Has Muslim Got Benefited from the National Health Mission? A Situational Analysis of Maternal Health Services in India

BACKGROUND: It is a marked recognition that when the population is disaggregated by religion, wide disparities in the utilization of maternal health care services can be observed. The study was aimed to analyze the levels and trends of maternal health services among Muslims in India. The study also delineated the investigation of confounding factors attributed to maternal health services among the selected population.METHODS: The study utilized the data from the third and fourth round of National Family Health Survey (NFHS), conducted in 2005-06 and 2015-16 respectively. The bivariate and multivariate logistic regression models were employed to accomplish the study objectives.RESULT: There is an increasing trend in the distributional patterns of all three indicators (full ANC, SBA and PNC) during the last two successive surveys. Muslim women belonging to Southern States were seen to be utilizing more maternal health care services as compared to Muslim women in the Northern States. Muslim populated States like Assam, Bihar, Jharkhand, Uttar Pradesh and West Bengal were far cry to achieve the MDG-15 target of utilization of 100 percent skilled birth attendants in 2015. Education, media exposure and wealth status appeared to be major confounding factors for determining the utilization of maternal health services.CONCLUSION: The study revealed that the utilizations of maternal health services among Muslims have progressed during the last decade. It can be concluded that the NHM policy has played an instrumental role in increasing the utilization of maternal health services among Muslims

Biochemical characterization and Biolog based identification of efficient Jute retting bacterial isolates from retting water

Jute is considered as one of the cheapest natural fibre after cotton in terms of its production and uses. Retting is the most important post-harvest operation to yield high quality jute fibre and is solely carried out by various types of retting microorganisms. The present study was undertaken to screen and characterize the efficient retting microbes isolated from retting water based on their enzymatic activity followed by biolog based idenfication of those efficient microbes. These isolates were characterized on the basis of qualitative and quantitative estimation of Pectinolytic, Xylanase and Cellulase activity. Out of 40 isolated strains only 3 were finally identified as efficient jute retting microorganism having high pectinolytic and Xylanase activity coupled with less Cellulase activity. These identified three micro organisms may provide a suitable means to develop a new retting technique especially under water stress condition

Active Learning on Medical Image

The development of medical science greatly depends on the increased utilization of machine learning algorithms. By incorporating machine learning, the medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the diagnosis process, and machine learning can aid in detecting diseases at an early stage. However, training machine learning models with limited annotated medical image data poses a challenge. The majority of medical image datasets have limited data, which can impede the pattern-learning process of machine-learning algorithms. Additionally, the lack of labeled data is another critical issue for machine learning. In this context, active learning techniques can be employed to address the challenge of limited annotated medical image data. Active learning involves iteratively selecting the most informative samples from a large pool of unlabeled data for annotation by experts. By actively selecting the most relevant and informative samples, active learning reduces the reliance on large amounts of labeled data and maximizes the model's learning capacity with minimal human labeling effort. By incorporating active learning into the training process, medical imaging machine learning models can make more efficient use of the available labeled data, improving their accuracy and performance. This approach allows medical professionals to focus their efforts on annotating the most critical cases, while the machine learning model actively learns from these annotated samples to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Case Studies on X-Ray Imaging, MRI and Nuclear Imaging

The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.Comment: 14 pages, 3 figures, 4 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Determination of Median Lethal (LD50) and Growth Reduction (GR50) Dose of Gamma Irradiation for Induced Mutation in Wheat

Abstract The determination of the optimum dose of radiation through its impacts on the growth attributes of the crop is the prerequisite for successful induced mutation breeding. For evaluating the impact of different doses of gamma radiation on wheat (Triticum aestivum), two wheat varieties DBW 187 and K 1006 were irradiated at six different doses (200, 250, 300, 350, 400 and 450 Gy) using a Cobalt-60 source at Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India. Seed germination, survivability and seedling length of the irradiated seeds were measured at 7 days after sowing in laboratory experiments, while plant height, panicle length, grains per panicle and 1000 seed weight was recorded for field studies. It was observed that seed germination, survivability and seedling length declined with the increase in gamma radiation dose. The germination percentage showed significant differences among treatments (100 to 75%), while the survival percentage exhibited significant differences from 200 to 300 Gy in both the varieties. The LD50 for DBW 187 and K 1006 were found to be 272.71 and 278.61 respectively, while the GR50 values were 316.22 and 346.73 for DBW 187 and K 1006 respectively under laboratory conditions. The GR50 for field observations were 341.19 Gy and 339.70 Gy for DBW 187 and K 1006 respectively. Hence, the gamma radiation dose between 250 Gy and 300 Gy was found optimum to obtain desirable results. The obtained dose could be used to generate highest mutation mediated changes with least lethal effects in the subsequent generations of wheat

Gibberellins - a multifaceted hormone in plant growth regulatory network

Plants tend to acclimatize to unfavourable environs by integrating growth and development to environmentally activated signals. Phytohormones strongly regulate convergent developmental and stress adaptive procedures and synchronize cellular reaction to the exogenous and endogenous conditions within the adaptive signaling networks. Gibberellins (GA), a group of tetracyclic diterpenoids, being vital regulators of plant growth, are accountable for regulating several aspects of growth and development of higher plants. If the element of reproduction is considered as an absolute requisite then for a majority of the higher plants GA signaling is simply indispensable. Latest reports have revealed unique conflicting roles of GA and other phytohormones in amalgamating growth and development in plants through environmental signaling. Numerous physiological researches have detailed substantial crosstalk between GA and other hormones like abscisic acid, auxin, cytokinin, and jasmonic acid. In this review, a number of explanations and clarifications for this discrepancy are explored based on the crosstalk among GA and other phytohormones

