Root Morphological and Physiological Bases to Understand Genotypic Control of Mineral Acquisition in Rice Grains

Rice (Oryza sativa L.) supports half of the human population. However, predominant rice consumption leads to malnutrition due to mineral deficiencies. The research goal was to support identification of genes responsible for the uptake/accumulation of potassium (K), iron (Fe), zinc (Zn) and molybdenum (Mo), thus promoting the breeding for rice with high grain concentrations of these elements. Prior studies identified rice genotypes with high grain-K, -Fe, -Zn or -Mo concentrations that were hypothesized to be due to differences in root traits. The research objective was to identify root traits associated with these elements. These traits could be bases for identifying genes. The first study determined if these genotypes showed similar accumulation patterns in leaves as in grains, which would hint at influences of the roots and enable identifying distinct root traits and possible genes in vegetative growth stages. The second study determined if root traits of high grain-Mo genotypes displayed an acid-tolerance mechanism as these genotypes originated from Malaysia where acidic soils strongly adsorb Mo making it unavailable for plants. The third study identified root trait differences of high grain-K, -Fe, -Zn and -Mo genotypes in hydroponics media, while the fourth determined root trait differences in these genotypes in sand-culture media including a 1-Naphthalene Acetic Acid (NAA) seed treatment for perturbation. The first study identified several high grain-Mo genotypes with similar Mo accumulation patterns in V4 to V6 stage-leaves as in grains, suggestive of a root influence. The second study established that gross morphological and physiological root traits of a high grain-Mo genotype were not part of an acid-tolerance mechanism. Neither the third nor fourth study identified root traits related to shoot K, Fe, Zn or Mo concentration, however positive associations of seedling vigor traits with several beneficial elements, including K, and negative associations with numerous toxic elements were established. Lack of correlation with root traits suggests other mechanisms (e.g. active uptake transporters) instead control the observed grain accumulation differences. Based on the fourth study, either direct effects of NAA on element uptake/transfer or indirect effects on soil pH and redox potential altered tissue Fe and Zn levels

Enhancing Public Healthcare Security: Integrating Cutting-Edge Technologies into Social Medical Systems

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities and potential threats in the IoT-23 dataset. The results contribute to the wider discussion on enhancing the security of public healthcare systems. They provide suggestions for incorporating anomaly detection, encryption protocols, and continuous monitoring to strengthen the security of Social Medical Systems. This research provides guidance for policymakers, healthcare practitioners, and technologists as they navigate the changing landscape of healthcare digitization. It advocates for the proactive integration of advanced technologies to ensure the security, privacy, and accessibility of healthcare information within the interconnected web of Social Medical Systems. DOI: https://doi.org/10.52710/seejph.48

Next-Gen Security: Leveraging Advanced Technologies for Social Medical Public Healthcare Resilience

The healthcare industry is undergoing a significant change as it incorporates advanced technologies to strengthen its security infrastructure and improve its ability to withstand current challenges and  explores the important overlap between security, technology, and public health. The introductory section presents a thorough overview, highlighting the current status of public healthcare and emphasizing the crucial importance of security in protecting confidential medical data. This statement highlights the current difficulties encountered by social medical public healthcare systems and emphasizes the urgent need to utilize advanced technologies to strengthen their ability to adapt and recover. The systematic literature review explores a wide range of studies, providing insight into the various aspects of healthcare security. This text examines conventional security methods, exposes their constraints, and advances the discussion by examining cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning, Blockchain, Internet of Things (IoT), and Biometric Security Solutions. Every technology is carefully examined to determine its ability to strengthen healthcare systems against cyber threats and breaches, guaranteeing the confidentiality and accuracy of patient data. The methodology section provides a clear explanation of the research design, the process of selecting participants, and the strategies used for analyzing the data. The research seeks to evaluate the present security situation and determine the best methods for incorporating advanced technologies into healthcare systems, using either qualitative or quantitative methods. The following sections elucidate the security challenges inherent in social medical public healthcare, encompassing cyber threats and privacy concerns. Drawing on case studies, the paper illustrates successful implementations of advanced technologies in healthcare security, distilling valuable lessons and best practices. The recommendations section goes beyond the technical domain, exploring the policy implications and strategies for technological implementation. The exploration of regulatory frameworks, legal considerations, and ethical dimensions is conducted to provide guidance for the smooth integration of advanced technologies into healthcare systems. Healthcare professionals are encouraged to participate in training and awareness programs to ensure a comprehensive and efficient implementation. To summarize, the paper combines the results, highlighting the importance of utilizing advanced technologies to strengthen the security framework of social medical public healthcare. The significance of healthcare resilience is emphasized, and potential areas for future research are delineated. This research is an important resource that offers valuable insights and guidance for stakeholders, policymakers, and technologists who are dealing with the intricate field of healthcare security in the age of advanced technologies. DOI: https://doi.org/10.52710/seejph.48

