p> <p>Findings</p> <p>Six conceptually diverse methods - based on number of maximal cliques, correlation of control spots with expressed genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of p-values, and statistical power - were used to estimate a correlation threshold for three time-series microarray datasets. The validity of thresholds was tested by comparison to thresholds derived from Gene Ontology information. Stability and reliability of the best methods were evaluated with block bootstrapping.</p> <p>Two threshold methods, number of maximal cliques and spectral graph, used information in the correlation matrix structure and performed well in terms of stability. Comparison to Gene Ontology found thresholds from number of maximal cliques extracted from a co-expression matrix were the most biologically valid. Approaches to improve both methods were suggested.</p> <p>Conclusion</p> <p>Threshold selection approaches based on network structure of gene relationships gave thresholds with greater relevance to curated biological relationships than approaches based on statistical pair-wise relationships.</p

A non-interventional, prospective, multicentric real life Indian study to assess safety and effectiveness of un-denatured type 2 collagen in management of osteoarthritis

Background: Osteoarthritis (OA) is the most common musculoskeletal condition affecting the quality of life. Undenatured collagen type II has emerged as one of the promising treatment options in treatment of OA. Despite being available in India, clinical safety and efficacy have not been evaluated. We performed a non-interventional, real-life study to determine its safety and efficacy in Indian population.Methods: A non-interventional, real-life study was performed in patients with OA of knee by 18 orthopaedicians in India. Patients enrolled were followed-up at day 30 (visit 2), day 60 (visit 3) and day 90 (visit 4). Efficacy was assessed by Western Ontario McMaster Osteoarthritis Index (WOMAC) and Visual Analogue scale (VAS) on each visit. Safety was assessed by incidence of suspected adverse events (AEs), and abnormal laboratory parameters.Results: Among 291 enrolled patients 226 patients completed the study. Mean age of the population was 56.2±8.7 years and 53.3% of them were females. In 291 patients included in safety analysis, at least one treatment emergent adverse event (TEAE) was seen in 4.47% patients. None of the AEs were serious or resulted in termination of patient from the study. Nausea (1.37%) and headache (1.03%) were the common AEs. Treatment with undenatured collagen type II was associated with significant reduction in WOMAC score (p&lt;0.0001) and VAS scores (p&lt;0.0001) from baseline to day 90.Conclusions: Undenatured collagen type II is safe and efficacious in Indian patients with OA. This can be considered early in the initial management of OA