Theory of the asymmetric ripple phase in achiral lipid membranes

We present a phenomenological theory of phase transitions in achiral lipid membranes in terms of two coupled order parameters -- a scalar order parameter describing lipid chain melting, and a vector order parameter describing the tilt of the hydrocarbon chains below the chain-melting transition. Existing theoretical models fail to account for all the observed features of the phase diagram, in particular the detailed microstructure of the asymmetric ripple phase lying between the fluid and the tilted gel phase. In contrast, our two-component theory reproduces all the salient structural features of the ripple phase, providing a unified description of the phase diagram and microstructure

Phase behavior of two-component lipid membranes: theory and experiments

The structure of the ripple phase of phospholipid membranes remains poorly understood in spite of a large number of theoretical studies, with many experimentally established structural features of this phase unaccounted for. In this article we present a phenomenological theory of phase transitions in single- and two-component achiral lipid membranes in terms of two coupled order parameters -- a scalar order parameter describing {\it lipid chain melting}, and a vector order parameter describing the {\it tilt of the hydrocarbon chains} below the chain-melting transition. This model reproduces all the salient structural features of the ripple phase, providing a unified description of the phase diagram and microstructure. In addition, it predicts a variant of this phase which does not seem to have been experimentally observed. Using this model we have calculated generic phase diagrams of two-component membranes. We have also determined the phase diagram of a two-component lipid membrane from x-ray diffraction studies on aligned multilayers. This phase diagram is found to be in good agreement with that calculated from the model.Comment: 10 pages, 10 figure

A COMPARATIVE STUDY OF POSTPARTUM BLOOD LOSS BY VISUAL ESTIMATION METHOD AND BY GRAVIMETRIC METHOD

Objectives: The aim of the study was to estimate postpartum blood loss visually and by gravimetric method and compare the both. Methods: This study was conducted in the Department of Obstetrics and Gynaecology, GMC Amritsar on 100 pregnant women satisfying the inclusion criteria over a period of 1.5 years. Patient’s consent was taken and visual estimation was done by the attending obstetrician and obstetric nurse. Total blood loss was calculated using gravimetric method and was compared to value given by visual estimation. Furthermore, comparison was done between the visual estimation values of the attending obstetrician and the obstetric nurse. Results: Obstetrician observed 21.47% less blood loss than the actual (by gravimetric method) blood loss. Obstetric nurse observed 20.01% less blood loss than the obstetrician and 37.19% less than the actual loss. Conclusion: Visual estimation underestimates the actual blood loss and, hence, an objective gravimetric method should be used for early and effective management of PPH

A Hybrid Computational Intelligence based Technique for Automatic Cryptanalysis of Playfair Ciphers

The Playfair cipher is a symmetric key cryptosystem-based on encryption of digrams of letters. The cipher shows higher cryptanalytic complexity compared to mono-alphabetic cipher due to the use of 625 different letter-digrams in encryption instead of 26 letters from Roman alphabets. Population-based techniques like Genetic algorithm (GA) and Swarm intelligence (SI) are more suitable compared to the Brute force approach for cryptanalysis of cipher because of specific and unique structure of its Key Table. This work is an attempt to automate the process of cryptanalysis using hybrid computational intelligence. Multiple particle swarm optimization (MPSO) and GA-based hybrid technique (MPSO-GA) have been proposed and applied in solving Playfair ciphers. The authors have attempted to find the solution key applied in generating Playfair crypts by using the proposed hybrid technique to reduce the exhaustive search space. As per the computed results of the MPSO-GA technique, correct solution was obtained for the Playfair ciphers of 100 to 200 letters length. The proposed technique provided better results compared to either GA or PSO-based technique. Furthermore, the technique was also able to recover partial English text message for short Playfair ciphers of 80 to 120 characters length

Role of norepinephrine in the regulation of rapid eye movement sleep

Sleep and wakefulness are instinctive behaviours that are present across the animal species. Rapid eye movement (REM) sleep is a unique biological phenomenon expressed during sleep. It evolved about 300 million years ago and is noticed in the more evolved animal species. Although it has been objectively identified in its present characteristic form about half a century ago, the mechanics of how REM is generated, and what happens upon its loss are not known. Nevertheless, extensive research has shown that norepinephrine plays a crucial role in its regulation. The present knowledge that has been reviewed in this manuscript suggests that neurons in the brain stem are responsible for controlling this state and presence of excess norepinephrine in the brain does not allow its generation. Furthermore, REM sleep loss increases levels of norepinephrine in the brain that affects several factors including an increase in Na-K ATPase activity. It has been argued that such increased norepinephrine is ultimately responsible for REM sleep deprivation, associated disturbances in at least some of the physiological conditions leading to alteration in behavioural expression and settling into pathological conditions

Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition

Meta-learning is critical for a variety of practical ML systems -- like personalized recommendations systems -- that are required to generalize to new tasks despite a small number of task-specific training points. Existing meta-learning techniques use two complementary approaches of either learning a low-dimensional representation of points for all tasks, or task-specific fine-tuning of a global model trained using all the tasks. In this work, we propose a novel meta-learning framework that combines both the techniques to enable handling of a large number of data-starved tasks. Our framework models network weights as a sum of low-rank and sparse matrices. This allows us to capture information from multiple domains together in the low-rank part while still allowing task specific personalization using the sparse part. We instantiate and study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements. We propose an alternating minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part effectively and efficiently. For the realizable, Gaussian data setting, we show that AMHT-LRS indeed solves the problem efficiently with nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy of each individual user in the dataset, while still ensuring strong generalization with nearly optimal number of samples. Finally, on multiple datasets, we demonstrate that the framework allows personalized models to obtain superior performance in the data-scarce regime.Comment: 97 pages, 3 figure

A Phosphoproteomics Study of the Soybean root necrosis 1 Mutant Revealed Type II Metacaspases Involved in Cell Death Pathway

The soybean root necrosis 1 (rn1) mutation causes progressive browning of the roots soon after germination and provides increased tolerance to the soil-borne oomycete pathogen Phytophthora sojae in soybean. Toward understanding the molecular basis of the rn1 mutant phenotypes, we conducted tandem mass tag (TMT)-labeling proteomics and phosphoproteomics analyses of the root tissues of the rn1 mutant and progenitor T322 line to identify potential proteins involved in manifestation of the mutant phenotype. We identified 3,160 proteins. When the p-value was set at ≤0.05 and the fold change of protein accumulation between rn1 and T322 at ≥1.5 or ≤0.67, we detected 118 proteins that showed increased levels and 32 proteins decreased levels in rn1 as compared to that in T322. The differentially accumulated proteins (DAPs) are involved in several pathways including cellular processes for processing environmental and genetic information, metabolism and organismal systems. Five pathogenesis-related proteins were accumulated to higher levels in the mutant as compared to that in T322. Several of the DAPs are involved in hormone signaling, redox reaction, signal transduction, and cell wall modification processes activated in plant–pathogen interactions. The phosphoproteomics analysis identified 22 phosphopeptides, the levels of phosphorylation of which were significantly different between rn1 and T322 lines. The phosphorylation levels of two type II metacaspases were reduced in rn1 as compared to T322. Type II metacaspase has been shown to be a negative regulator of hypersensitive cell death. In absence of the functional Rn1 protein, two type II metacaspases exhibited reduced phosphorylation levels and failed to show negative regulatory cell death function in the soybean rn1 mutant. We hypothesize that Rn1 directly or indirectly phosphorylates type II metacaspases to negatively regulate the cell death process in soybean roots