Chemical tracers in proto-brown dwarfs: CO, ortho-H2_{2}CO, para-H2_{2}CO, HCO+^{+}, CS observations

We present a study of the CO isotopologues and the high-density tracers H$_{2}$CO, HCO$^{+}$, and CS in Class 0/I proto-brown dwarfs (proto-BDs). We have used the IRAM 30m telescope to observe the $^{12}$CO (2-1), $^{13}$CO (2-1), C$^{18}$O (2-1), C$^{17}$O (2-1), H$_{2}$CO (3-2), HCO$^{+}$ (3-2), and CS (5-4) lines in 7 proto-BDs. The hydrogen column density for the proto-BDs derived from the CO gas emission is $\sim$2-15 times lower than that derived from the dust continuum emission, indicating CO depletion from the gas-phase. The mean H$_{2}$CO ortho-to-para ratio is $\sim$3 for the proto-BDs and indicates gas-phase formation for H$_{2}$CO. We have investigated the correlations in the molecular abundances between the proto-BDs and protostars. Proto-BDs on average show a factor of $\sim$2 higher ortho-to-para H$_{2}$CO ratio than the protostars. Possible explanations include a difference in the H$_{2}$CO formation mechanism, spin-selective photo-dissociation, self-shielding effects, or different emitting regions for the ortho and para species. There is a tentative trend of a decline in the HCO$^{+}$ and H$_{2}$CO abundances with decreasing bolometric luminosity, while the CS and CO abundances show no particular difference between the proto-BDs and protostars. These trends reflect the scaled-down physical structures for the proto-BDs compared to protostars and differences in the peak emitting regions for these species. The C$^{17}$O isotopologue is detected in all of the proto-BDs as well as the more evolved Class Flat/Class II BDs in our sample, and can probe the quiescent gas at both early and late evolutionary stages.Comment: Accepted in MNRAS. arXiv admin note: text overlap with arXiv:1809.1016

Chemical tracers in proto-brown dwarfs: CN, HCN, and HNC observations

We present results from a study of nitrogen chemistry in Class 0/I proto-brown dwarfs (proto-BDs). We have used the IRAM 30 m telescope to observe the CN (2-1), HCN (3-2), and HNC (3-2) lines in 7 proto-BDs. All proto-BDs show a large CN/HCN abundance ratio of >20, and a HNC/HCN abundance ratio close to or larger than unity. The enhanced CN/HCN ratios can be explained by high UV flux originating from an active accretion zone in the proto-BDs. The larger than unity HNC/HCN ratio for the proto-BDs is likely caused by a combination of low temperature and high density. Both CN and HNC show a flat distribution with CO, indicating that these species can survive in regions where CO is depleted. We have investigated the correlations in the molecular abundances of these species for the proto-BDs with Class 0/I protostars. We find tentative trends of CN (HCN) abundances being about an order of magnitude higher (lower) in the proto-BDs compared to protostars. HNC for the proto-BDs shows a nearly constant abundance unlike the large spread of ~2 orders of magnitude seen for the protostars. Also notable is a rise in the HNC/HCN abundance ratio for the lowest luminosity objects, suggesting that this ratio is higher under low-temperature environments. None of the relatively evolved Class Flat/Class II brown dwarfs in our sample show emission in HNC. The HNC molecule can be considered as an efficient tracer to search and identify early stage sub-stellar mass objects.Comment: Accepted in MNRA

Toxicity induced by Solanapyrone A in Chickpea shoots and its metabolism through Glutathione/Glutathione-S-Transferase system

