Estimation of daily risk of neonatal death, including the day of birth, in 186 countries in 2013: a vital-registration and modelling-based study

Background The days immediately after birth are the most risky for human survival, yet neonatal mortality risks are generally not reported by day. Early neonatal deaths are sometimes under-reported or might be misclassifi ed by day of death or as stillbirths. We modelled daily neonatal mortality risk and estimated the proportion of deaths on the day of birth and in week 1 for 186 countries in 2013. Methods We reviewed data from vital registration (VR) and demographic and health surveys for information on the timing of neonatal deaths. For countries with high-quality VR we used the data as reported. For countries without high-quality VR data, we applied an exponential model to data from 206 surveys in 79 countries (n=50 396 deaths) to estimate the proportions of neonatal deaths per day and used bootstrap sampling to develop uncertainty estimates. Findings 57 countries (n=122 757 deaths) had high-quality VR, and modelled data were used for 129 countries. The proportion of deaths on the day of birth (day 0) and within week 1 varied little by neonatal mortality rate, income, or region. 1·00 million (36.3%) of all neonatal deaths occurred on day 0 (uncertainty range 0·94 million to 1·05 million), and 2·02 million (73.2%) in the fi rst week (uncertainty range 1·99 million to 2·05 million). Sub-Saharan Africa had the highest risk of neonatal death and, therefore, had the highest risk of death on day 0 (11·2 per 1000 livebirths); the highest number of deaths on day 0 was seen in southern Asia (n=392 300). Interpretation The risk of early neonatal death is very high across a range of countries and contexts. Cost-eff ective and feasible interventions to improve neonatal and maternity care could save many lives

Role of yogic practices in individuals with hypertension and low-Peak Expiratory Flow Rate (PEFR) of Ahmedabad city

589-594Hypertension is one of the most important risk factors for various heart related diseases in India, especially in South-Asian region. Nowadays because of very fast life style, breathing pattern and its duration is changed considerably. Breathing duration becomes very short. The main aim of the present study was to assess the therapeutic role of yoga on various cardiovascular parameters, peak expiratory flow rate (PEFR) through pulmonary function test and peripheral capillary oxygen saturation (SpO2), amount of oxygen in the blood in Ahmedabad population. Total 50 individuals with hypertension, low-PEFR and low-SpO2 were selected for the present study. All participants were subjected to yoga therapy (pranayama, yoga postures and meditation) for various time intervals of 0, 3, 6, 9, and 12 months. Heart rate (HR), systolic pressure (SP), diastolic pressure (DP), pulse pressure (PP), mean arterial pressure (MAP), rate pressure product (RPP), double product (DoP), PEFR and SpO2 were measured from all individuals at different intervals. At 0 month, all individuals had very high heart rate (HR), systolic pressure (SP), diastolic pressure (DP), pulse pressure (PP), mean arterial pressure (MAP), rate pressure product (RPP), double product (DoP), but PEFR and SpO2 levels were very low. At the end of 12 month of yoga intervention, significant decrease in all cardiovascular parameters whereas significant elevation of PEFR and SpO2 levels were observed. In conclusion, a comprehensive yoga therapy programme has immense potential to augment the beneficial effects of standard medical management of hypertension, lungs function and total oxygen concentration. Hence it can be used as an effective complementary therapy for heart related diseases

The Involvement of Urinary Kallikrein in the Renal Escape from the Sodium Retaining Effect of Mineralocorticoids

It is well known that the normal kidney escapes the sodium retaining effect of mineralocorticoids. However, the mechanism that mediates this escape is not understood. The possible role of kallikrein in this escape phenomenon was investigated by placing seven dogs in metabolic cages and giving them a constant sodium diet. After they had been on this diet three days, urine was collected for two 24-hour periods. DOCA (25 mg/day) was then given intramuscularly for five days. Urine was collected daily during this DOCA period and for two additional 24- hour periods. Urine volume, sodium, potassium, protein, and kallikrein excretion were then measured. Urinary kallikrein increased from 251.9 ± 34.8 (mean ± SE) in the second day of the control period to 639.8 ± 110.1 IJ-g/day (P \u3c .01) by the third day of treatment. It remained elevated two days after DOCA was discontinued. Sodium excretion decreased significantly on the first day of DOCA treatment, returning to the previous values thereafter. Urine protein excretion remained constant. The enhanced urinary kallikrein during the escape suggests that the kallikrein system could be involved in the regulation of sodium metabolism by acting as a natriuretic factor, or perhaps by regulating the renal blood flow

Role of yogic practices in individuals with hypertension and low-Peak Expiratory Flow Rate (PEFR) of Ahmedabad city