Distributed Control of Cyber Physical System on Various Domains: A Critical Review

Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is assisting to incorporate the world and influencing our ordinary life significantly. In terms of practical utilization of CPS interacting abundant difficulties. Currently, CPS is involved in modern society very vastly with many uptrend perspectives. All the new technologies by using CPS are accelerating our journey of innovation. In this paper, we have explained the research areas of 14 important domains of Cyber-Physical Systems (CPS) including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. We also demonstrated the challenges and future direction of each paper of all domains. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 158 papers are studied in this survey as well as among these, 98 papers are directly studied with the 14 domains with challenges and future instruction which is the first survey paper as per the knowledge of authors

Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region

Highlights • Water balance algorithms were used to simulate semi-arid lake water levels. • Scenarios were formed by combining in-situ and remote sensing data sources. • The proposed combinations can reproduce lake water level even without in-situ data. • Using in-situ data as initial water level matched best to simulate lake water level. • 9 out of 19 scenarios did not vary significantly with in-situ water level.Lake water level fluctuation is a function of hydro-meteorological components, namely input, and output to the system. The combination of these components from in-situ and remote sensing sources has been used in this study to define multiple scenarios, which are the major explanatory pathways to assess lake water levels. The goal is to analyze each scenario through the application of the water balance equation to simulate lake water levels. The largest lake in Iran, Lake Urmia, has been selected in this study as it needs a great deal of attention in terms of water management issues. We ran a monthly water balance simulation of nineteen scenarios for Lake Urmia from 2003 to 2007 by applying different combinations of data, including observed and remotely sensed water level, flow, evaporation, and rainfall. We used readily available water level data from Hydrosat, Hydroweb, and DAHITI platforms; evapotranspiration from MODIS and rainfall from TRMM. The analysis suggests that the consideration of field data in the algorithm as the initial water level can reproduce the fluctuation of Lake Urmia water level in the best way. The scenario that combines in-situ meteorological components is the closest match to the observed water level of Lake Urmia. Almost all scenarios showed good dynamics with the field water level, but we found that nine out of nineteen scenarios did not vary significantly in terms of dynamics. The results also reveal that, even without any field data, the proposed scenario, which consists entirely of remote sensing components, is capable of estimating water level fluctuation in a lake. The analysis also explains the necessity of using proper data sources to act on water regulations and managerial decisions to understand the temporal phenomenon not only for Lake Urmia but also for other lakes in semi-arid regions

Nitrogen Use Efficiency in Rice under Abiotic Stress: Plant Breeding Approach

Nitrogenous fertilizer has remarkably improved rice (Oryza sativa L.) yield across the world since its discovery by Haber-Bosch process. Due to climate change, future rice production will likely experience a wide range of environmental plasticity. Nitrogen use efficiency (NUE) is an important trait to confer adaptability across various abiotic stresses such as flooding, drought and salinity. The problem with the increased N application often leads to a reduction in NUE. New solutions are needed to simultaneously increase yield and maximize the NUE of rice. Despite the differences among flooding, salinity and drought, these three abiotic stresses lead to similar responses in rice plants. To develop abiotic stress tolerant rice varieties, speed breeding seems a plausible novel approach. Approximately 22 single quantitative trait loci (QTLs) and 58 pairs of epistatic QTLs are known to be closely associated with NUE in rice. The QTLs/genes for submergence (SUB1A) tolerance, anaerobic germination (AG, TPP7) potential and deepwater flooding tolerance (SK1, SK2) are identified. Furthermore, phytochrome-interacting factor-like14 (OsPIL14), or loss of function of the slender rice1 (SLR1) genes enhance salinity tolerance in rice seedlings. This review updates our understanding of the molecular mechanisms of abiotic stress tolerance and discusses possible approaches for developing N-efficient rice variety

Elevated CO₂ Concentration Improves Heat-Tolerant Ability in Crops

The rising concentration of atmospheric carbon dioxide (aCO2) and increasing temperature are the main reasons for climate change, which are significantly affecting crop production systems in this world. However, the elevated carbon dioxide (CO2) concentration can improve the growth and development of crop plants by increasing photosynthetic rate (higher availability of photoassimilates). The combined effects of elevated CO2 (eCO2) and temperature on crop growth and carbon metabolism are not adequately recognized, while both eCO2 and temperature triggered noteworthy changes in crop production. Therefore, to increase crop yields, it is important to identify the physiological mechanisms and genetic traits of crop plants which play a vital role in stress tolerance under the prevailing conditions. The eCO2 and temperature stress effects on physiological aspects as well as biochemical profile to characterize genotypes that differ in their response to stress conditions. The aim of this review is directed the open-top cavities to regulate the properties like physiological, biochemical, and yield of crops under increasing aCO2, and temperature. Overall, the extent of the effect of eCO2 and temperature response to biochemical components and antioxidants remains unclear, and therefore further studies are required to promote an unperturbed production system