Reticulocytes-Mother of Erythrocytes

Reticulocytes are immature red blood cells (RBCs) that is seen in the bone marrow after through nuclear extrusion from the orthochromatic normoblasts. They are released into the peripheral blood as mature RBCs, after completion of maturation in the bone marrow. The reticulocyte count reflects the erythropoietic activity of the bone marrow, the rate of reticulocyte delivery from the bone marrow into the peripheral blood, and the rate of reticulocyte maturation. Reticulocyte enumeration is also of value in monitoring bone marrow regenerative activity after chemotherapy or bone marrow transplantation. Manual counting of reticulocytes by light microscopy with supravital dyes for RNA remains the standard method of reticulocyte enumeration. However, automated methods of reticulocyte enumeration developed during the past decade are much more accurate, precise, and cost-effective than manual counting, and are increasingly being performed in the clinical laboratory. The differentiation of the reticulocyte is based on the presence of RNA. The newer techniques provide a variety of reticulocyte related parameters, such as the reticulocyte maturation index and immature reticulocyte fraction, which are not available with light microscopy. These new parameters are under evaluation in the clinical diagnosis and monitoring of hematological disorders

Combining ability and heterosis analysis for fibre yield and quality parameters in roselle (Hibiscus sabdariffa L.)

Roselle (Hibiscus sabdariffa L.) is second important bast fibre crop after jute in India. With an aim to ex-ploit non-additive genetic variance present experiment was designed to identify good general combining parents and specific cross combination for fibre yield and fibre quality parameters (fibre fineness, fibre tenacity) in roselle. A total of 11 parents were crossed in complete diallel fashion which resulted 55 F1, 55 RF1 (reciprocal F1). Parents, F1s and RF1s were grown in randomized block design. Analysis of variance revealed significant differences (P< 0.01, P<0.05) among the parents and their hybrids. The parents AMV 1, AMV 5, GR 27 and AHS 160 were identified as good combiners since they recorded significant general combining ability (GCA) effects for fibre yield and quality parameters. Further, For fibre yield only three crosses (AMV 1 × AMV 4, AMV 1 × GR 27, HS 4288 × JRR 07) showed significant specific combining ability (SCA) effects from them hybrid AMV 1 × GR 27 (fibre yield=27.37g/ plant) exhibited positively significant best parent (Non bris 4, Mean fibre yield=21.16g/plant) heterosis (29.35%). Similarly, for fibre tenacity, hybrid GR 27 × JRR 07 (fibre tenacity=23.47g/tex) exhibited positively significant best parent (HS 4288; fibre tenacity=20.35g/tex) heterosis (15.30%)

Prevalence of depression and health related quality of life among patients with diabetes mellitus and hypertension attending a secondary care hospital in district Faridabad, Haryana

Background:  Diabetes mellitus (DM) and hypertension (HT) have significant effect on the mental health of the patient. and. We aimed to estimate the prevalence of depression, and the quality of life among patients with diabetes mellitus and hypertension who attended a secondary care hospital. Methods: A cross-sectional study was carried out among 618 patients who had DM and/or HT.  PHQ-9 and WHO-BREF QOL questionnaire were administered to assess depression and health related quality of life respectively. Results: More than 2/3rd of patients had depression. Among those who had depression, nearly half (46%) had moderate depression and 2.1% had severe depression. The proportion of severely depressed patients was higher in diabetes mellitus group compared to the hypertension group.   Patients that were depressed had poorer quality of life compared to non-depressed, and the difference was statistically significant. Conclusion: Patients with diabetes mellitus and hypertension may be screened for depression and managed accordingly

An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm

Evolution and Final Fates of a Rotating 25 M⊙_{\odot} Pop III star

In this proceeding, we present the 1-dimensional stellar evolution of two rotating population III (Pop III) star models, each having a mass of 25 M$_{\odot}$ at the zero-age main-sequence (ZAMS). The slowly rotating model has an initial angular rotational velocity of 10 per cent of the critical angular rotational velocity. In contrast, the rapidly rotating model has an initial angular rotational velocity of 70 per cent of the critical angular rotational velocity. As an effect of rotationally enhanced mixing, we find that the rapidly rotating model suffers an enormous mass loss due to the deposition of a significant amount of CNO elements toward the surface after the main-sequence phase. We also display the simulated light curves as these models explode into core-collapse supernovae (CCSNe).Comment: Resubmitted after incorporating minor revision, Part of 3$^{rd}$ BINA conference proceeding

Status of Plasmodium Falciparum and Vivax in Jharkhand: A Five Year (2004-08) Retrospective Study at Rajendra Institute of Medical Sciences, Ranchi