Toluidine blue: rapid and simple malaria parasite screening and species identification

Malaria, a febrile illness mostly confined to the tropical countries is transmitted by bite of infected female Anopheles mosquito. In 2015 alone, 88% of the malaria burden and 90% deaths due to malaria were confined to the African and Asian countries. Although number of tests are available for rapid diagnosis and screening for malaria, peripheral blood smear examination remains the gold standard.  Leishman stain is recommended by WHO however herein we evaluate one of the alternative methods of staining which is simple and rapid. Fifty patients attending the various outpatient departments of the tertiary care hospital with fever and suspected to have malaria were selected. Two thin-air dried smears prepared from the peripheral venous blood from these subjects were stained by Leishman and Toluidine blue method. The findings of the slides by two independent qualified professionals were noted and the results were analyzed. A total of 14% (7/50) cases were diagnosed to have malaria. All the malaria cases which were positive in Leishman stain were also detected in Toluidine blue stain. Malarial parasites were clearly visible against the homogenously light green background in Toluidine blue. The detection of malarial parasite by Toluidine blue was quick, easy and  confirmative. Toluidine blue stained peripheral blood smear allows for easy  identification and speciation of malarial parasite at low magnification and in shorterperiod of time. Key words: Leishman stain, malaria, screening, toluidine blue stai

IoT and Machine Learning Based Attacks Detection Model on Wearable Health Care Devices

The Internet of Things (IoT) in healthcare is becoming more and more popular in the field of research aimed at improving the effectiveness of intelligent healthcare networks and applications. Nonetheless, distinct risks affect the security and privacy of data in smart health (S-Health). IoT enables healthcare professionals to engage with patients more proactively and with greater vigilance. Smart gadgets with tiny sensors attached to them that communicate with one another to track each other\u27s performance are part of the Internet of Things. To defend S-Health from MITM attacks. The suggested method employs two layers of machine learning algorithms for attack detection and security mechanisms, including low-cost access policies for SHRs (Smart Health Records), lightweight IoT detection schemes, and timely detection of to lessen their impact on the network. According to simulation data, the suggested Hybrid ML performs better than current methods and has a higher attack detection rate overall. The main goal of this research article is to develop an attack detection technique

Cybersecurity Technologies for Protecting Social Medical Data in Public Healthcare Environments

