An efficient protocol for in vitro propagation of Rosa gruss an teplitz and Rosa centifolia

Rose is a beautiful flower having commercial and ornamental value. In order to establish protocol shoot tips explants of Rosa gruss an teplitz and Rosa centifolia were proliferated in vitro using MS medium supplemented with different levels of benzylaminopurine (0, 0.5,1.0, 1.5, 2.0, 2.5 and 3.0 mg l-1 ). Maximum numbers of shoots (3.906), shoot length (3.106 cm), fresh weight (178.47 mg) and dry weight (43.06 mg) was recorded at 1.0 mg l-1 BAP. For induction of root, uniform micro-shoots were excised and transferred to the rooting medium (1/2 MS macro, micro elements and vitamins) supplemented with 20 g l-1 sucrose and different concentrations (0.00, 0.25, 0.50, 1.0, 1.5 and 2.0 mg l-1) of indole-3-butyric acid (IBA). IBA increased culture rooting percentage (89.375), number of roots (8.7188) and root length (3.5781 cm) more efficiently at 0.50 mg l-1.Key words: In vitro propagation, BAP, indole-3-butyric acid (IBA), Rosa gruss an teplitz, Rosa centifolia

The Classification of Periodic Light Curves from non-survey optimized observational data through Automated Extraction of Phase-based Visual Features

We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. These light curves are generated from a reduction of non-survey optimized observational images gathered by wide-field cameras mounted on the Liverpool Telescope. We extract 16 features found to be highly informative in previous studies but achieve only 19.82% accuracy on a 30% test set, 5.56% above a random model. Noise and sampling defects present in these light curves poison these features primarily by reducing our Periodogram period match rate to fewer than 5%. We propose using an automated visual feature extraction technique by transforming the phase-folded light curves into image based representations. This eliminates much of the noise and the missing phase data, due to sampling defects, should have a less destructive effect on these shape features as they still remain at least partially present. We produced a set of scaled images with pixels turned either on or off based on a threshold of data points in each pixel defined as at minimum one fifth of those of the most populated pixel for each light curve. Training on the same feedforward network, we achieve 29.13% accuracy, a 13.16% improvement over a random model and we also show this technique scales with an improvement to 33.51% accuracy by increasing the number of hidden layer neurons. We concede that this improvement is not yet sufficient to allow these light curves to be used for automated classification and in conclusion we discuss a new pipeline currently being developed that simultaneously incorporates period estimation and classification. This method is inspired by approximating the manual methods employed by astronomers

GRAPE: Genetic Routine for Astronomical Period Estimation

Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: A Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimised for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalised Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular survey cadence and the unique Skycam variable cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary and eccentric eclipsing binary. We apply GRAPE and a frequency spectrum BGLS periodogram to the light curves and show that the performance of GRAPE is superior to the frequency spectrum for any signal well modelled by the fitness function. This is due to treating the parameter space as a continuous variable.We also show that the Skycam sampling is sufficient to correctly estimate the period of over 90% of the sinusoidal shape light curves relative to the more standard regular cadence.We note that GRAPE has a computational overhead which makes it slower on light curves with low numbers of observations and faster with higher numbers of observations and discuss the potential optimisations used to speedup the runtime. Finally, we analyse the period dependence and baseline importance of the performance of both methods and propose improvements which will extend this method to the detection of quasi-periodic signals

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method. The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods. We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct

A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. This makes it almost impossible for objects to be identified manually. Therefore the production of methods and systems for the automated classification of time-domain astro-nomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure. These in-struments have been in operation since March 2009 gathering data of multi-degree sized areas of sky around the current field of view of the main telescope. Utilizing a Structured Query Language database established by a pre-processing operation upon the resultant images, which has identified millions of candidate variable stars with multiple time-varying magnitude observations, we applied a method designed to extract time-translation invariant features from the time-series light curves of each object for future input into a classification system. These efforts were met with limited success due to noise and uneven sampling within the time-series data. Additionally, finely surveying these light curves is a processing intensive task. Fortunately, these algorithms are capable of multi-threaded implementations based on available resources. Therefore we propose a new system designed to utilize multiple intelligent agents that distribute the data analysis across multiple machines whilst simultaneously a powerful intelligence service operates to constrain the light curves and eliminate false signals due to noise and local alias periods. This system will be highly scalable, capable of operating on a wide range of hardware whilst maintaining the production of ac-curate features based on the fitting of harmonic models to the light curves within the initial Structural Query Language database

Stellar Coronal and Wind Models: Impact on Exoplanets

Surface magnetism is believed to be the main driver of coronal heating and stellar wind acceleration. Coronae are believed to be formed by plasma confined in closed magnetic coronal loops of the stars, with winds mainly originating in open magnetic field line regions. In this Chapter, we review some basic properties of stellar coronae and winds and present some existing models. In the last part of this Chapter, we discuss the effects of coronal winds on exoplanets.Comment: Chapter published in the "Handbook of Exoplanets", Editors in Chief: Juan Antonio Belmonte and Hans Deeg, Section Editor: Nuccio Lanza. Springer Reference Work

Maternal BMI and nutritional status in early pregnancy and its impact on neonatal outcomes at birth in Bangladesh