ABSTRACT Malaria is well known for its fatalities worldwide. In India, it is still endemic in many areas where two species of Plasmodium namely Plasmodium vivax and Plasmodium falciparum are reported. P.vivax is widespread, creating lots of morbidities across the country. P. falciparum, on the other hand, though comparatively narrow in its infectious volume, is a serious cause of mortalities in India. A five year survey was conducted from 2004 to 2008 in a high malaria-hit district, Ranchi. Thick and thin blood smears were made at the Department of Clinical Pathology, Rajendra Institute of Medical Sciences (RIMS), where the microscopic examinations were carried out. The overall reported and examined cases at RIMS included 36643 suspected malaria cases, out of which, 21833(59.5%) were found positive. Out of these positive cases, 6842(31.3%) were confirmed as P. falciparum patients and 14991(68.6%) as P. vivax cases respectively. Number of negative cases was 14811 (40.4%). In this study, it was observed that after the year 2005, incidence of malaria suddenly dropped by 50% and remained almost static on the same level in the following years with only some seasonal variations. However, it was observed that P. falciparum steadily became more dangerous. It is therefore highly necessary to take immediate and effective measures to minimize the complications of P. falciparum along with P. vivax to prevent death toll in these areas

SN 2010kd: Photometric and Spectroscopic Analysis of a Slow-decaying Superluminous Supernova

This paper presents data and analysis of SN 2010kd, a low-redshift (z = 0.101) H-deficient superluminous supernova (SLSN), based on ultraviolet/optical photometry and optical spectroscopy spanning between curve comparison of SN 2010kd with a subset of well-studied SLSNe I at comparable redshifts indicates that it is a slow-decaying PTF12dam-like SLSN. Analytical light-curve modeling using the Minim code suggests that the bolometric light curve of SN 2010kd favors circumstellar matter phase spectra does not identify broad H or He lines, whereas the photospheric-phase spectra are dominated by O I, O II, C II, C IV, and Si II, in particular the presence of both low- and high-velocity components of O II and Si II lines. The nebular-phase spectra of SN 2010kd are dominated by O I and Ca II emission lines similar to those seen in other SLSNe I. The line velocities in SN 2010kd exhibit flatter evolution curves similar to SN 2015bn but with comparatively higher values. SN 2010kd shows a higher single-zone local thermodynamic equilibrium temperature in comparison to PTF12dam and SN 2015bn, and it has an upper O I ejected mass limit of ∼10 M☉. The host of SN 2010kd is a dwarf galaxy with a high star formation rate (∼0.18 ± 0.04 M☉ yr-1) and extreme emission lines

First Report Confirming the Occurrence of Jumping Halfbeak Hemiramphus archipelagicus Collette and Parin 1978 (Beloniformes: Hemiramphidae) from the Western Bay of Bengal

The Jumping halfbeak, Hemiramphus archipelagicus Collette and Parin 1978, has been previously reported from the west coast of India (Arabian Sea). During a taxonomic study on Hemiramphidae, eight specimens of H. archipelagicus, collected from the coastal water of Odisha, east coast of India, were examined. The species is characterized by 13–14 dorsal-fin rays, 9–12 anal-fin rays, 11–12 pectoral-fin rays, 6 pelvic-fin rays, 25–28 gillrakers on first gill arch and 36–41 pre-dorsal scales, body width less than the depth (62.1–62.3% in body depth), pectoral-fin short (17.2–18.4% in SL) not reaching anterior margin of nasal pit when folded forward, dorsal fin without well-developed anterior lobe and absence of vertical bars on the sides of body. This is the first report confirming the occurrence of H. archipelagicus from the east coast of India (western Bay of Bengal) suggesting that this species is widely distributed throughout the Indian coast

Real-Time Prediction of Reliability of Dynamic Positioning Sub-Systems for Computation of Dynamic Positioning Reliability Index (DP-RI) Using Long Short Term Memory (LSTM)

In this study, a framework using Long Short Term Memory (LSTM) for prediction of reliability of Dynamic Positioning (DP) sub-systems for computation of Dynamic Positioning Reliability Index (DP-RI) has been proposed. The DP System is complex with significant levels of integration between many sub-systems such as the Reference System, DP Control System, Thruster / Propulsion System, Power System, Electrical System and the Environment System to perform diverse control functions. The proposed framework includes a mathematical computation approach to compute reliability of DP sub-systems and a data driven approach to predict the reliability at a sub-system level for evaluation of model performance and accuracy. The framework results demonstrate excellent performance under a wide range of data availability and guaranteed lower computational burden for real-time non-linear optimization. There are three main components of the proposed architecture for the mathematical formulation of the DP sub-systems based on individual sensor arrangements within the sub-system, computation of reliability of sub-systems and optimized LSTM deep learning algorithm for prediction of its reliability. Firstly, the mathematical formulation for the reliability of sub-systems is determined based on the series/parallel arrangement of the sensors of each individual equipment item within the sub-systems. Secondly, the computation of the reliability of sub-systems is achieved through an integrated approach during complex operation of the vessel. Thirdly, the novel optimized LSTM network is constructed to predict the reliability of the subsystems while minimizing integral errors in the algorithm. In this paper, numerical simulations are set-up using a state-of-the-art advisory decision-making tool with mock-up and real-world data to give insights into the model performance and validate it against the existing risk assessment methodologies. Furthermore, we have analyzed the efficiency and stability of the proposed model against various levels of data availability. In conclusion the prediction accuracy of the proposed model is scalable and higher when compared with other model results