The growing digitization of healthcare systems has made safeguarding sensitive social medical data a crucial priority. The primary objective of this study is to utilize sophisticated cybersecurity technologies, particularly machine learning (ML) algorithms, to improve the security of Electronic Health Records (EHR) in public healthcare settings. The proposed approach presents an innovative technique that merges the advantages of isolation forest and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [IF-DBSCAN]algorithms for anomaly detection, achieving an impressive accuracy rate of 0.968. The study examines the difficulties presented by the distinct characteristics of healthcare data, which includes both medical and social information. The inadequacy of conventional security measures has necessitated the incorporation of sophisticated machine learning algorithms to detect abnormal patterns that may indicate potential security breaches. The hybrid model, which combines isolation forest and DBSCAN, seeks to overcome the constraints of current anomaly detection techniques by offering a resilient and precise solution specifically designed for the healthcare domain. The isolation forest is highly proficient at isolating anomalies by leveraging the inherent attributes of normal data, whereas DBSCAN is adept at detecting clusters and outliers within densely populated data regions. The integration of these two algorithms is anticipated to augment the overall anomaly detection capabilities, thereby strengthening the cybersecurity stance of healthcare systems. The proposed method is subjected to thorough evaluation using real-world datasets obtained from public healthcare environments. The accuracy rate of 0.968 demonstrates the effectiveness of the hybrid approach in accurately differentiating between normal and anomalous activities in EHR data. The research makes a valuable contribution to the field of cybersecurity in healthcare and also tackles the increasing concerns related to the privacy and reliability of social medical data. This research introduces an innovative method for protecting social medical data in public healthcare settings. It utilizes a sophisticated combination of isolation forest and DBSCAN to detect anomalies. The method\u27s high accuracy in the evaluation highlights its potential to greatly improve cybersecurity in healthcare systems, thereby guaranteeing the confidentiality and integrity of sensitive patient information. DOI: https://doi.org/10.52710/seejph.48

Elephant Habitat Suitability Analysis of Alipurduar District, West Bengal Using Geospatial Technology

In India’s Tarai-Dooars region, elephants are the most common wildlife species. The man-wildlife conflict has arisen as a result of forest scarcity, forest fragmentation, global climate change, land use land cover change in the Dooars region, and encroachment into forest life. Although the Wildlife Protection Act of 1972 addressed the conservation of wild animals, the number of wild elephants in West Bengal was constantly changing. The goal of this project is to use geospatial technologies to determine wild elephant habitat suitability zones in West Bengal’s Alipurduar area. The first stage in the conservation and management of wild elephants is to determine their habitat suitability. To assess the result, the various habitat suitability factors/parameters of wild elephants were integrated through weighted overlay analysis in the ArcGIS environment. The result shows that the central part of the district - the Buxa forest area, holds the largest suitable environment for elephant habitat. The rest of the study area can be categorized as a medium habitat suitable area excluding some settlements and built-up areas. The authors hope the result will help the proper management and conservation of wild elephants

Mudflats in lower middle estuary as a favorable location for concentration of metals, west coast of India

372-385Present study was an attempt to understand hydrodynamic conditions and the main factors regulating the distribution of metals in mudflats in the recent past along Mandovi Estuary. Sediment cores of 20 cm length were collected from three mudflats viz. near the mouth, lower middle and upper middle regions representing monsoon season from Mandovi Estuary. Cores were sub sampled at every 2 cm interval and analyzed for sand, silt, clay, organic carbon and selected metals. Sand percentage is the highest in the mudflat situated near the mouth (Betim) and finer sediments are higher in the lower (Karyabhat) and upper (Ribander) middle regions of the estuary. Organic matter associated more with finer sediment fractions at Karyabhat and Ribander (2.19% to 2.37%) mudflat cores as compared to Betim (0.13%). The sediments of Betim fall in the class I to IV, whereas sediments of two cores collected from middle estuarine region fall largely in class III. Sediment samples were analyzed for selected metals. Fe is highest in Ribander mudflat as compared to other cores. Cr, Co, Cu and Pb distribution agrees largely with the pattern of Fe in all the cores. Distribution of Cu and Pb also agrees with that of Mn. Zn and Ni follow the pattern of Co agreeing Fe distribution pattern. Concentration of Fe and Mn along with Ni, Zn, Cr, Pb and Cu are higher in Karyabhat

Oxazoline terminated poly(methyl acrylate) macromonomers: synthesis and characterization

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