A Rare Case of Reversible Encephalopathy Syndrome Accompanying Late Postpartum Eclampsia or Hypertensive Encephalopathy-A Clinical Dilemma

Posterior Reversible Encephalopathy Syndrome (PRES) refers to a clinic-radiologic diagnosis. Clinically it is characterized by non specific symptoms such as headache, confusion, visual disturbances and seizures. The radiological findings in PRES are thought to be due to vasogenic oedema, predominantly in the posterior cerebral hemispheres, and are reversible with appropriate management. We report a case of reversible encephalopathy diagnosed by MRI scan occurring in atypical areas like the caudate and lentiform nuclei of the brain following an uneventful lower segment caesarean section in a normotensive patient, who was successfully treated with antihypertensives, anticonvulsants and supportive treatment. The differential diagnosis of convulsions in the post-partum period is discussed

Granger Causality Detection via Sequential Hypothesis Testing

Most of the metrics used for detecting a causal relationship among multiple time series ignore the effects of practical measurement impairments, such as finite sample effects, undersampling and measurement noise. It has been shown that these effects significantly impair the performance of the underlying causality test. In this paper, we consider the problem of sequentially detecting the causal relationship between two time series while accounting for these measurement impairments. In this context, we first formulate the problem of Granger causality detection as a binary hypothesis test using the norm of the estimates of the vector auto-regressive~(VAR) coefficients of the two time series as the test statistic. Following this, we investigate sequential estimation of these coefficients and formulate a sequential test for detecting the causal relationship between two time series. Finally via detailed simulations, we validate our derived results, and evaluate the performance of the proposed causality detectors.Comment: 5 pages 3 figure

Robust Direction-of-Arrival Estimation using Array Feedback Beamforming in Low SNR Scenarios

A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer, viz. half-power beamwidth (HPBW), side-lobe suppression, and directivity. Through quantitative comparison, we show that our approach outperforms the previous feedback beamforming approach with a single transmit antenna, and the conventional beamformer. We then incorporate a retransmission based minimum variance distortionless response (MVDR) beamformer with the feedback beamforming setup. We propose two approaches, show that one approach is superior in terms of lower estimation error, and use that as the DoA estimation method. We then compare this approach with Multiple Signal Classification (MUSIC), Estimation of Parameters using Rotation Invariant Technique (ESPRIT), robust MVDR, nested-array MVDR, and reduced-dimension MVDR methods. The results show that at SNR levels of -60 dB to -10 dB, the angle estiation error of the proposed method is 20 degree less compared to that of prior methods

Clinical profile of primary hyperparathyroidism in Northeast India: a single centre experience

Background: A retrospective study of the presentation of primary hyperparathyroidism was done at a tertiary care centre in northeast India and was compared with variable features in other parts in India and worldwide.Methods: The clinical presentation, biochemical parameters, radiological and histopathology findings of 27 subjects of primary hyperparathyroidism who presented to us over a period of 5 years were retrospectively analysed. Chi-square test, student t test and 'one way ANOVA' were used to compare different variables. Statistical significance was set at p<0.05.Results: The age distribution ranged from as young as 13 years to 72 years (39±16.7). The male:female ratio was 1:1.25. The duration of symptoms at presentation ranged from 2 to 72 months (21.7±20.3). The most common presentation was bone pain in 59.2% of cases, followed by proximal myopathy (48.1%), fatigue (44.4%), abdominal pain (44.4%), constipation (11.1%), hypertension (18.5%), palpable neck swelling (22.2%), limb deformity (22.2%) and fracture (14.8%). The mean serum calcium was 12.2±0.87mg/dl. Parathyroid adenoma was localized radiologically in all patients and single adenoma was the most common cause in 96.3%. Left inferior parathyroid adenoma was the most common site of involvement in 51.8%.Conclusions: Hyperparathyroidism at our centre in northeast India has a classic symptomatic presentation with severe bone and renal involvement and younger age at diagnosis, and equal gender distribution