Solanapyrone A and C were isolated from a Pakistani isolate of Ascochyta rabiei, Pk-1. Two experiments were conducted to investigate the phytotoxic effects of the most potent toxin, solanapyrone A on chickpea cultivars and its subsequent detoxification through glutathion/glutathion-s-transferase(GST) system. When the shoots of cultivars were fed solanapyrone A, symptoms mimicking to Ascochyta blight appeared and extent of manifestation of symptoms varied with the cultivar. In the first experiment, the effect of three different plant ages of 2 cultivars with different levels of resistance to toxin was determined in terms of GST activity unit. GST activity in Balkasar-2000 (a resistant cultivar) increased 1.92 times, 1.72 and 1.65 times in two-week-old seedling, eight-week-old and adult plants (all treated) respectively as compared to their respective controls. In the highly susceptible cultivar, AUG-424, a slight increase (1.14 times) over control was noticed in GST activity at all the three ages. In the second experiment, where shoots of three cultivars were tested against 2 doses of the toxin, an increase in GST activity in Noor-91 (a moderately susceptible cultivar) and AUG-424 was significantly less than resistant cultivar, Balkasar-2000 showing direct relationship between resistance and activity of the enzyme. It may be concluded that it is a reason for difference in response of cultivars to the disease

Domain Generalisation with Bidirectional Encoder Representations from Vision Transformers

Domain generalisation involves pooling knowledge from source domain(s) into a single model that can generalise to unseen target domain(s). Recent research in domain generalisation has faced challenges when using deep learning models as they interact with data distributions which differ from those they are trained on. Here we perform domain generalisation on out-of-distribution (OOD) vision benchmarks using vision transformers. Initially we examine four vision transformer architectures namely ViT, LeViT, DeiT, and BEIT on out-of-distribution data. As the bidirectional encoder representation from image transformers (BEIT) architecture performs best, we use it in further experiments on three benchmarks PACS, Home-Office and DomainNet. Our results show significant improvements in validation and test accuracy and our implementation significantly overcomes gaps between within-distribution and OOD data.Comment: 4 pages, accepted at the Irish Machine Vision and Image Processing Conference (IMVIP), Galway, August 202

Why do patients with limb ischaemia present late to a vascular surgeon? A prospective cohort study from the developing world

OBJECTIVE: To look into the factors responsible for delay in presentation of Iimb ischemia patients to a vascular surgeon. METHODS: The prospective cohort study was conducted at the Aga Khan University Hospital, Karachi, from October 01, 2016, to August 10, 2018. Patients coming with delayed presentation of both acute and chronic limb ischemia were included. All the patients were assessed by qualified vascular surgeons. SPSS 23 was used for data analysis. RESULTS: Of the 55 patients, 33(60%) had acute and 22(40%) had chronic limb ischaemia. Mean age of acute cases was 44±23.72 years and it was 60±12.49 years for chronic cases. Overall, the commonest reason behind delay was non-referral by primary physician which was the case with 11(33.3%) patients in the acute group, and 13(59%) in the chronic group. The limb loss in the acute group was 20(60%) and 8(36%) in the chronic group.. CONCLUSION: Delayed presentation of patients with limb ischaemia is mainly due to non-referral. A robust campaign needs to be launched to reduce the rate of limb loss

Massive primary postpartum haemorrhage: Setting up standards of care

Objective: To review practice of massive primary postpartum haemorrhage management and develop a protocol.Methods: Cross-sectional study conducted at the Department of Obstetrics and Gynaecology at Aga Khan University Hospital, Karachi between January 1, 2003 and July 31, 2004. Women with primary postpartum haemorrhage and had blood loss \u3e1000ml were included in the study. Medical record files of these women were reviewed for maternal mortality and morbidities which included mode of delivery, possible cause of postpartum haemorrhage, supportive, medical and surgical interventions. Results: Approximately 3% (140/4881) of women had primary postpartum haemorrhage. \u27Near miss\u27 cases with blood loss \u3e1500ml was encountered in 14.37% (20/140) of these cases. Fifty-six percent (18/32) of the women who had massive postpartum haemorrhage delivered vaginally. Uterine-atony was found to be the most common cause, while care in High Dependency Unit (HDU) was required in 87.5% (28/32) of women. In very few cases balloon tamponade (2-cases) and compression sutures (2-cases) were used. Hysterectomy was performed in 4-cases and all of them encountered complications. Blood transfusions were required in 56% of women who had massive postpartum haemorrhage. Conclusion: This study highlights the existence variable practices for the management of postpartum haemorrhage. Interventions to evaluate and control bleeding were relatively aggressive; newer and less invasive options were underutilized. Introduction of an evidence-based management model can potentially reduce the practice variability and improve the quality of car

