Age at menarche and menstrual complications: a cross cultural study among hostel students in Tirupati, Chittoor district, Andhra Pradesh, India

Background: Menarche is an important biological and physiological event, which occur in the lifecycle of every normal female. This is accompanied by many morphological, physiological changes in the body. The age of menarche is generally between 10-16 years. The objectives was to assess the age at menarche and menstrual complications among hostel students during menstrual period.Methods: This is a cross-sectional study in which 326 girl students staying at hostel were selected by retrospective method of recall and were evaluated. The exact date of menarche and also any complications occur during menstruation period like pre menstrual syndrome (PMS), irregular periods, painful periods etc. was noted.Results: In the present study majority of the women attain menstruation at the 14th year of their age (31.0%). The mean menarcheal age was 13.83±1.209 years. It was found that among 18.7% are getting irregular periods, 77.9% reported to have premenstrual symptoms and 31.3% students having a set of symptoms like abdominal or back pain / feeling heaviness of the body this was the major ailment suffered by most of the students.  It was reported that 53.4% are having painful periods.Conclusions: Create awareness related to reduce disease burden and poor health outcome associated with poor menstrual  complications and self care has to be  taken among this group to Promote  positive attitudes  towards the mean age at menarche and menstruation related problems among the adolescent girls is the need of the hour. Hence, we suggest that health education programs regarding PMS and other menstrual problems must be included in the curriculum of secondary schools to bring down the prevalence of such problems.

Teaching Machines to Ask Useful Clarification Questions

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. Asking questions is also a natural way for machines to express uncertainty, a task of increasing importance in an automated society. In the field of natural language processing, despite decades of work on question answering, there is relatively little work in question asking. Moreover, most of the previous work has focused on generating reading comprehension style questions which are answerable from the provided text. The goal of my dissertation work, on the other hand, is to understand how can we teach machines to ask clarification questions that point at the missing information in a text. Primarily, we focus on two scenarios where we find such question asking to be useful: (1) clarification questions on posts found in community-driven technical support forums such as StackExchange (2) clarification questions on descriptions of products in e-retail platforms such as Amazon. In this dissertation we claim that, given large amounts of previously asked questions in various contexts (within a particular scenario), we can build machine learning models that can ask useful questions in a new unseen context (within the same scenario). In order to validate this hypothesis, we firstly create two large datasets of context paired with clarification question (and answer) for the two scenarios of technical support and e-retail by automatically extracting these information from available datadumps of StackExchange and Amazon. Given these datasets, in our first line of research, we build a machine learning model that first extracts a set of candidate clarification questions and then ranks them such that a more useful question would be higher up in the ranking. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We hypothesize that by explicitly modeling the value added by an answer to a given context, our model can learn to identify more useful questions. We evaluate our model against expert human judgments on the StackExchange dataset and demonstrate significant improvements over controlled baselines. In our second line of research, we build a machine learning model that learns to generate a new clarification question from scratch, instead of ranking previously seen questions. We hypothesize that we can train our model to generate good clarification questions by incorporating the usefulness of an answer to the clarification question into the recent sequence-to-sequence based neural network approaches. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate our model on our two datasets of StackExchange and Amazon, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training. We observe that our question generation model generates questions that range a wide spectrum of specificity to the given context. We argue that generating questions at a desired level of specificity (to a given context) can be useful in many scenarios. In our last line of research we, therefore, build a question generation model which given a context and a level of specificity (generic or specific), generates a question at that level of specificity. We hypothesize that by providing the level of specificity of the question to our model during training time, it can learn patterns in the question that indicate the level of specificity and use those to generate questions at a desired level of specificity. To automatically label the large number of questions in our training data with the level of specificity, we train a binary classifier which given a context and a question, predicts whether the question is specific (to the context) or generic. We demonstrate the effectiveness of our specificity-controlled question generation model by evaluating it on the Amazon dataset using human judgements

A 45-DAY RANDOMIZED, OPEN-LABEL, COMPARATOR STUDY TO EVALUATE THE SAFETY AND EFFICACY OF ZINCOVIT TABLETS WITH GRAPE SEED EXTRACT (NUTRITIONAL FOOD SUPPLEMENT) IN PATIENTS WITH TYPE 2 DIABETES MELLITUS

ABSTRACTObjective: To evaluate the efficacy of Zincovit (ZVT) tablets with grape seed extract (GSE) in patients with Type 2 diabetes mellitus by testing thehypothesis of a greater reduction in plasma glucose levels (fasting blood sugar [FBS] and post-prandial blood sugar [PPBS]) from baseline and after45 days of therapy as compared to standard comparator.Methods: This was a randomized, open-label, comparative (2-arm), prospective 45 days study. Treatment consisted of 2 arms: Antidiabetic drugplus non-pharmacological measure alone or ZVT tablets with GSE plus non-pharmacological measures. A total of 30 patients (15 in each arm) wereincluded in the study.Results: ZVT tablet did not alter the FBS, PPBS, and HbA1c level in diabetic patients compared to diabetic patients treated with placebo. No changeswere seen in any of the safety parameters when given for 45 days.Conclusion: ZVT tablets do not possess antidiabetic activity in spite of good safety profile in our study design. This could be due to several limitationsof the study such as inadequate sample size, short duration of the study, and wrong selection of the patients. A long-term, double-blind, placebocontrolled study in a large sample of population measuring glycemic parameters, and cardiovascular outcomes could give a clear picture of the antidiabeticeffectof ZVT with GSE tablets.Keywords: Diabetes mellitus, Zincovit tablets, Grape seed extract, Antioxidant, Safety parameters

Polarization Elements-A Group Theoretical Study

The Classification of Polarization elements, the polarization affecting optical devices which have a Jones matrix representation, according to the types of eigenvectors they possess, is given a new visit through the Group-theoretical connection of polarization elements. The diattenuators and retarders are recognized as the elements corresponding to boosts and rotations respectively. The structure of homogeneous elements other than diattenuators and retarders are identified by giving the quaternion corresponding to these elements. The set of degenerate polarization elements is identified with the so called `null' elements of the Lorentz Group. Singular polarization elements are examined in their more illustrative Mueller matrix representation and finally the eigenstructure of a special class of singular Mueller matrices is studied.Comment: 7 pages, 2 tables, submitted to `Optics Communications