Hypertension is one of the most important risk factors for various heart related diseases in India, especially in South-Asian region. Nowadays because of very fast life style, breathing pattern and its duration is changed considerably. Breathing duration becomes very short. The main aim of the present study was to assess the therapeutic role of yoga on various cardiovascular parameters, peak expiratory flow rate (PEFR) through pulmonary function test and peripheral capillary oxygen saturation (SpO2), amount of oxygen in the blood in Ahmedabad population. Total 50 individuals with hypertension, low-PEFR and low-SpO2 were selected for the present study. All participants were subjected to yoga therapy (pranayama, yoga postures and meditation) for various time intervals of 0, 3, 6, 9, and 12 months. Heart rate (HR), systolic pressure (SP), diastolic pressure (DP), pulse pressure (PP), mean arterial pressure (MAP), rate pressure product (RPP), double product (DoP), PEFR and SpO2 were measured from all individuals at different intervals. At 0 month, all individuals had very high heart rate (HR), systolic pressure (SP), diastolic pressure (DP), pulse pressure (PP), mean arterial pressure (MAP), rate pressure product (RPP), double product (DoP), but PEFR and SpO2 levels were very low. At the end of 12 month of yoga intervention, significant decrease in all cardiovascular parameters whereas significant elevation of PEFR and SpO2 levels were observed. In conclusion, a comprehensive yoga therapy programme has immense potential to augment the beneficial effects of standard medical management of hypertension, lungs function and total oxygen concentration. Hence it can be used as an effective complementary therapy for heart related diseases

nu-Anomica: A Fast Support Vector Based Novelty Detection Technique

In this paper we propose nu-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In -Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5 - 20 times

Fast and Flexible Multivariate Time Series Subsequence Search

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which often contain several gigabytes of data. Surprisingly, research on MTS search is very limited. Most of the existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two algorithms to solve this problem (1) a List Based Search (LBS) algorithm which uses sorted lists for indexing, and (2) a R*-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences. Both algorithms guarantee that all matching patterns within the specified thresholds will be returned (no false dismissals). The very few false alarms can be removed by a post-processing step. Since our framework is also capable of Univariate Time-Series (UTS) subsequence search, we first demonstrate the efficiency of our algorithms on several UTS datasets previously used in the literature. We follow this up with experiments using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>99%) thus needing actual disk access for only less than 1% of the observations. To the best of our knowledge, MTS subsequence search has never been attempted on datasets of the size we have used in this paper

Study of therapeutic role of yoga (Hathyoga) on lipid profile in dyslipidemic individuals of Ahmedabad city

333-338Abnormal lipid profile is a common problem among hypertensive as well as working individuals. The rate of mortality is increasing day-by-day due to cardiovascular problems which occurs due to dyslipidemia. The aim of the present study was to assess the therapeutic role of yoga on lipid profile in Ahmedabad population. Total 50 normal healthy control and 50 individuals with dyslipidemia aged >20 years were enrolled for the present study. They were divided into two groups. Individuals in Group-1 were normal healthy, whereas individuals in Group-2 were dyslipidemics. All participants were subjected to yoga practices (Hathyogic practices - Pranayama, yoga postures and meditation) for the various intervals of 0, 3, 6, 9, and 12 months. Serum lipid profile was estimated for all individuals at different intervals. Before beginning the yoga intervention i.e., at 0 month the levels of Total Cholesterol, Total Triglycerides, LDL-cholesterol and VLDL-cholesterol were significantly high, whereas HDL-cholesterol levels were significantly low. After completion of 12 months yoga intervention a significant reduction was observed in Total Cholesterol, Total Triglycerides, LDL-cholesterol and VLDL-cholesterol as well as a significant elevation of HDL-cholesterol was observed. Yoga (Hathyoga) can be a new added adjuvant and cost effective therapy for the patients with abnormal lipid profile

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art method

Fast Multivariate Search on Large Aviation Datasets

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actua

Neonatal cause-of-death estimates for the early and late neonatal periods for 194 countries: 2000-2013.

OBJECTIVE: To estimate cause-of-death distributions in the early (0-6 days of age) and late (7-27 days of age) neonatal periods, for 194 countries between 2000 and 2013. METHODS: For 65 countries with high-quality vital registration, we used each country's observed early and late neonatal proportional cause distributions. For the remaining 129 countries, we used multinomial logistic models to estimate these distributions. For countries with low child mortality we used vital registration data as inputs and for countries with high child mortality we used neonatal cause-of-death distribution data from studies in similar settings. We applied cause-specific proportions to neonatal death estimates from the United Nations Inter-agency Group for Child Mortality Estimation, by country and year, to estimate cause-specific risks and numbers of deaths. FINDINGS: Over time, neonatal deaths decreased for most causes. Of the 2.8 million neonatal deaths in 2013, 0.99 million deaths (uncertainty range: 0.70-1.31) were estimated to be caused by preterm birth complications, 0.64 million (uncertainty range: 0.46-0.84) by intrapartum complications and 0.43 million (uncertainty range: 0.22-0.66) by sepsis and other severe infections. Preterm birth (40.8%) and intrapartum complications (27.0%) accounted for most early neonatal deaths while infections caused nearly half of late neonatal deaths. Preterm birth complications were the leading cause of death in all regions of the world. CONCLUSION: The neonatal cause-of-death distribution differs between the early and late periods and varies with neonatal mortality rate level. To reduce neonatal deaths, effective interventions to address these causes must be incorporated into policy decisions