Structural modeling of natural citrus products as potential cross-strain inhibitors of Dengue virus

There are four serotypes of Dengue virus and there are existing drugs used against specific serotype. There is no drug that is effective against all strains of this virus. In this research, bioinformatics tools were used to predict the affinity of natural ligands for the glycoprotein E of Dengue virus by considering the conserved domains. Molecular docking studies were carried out by using Autodock 3.0. Computational analysis which showed that two ligands have the potential to inhibit the site in glycoprotein E and control of all strains is now possible by these ligands.Key words: Bioinformatics, multivariate drug designing, Dengue virus, in silico drug for dengue, glycoprotein E, conserved domain

Which measures of cigarette dependence are predictors of smoking cessation during pregnancy? Analysis of data from a randomized controlled trial.

AIMS: To examine the ability of different common measures of cigarette dependence to predict smoking cessation during pregnancy. DESIGN: Secondary analysis of data from a parallel-group randomized controlled trial of physical activity for smoking cessation. The outcomes were biochemically validated smoking abstinence at 4 weeks post-quit and end-of-pregnancy. SETTING: Women identified as smokers in antenatal clinics in 13 hospital trusts predominantly in southern England, who were recruited to a smoking cessation trial. PARTICIPANTS: Of 789 pregnant smokers recruited, 784 were included in the analysis. MEASUREMENTS: Using random-effect logistic regression models, we analysed the effects of baseline measures of cigarette dependence, including numbers of cigarettes smoked daily, Fagerström Test of Cigarette Dependence (FTCD) score, the two FTCD subscales of Heaviness of Smoking Index (HSI) and non-Heaviness of Smoking Index (non-HSI), expired carbon monoxide (CO) level and urges to smoke (strength and frequency) on smoking cessation. Associations were adjusted for significant socio-demographic/health behaviour predictors and trial variables, and area under the receiver operating characteristic (ROC) curve was used to determine the predictive ability of the model for each measure of dependence. FINDINGS: All the dependence variables predicted abstinence at 4 weeks and end-of-pregnancy. At 4 weeks, the adjusted odds ratio (OR) (95% confidence interval) for a unit standard deviation increase in FTCD was 0.59 (0.47-0.74), expired CO = 0.54 (0.41-0.71), number of cigarettes smoked per day 0.65 (0.51-0.84) and frequency of urges to smoke 0.79 (0.63-0.98); at end-of-pregnancy they were: 0.60 (0.45-0.81), 0.55 (0.37-0.80), 0.70 (0.49-0.98) and 0.69 (0.51-0.94), respectively. HSI and non-HSI exhibited similar results to the full FTCD. CONCLUSIONS: Four common measures of dependence, including number of cigarettes smoked per day, scores for Fagerström Test of Cigarette Dependence and frequency of urges and level of expired CO, all predicted smoking abstinence in the short term during pregnancy and at end-of-pregnancy with very similar predictive validity

Vision based machine learning algorithms for out-of-distribution generalisation

There are many computer vision applications including object segmentation, classification, object detection, and reconstruction for which machine learning (ML) shows state-of-the-art performance. Nowadays, we can build ML tools for such applications with real-world accuracy. However, each tool works well within the domain in which it has been trained and developed. Often, when we train a model on a dataset in one specific domain and test on another unseen domain known as an out of distribution (OOD) dataset, models or ML tools show a decrease in performance. For instance, when we train a simple classifier on real-world images and apply that model on the same classes but with a different domain like cartoons, paintings or sketches then the performance of ML tools disappoints. This presents serious challenges of domain generalisation (DG), domain adaptation (DA), and domain shifting. To enhance the power of ML tools, we can rebuild and retrain models from scratch or we can perform transfer learning. In this paper, we present a comparison study between vision-based technologies for domain-specific and domain-generalised methods. In this research we highlight that simple convolutional neural network (CNN) based deep learning methods perform poorly when they have to tackle domain shifting. Experiments are conducted on two popular vision-based benchmarks, PACS and Office-Home. We introduce an implementation pipeline for domain generalisation methods and conventional deep learning models. The outcome confirms that CNN-based deep learning models show poor generalisation compare to other extensive methods