662 research outputs found
Reviewing the prospect of fermion triplets as dark matter and source of baryon asymmetry in non-standard cosmology
No full textIndirect searches of Dark Matter (DM), in conjugation with `missing track
searches' at the collider seem to confine SU(2) fermion triplet DM (FTDM)
mass within a narrow range around 1 TeV. The canonical picture of the pure FTDM
is in tension since it is under-abundant for the said mass range. Several
preceding studies have reported that an extra species (), redshifts
faster than the radiation ( where ), leads to a faster
expanding early Universe by dominating in the energy density with an enhanced
Hubble parameter. This has the potential to revive the under-abundant FTDM
( odd, lightest generation) by causing freeze-out earlier without
modifying the interaction strength between DM and thermal bath. On the other
hand, although the CP asymmetry produced due to the decay of
even heavier generations of the triplet remains unaffected, its evolution is
greatly affected by the non-standard cosmology. It has been observed through
numerical estimations that the minimum mass of the triplet, required to produce
sufficient baryon asymmetry of the Universe (BAU), can be lowered up to two
orders (compared to the standard cosmology) in this fast expansion scenario.
The non-standard parameters and (a reference temperature below which
radiation dominance prevails), which simultaneously control DM abundance as
well as the frozen value of BAU, are tightly constrained from the observed
experimental values. We have found that is strictly bounded within the
interval where the upper bound is imposed by the
BAU constraint whereas the lower bound arises to satisfy the correct DM
abundance. It has been noticed that the restriction on is not so
stringent as it can vary from sub-GeV to a few tens of GeV.Comment: 40 pages, 10 figures, 1 table, minor changes, version published in
JCA
Environmental toxicity, redox signaling and lung inflammation:the role of glutathione
No full textGlutathione (gamma-glutamyl-cysteinyl-glycine, GSH) is the most abundant intracellular antioxidant thiol and is central to redox defense during oxidative stress. GSH metabolism is tightly regulated and has been implicated in redox signaling and also in protection against environmental oxidant-mediated injury. Changes in the ratio of the reduced and disulfide form (GSH/GSSG) can affect signaling pathways that participate in a broad array of physiological responses from cell proliferation, autophagy and apoptosis to gene expression that involve H(2)O(2) as a second messenger. Oxidative stress due to oxidant/antioxidant imbalance and also due to environmental oxidants is an important component during inflammation and respiratory diseases such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, acute respiratory distress syndrome, and asthma. It is known to activate multiple stress kinase pathways and redox-sensitive transcription factors such as Nrf2, NF-kappaB and AP-1, which differentially regulate the genes for pro-inflammatory cytokines as well as the protective antioxidant genes. Understanding the regulatory mechanisms for the induction of antioxidants, such as GSH, versus pro-inflammatory mediators at sites of oxidant-directed injuries may allow for the development of novel therapies which will allow pharmacological manipulation of GSH synthesis during inflammation and oxidative injury. This article features the current knowledge about the role of GSH in redox signaling, GSH biosynthesis and particularly the regulation of transcription factor Nrf2 by GSH and downstream signaling during oxidative stress and inflammation in various pulmonary diseases. We also discussed the current therapeutic clinical trials using GSH and other thiol compounds, such as N-acetyl-l-cysteine, fudosteine, carbocysteine, erdosteine in environment-induced airways disease
Biswas-Milovic model and its optical solitons
No full textInternational Conference on Numerical Analysis and Applied Mathematics 2018, ICNAAM 2018 -- 13 September 2018 through 18 September 2018 -- -- 149843In this work, optical solitons are obtained for the Biswas - Milovic equation as a generalized model via the extended generalizing Riccati mapping method. This method reveals several optical solitons including traveling wave solutions. The found solutions are identified with two different forms including the hyperbolic functions, the rational functions and the trigonometric functions. Reliability of our solution is given graphical consequens. © 2019 Author(s)
Author Correction to: Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization
No full textThe original version of this article unfortunately contained a mistake. The family name of Rui Tato Marinho was incorrectly spelled as Marinhoe
On connections on principal bundles
No full textAbstractA new construction of a universal connection was given in Biswas, Hurtubise and Stasheff (2012). The main aim here is to explain this construction. A theorem of Atiyah and Weil says that a holomorphic vector bundle E over a compact Riemann surface admits a holomorphic connection if and only if the degree of every direct summand of E is zero. In Azad and Biswas (2002), this criterion was generalized to principal bundles on compact Riemann surfaces. This criterion for principal bundles is also explained
Equivariant principal bundles on toric varieties.
No full textWe classify holomorphic as well as algebraic -equivariant principal -bundles over a nonsingular toric variety , where is a complex linear algebraic group. We will also see that any algebraic principal -bundle on is -equivariant iff admits a logarithmic connection singular over . This is a joint work with Indranil Biswas and Mainak Poddar.Non UBCUnreviewedAuthor affiliation: IIT-MadrasFacult
-Connections on principal bundles over complete -varieties
No full textLet be a complete variety over an algebraically closed field of
characteristic zero, equipped with an action of an algebraic group . Let
be a reductive group. We study the notion of -connection on a principal
-bundle. We give necessary and sufficient criteria for the existence of
-connections extending the Atiyah-Weil type criterion for holomorphic
connections obtained by Azad and Biswas. We also establish a relationship
between the existence of -connection and equivariant structure on a
principal -bundle, under the assumption that is semisimple and simply
connected. These results have been obtained by Biswas et al. when the
underlying variety is smooth.Comment: 23 page
Satellite-retrieved direct radiative forcing of aerosols over North-East India and adjoining areas: climatology and impact assessment
No full textThe article by J. Biswas et al. contained an update in affiliation of author Binita Pathak. The author would like to add another affiliation to her name. Her updated affiliations are the following
Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk
No full textManual ultrasound (US)-based methods are adapted for lumen diameter (LD) measurement to estimate the risk of stroke but they are tedious, error prone, and subjective causing variability. We propose an automated deep learning (DL)-based system for lumen detection. The system consists of a combination of two DL systems: encoder and decoder for lumen segmentation. The encoder employs a 13-layer convolution neural network model (CNN) for rich feature extraction. The decoder employs three up-sample layers of fully convolution network (FCN) for lumen segmentation. Three sets of manual tracings were used during the training paradigm leading to the design of three DL systems. Cross-validation protocol was implemented for all three DL systems. Using the polyline distance metric, the precision of merit for three DL systems over 407 US scans was 99.61%, 97.75%, and 99.89%, respectively. The Jaccard index and Dice similarity of DL lumen segmented region against three ground truth (GT) regions were 0.94, 0.94, and 0.93 and 0.97, 0.97, and 0.97, respectively. The corresponding AUC for three DL systems was 0.95, 0.91, and 0.93. The experimental results demonstrated superior performance of proposed deep learning system over conventional methods in literature. [Figure not available: see fulltext.]
Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization
No full textFatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (KÂ =Â 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%
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