Indoor Distance Estimation using LSTMs over WLAN Network

The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy degradation and are almost unavailable in indoor environments. Indoor positioning systems (IPS) based on WiFi signals have been gaining popularity. However, owing to the strong spatial and temporal variations of wireless communication channels in the indoor environment, the achieved accuracy of existing IPS is around several tens of centimeters. We present the detailed design and implementation of a self-adaptive WiFi-based indoor distance estimation system using LSTMs. The system is novel in its method of estimating with high accuracy the distance of an object by overcoming possible causes of channel variations and is self-adaptive to the changing environmental and surrounding conditions. The proposed design has been developed and physically realized over a WiFi network consisting of ESP8266 (NodeMCU) devices. The experiment were conducted in a real indoor environment while changing the surroundings in order to establish the adaptability of the system. We introduce and compare different architectures for this task based on LSTMs, CNNs, and fully connected networks (FCNs). We show that the LSTM based model performs better among all the above-mentioned architectures by achieving an accuracy of 5.85 cm with a confidence interval of 93% on the scale of (4.14 m * 2.86 m). To the best of our knowledge, the proposed method outperforms other methods reported in the literature by a significant margin.Comment: Published in IEEE 16th Workshop on Positioning, Navigation and Communications (WPNC 2019, Germany

Optimal Power Flow with Hybrid Distributed Generators and Unified Controller

Optimal power flow (OPF) study is conducted on a power system to achieve one of the following objectives: cost/loss minimization or available transfer capability (ATC) calculation in a deregulated environment. Distributed generation (DG) is a small source of electric power conversion from non-conventional energy sources and Hybrid DGs which often the most cost-effective and reliable way to produce power. The optimality of control variables and minimum value of objective functions in OPF study would definitely change when DGs are interconnected to the grid. The change would be respect to the location, quantity and combination of power injection by DGs. On the other hand, FACTS controllers are effective in utilizing the existing of transmission network which is very important especially in a deregulated system. Unified power flow controller (UPFC), a second generation FACTS controller, is well known for minimizing the cost of generation/losses with a good voltage profile as well as for ATC improvement. This paper conducts a detailed OPF study on a 9 bus system for the above mentioned three objectives to analyze the effect of DGs with and without UPFC. From the results, it is found that hybrid DGs along with UPFC yields better performance in many aspects

Fatty Acid Profile, Tocopherol Content of Seed Oil, and Nutritional Analysis of Seed Cake of Wood Apple (Limonia acidissima L.), an Underutilized Fruit-Yielding Tree Species

The present study was aimed at analyzing the fatty acid composition, tocopherols, and physico-chemical characterization of wood apple (Limonia acidissima L.) seed oil and the nutritional profile of seed cake. The fatty acids in seed oil were analyzed by gas chromatography–mass spectrometry (GC-MS), and the total seed oil was 32.02 ± 0.08%, comprising oleic (21.56 ± 0.57%), alpha-linolenic (16.28 ± 0.29%), and linoleic acid (10.02 ± 0.43%), whereas saturated fatty acid content was 33.38 ± 0.60% including palmitic (17.68 ± 0.65%) and stearic acid (14.15 ± 0.27%). A greater amount of unsaturated fatty acids (52.37%) were noticed compared to saturated fatty acids (33.38%); hence the seed is highly suitable for nutritional and industrial applications. Gamma-tocopherol was present in a higher quantity (39.27 ± 0.07 mg/100 g) as compared to alpha (12.64 ± 0.01 mg/100 g) and delta (3.77 ± 0.00 mg/100 g) tocopherols, which are considered as natural antioxidants. The spectrophotometric technique was used for quantitative analysis of total phenolic content, and it revealed 135.42 ± 1.47 mg gallic acid equivalent /100 g DW in seed cake. All the results of the studied seed oil and cake showed a good source of natural functional ingredients for several health benefits.</jats:p