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background To assess the maternal characteristics and nutritional status according to body mass index (BMI) at 6–14 weeks of gestation and to examine the relationship between maternal nutritional status in early pregnancy and its impact on neonatal birth weight. Methods The investigation was conducted from April 2011 to June 2012 in Dhaka, Bangladesh. A total of 498 primigravida pregnant women participated in the study; women with known diabetes or previous gestational diabetes (GDM) were excluded. Maternal demographic details, pregnancy history and anthropometric measurements were obtained from the mother at the recruitment (6–14 weeks), 2nd visit between 24 and 28 week of gestation and 3rd visit at delivery. Cord venous blood samples of newborns (n = 138) were collected immediately after delivery for blood glucose, insulin, lipid profile, leptin and micronutrients including serum folate, ferritin, homocysteine, vitamin D, and vitamin B12. Results The prevalence at 6–14 weeks of pregnancy of anemia (Hb,  15 μmol/l), folate deficiency (< 3 ng/ml) and iron deficiency (ferritin < 13 ng/ml) were 19.5, 46.4, 15.1, 1.2, 0.4, and 12.7% respectively. GDM was found in 18.4% women. The prevalence of GDM was higher in overweight women (28.1%) than underweight (16.7%) and normal weight women (16.0%: p <  0.05). The incidence of low birth weight (LBW) and preterm delivery were 11.6 and 5.8% respectively and was not related to maternal BMI at 6–14 weeks of pregnancy. Maternal height was positively (p = 0.02), and homocysteine was negatively associated with neonatal birth weight (p = 0.02). In addition, the newborn’s cord serum folate was positively (p = 0.03) and cord triglyceride was negatively (p = 0.03) associated with neonatal birth weight. Conclusion Multiple maternal micronutrient deficiencies were present in early pregnancy. Maternal BMI in early pregnancy was not related to preterm deliveries or LBW. LBW was associated with lower folate, elevated cord triglyceride concentrations of the neonates and mother’s height and increase in maternal homocysteine levels. The data has important implications for pregnancy care in Bangladesh and other similar communities.Financial support from European Union (FP7 EU grant: 83599025)

Cancer mortality patterns in Ghana: a 10-year review of autopsies and hospital mortality

BACKGROUND: Cancer mortality pattern in Ghana has not been reviewed since 1953, and there are no population-based data available for cancer morbidity and mortality patterns in Ghana due to the absence of a population-based cancer registry anywhere in the country. METHODS: A retrospective review of autopsy records of Department of Pathology, and medical certificate of cause of death books from all the wards of the Korle-Bu Teaching Hospital (KBTH), Accra, Ghana during the 10-year period 1991–2000 was done. RESULTS: The present study reviews 3659 cancer deaths at the KBTH over the 10-year period. The male-to-female ratio was 1.2:1. The mean age for females was 46.5 [Standard Deviation (SD), 20.8] years, whilst that of males was 47.8 (SD, 22.2) years. The median age was 48 years for females and 50 years for males.Both sexes showed a first peak in childhood, a drop in adolescence and young adulthood, and a second peak in the middle ages followed by a fall in the elderly, with the second peak occurring a decade earlier in females than in males. The commonest cause of cancer death in females was malignancies of the breast [Age-Standardized Cancer Ratio (ASCAR), 17.24%], followed closely by haematopoietic organs (14.69%), liver (10.97%) and cervix (8.47%). Whilst in males, the highest mortality was from the liver (21.15%), followed by prostate (17.35%), haematopoietic organs (15.57%), and stomach (7.26%). CONCLUSION: Considering the little information available on cancer patterns in Ghana, this combined autopsy and death certification data from the largest tertiary hospital is of considerable value in providing reliable information on the cancer patterns in Ghana

Breast cancer risk factor knowledge among nurses in teaching hospitals of Karachi, Pakistan: a cross-sectional study

BACKGROUND: Breast cancer is the most common cancer among women in both the developed and the developing world. The incidence of breast cancer in Karachi, Pakistan is 69.1 per 100,000 with breast cancer presentation in stages III and IV being common (≥ 50%). The most pragmatic solution to early detection lies in breast cancer education of women. Nurses constitute a special group having characteristics most suited for disseminating breast cancer information to the women. We assessed the level of knowledge of breast cancer risk factors among registered female nurses in teaching hospitals of Karachi. We also identified whether selected factors among nurses were associated with their knowledge of breast cancer risk factors, so that relevant measures to improve knowledge of nurses could be implemented. METHODS: A cross-sectional survey was conducted in seven teaching hospitals of Karachi using stratified random sampling with proportional allocation. A total of 609 registered female nurses were interviewed using a structured questionnaire adapted from the Stager's Comprehensive Breast Cancer Knowledge Test. Knowledge of breast cancer risk factors was categorized into good, fair and poor categories. Ordinal regression was used to identify factors associated with risk knowledge among nurses. RESULTS: Thirty five percent of nurses had good knowledge of risk factors. Graduates from private nursing schools (aOR = 4.23, 95% CI: 2.93, 6.10), nurses who had cared for breast cancer patients (aOR = 1.41, 95% CI: 1.00, 1.99), those having received a breast examination themselves (aOR = 1.56, 95% CI: 1.08, 2.26) or those who ever examined a patient's breast (aOR = 1.87, 95% CI: 1.34, 2.61) were more likely to have good knowledge. CONCLUSION: A relatively small proportion of the nursing population had good level of knowledge of the breast cancer risk factors. This knowledge is associated with nursing school status, professional breast cancer exposure and self history of clinical breast examination. Since only about one-third of the nurses had good knowledge about risk factors, there is a need to introduce breast cancer education in nursing schools particularly in the public sector. Continuing nursing education at the workplace can be of additional benefit

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty