The effect of non-Gaussianity on error predictions for the Epoch of Reionization (EoR) 21-cm power spectrum

The Epoch of Reionization (EoR) 21-cm signal is expected to become increasingly non-Gaussian as reionization proceeds. We have used semi-numerical simulations to study how this affects the error predictions for the EoR 21-cm power spectrum. We expect $SNR=\sqrt{N_k}$ for a Gaussian random field where $N_k$ is the number of Fourier modes in each $k$ bin. We find that non-Gaussianity is important at high $SNR$ where it imposes an upper limit $[SNR]_l$. For a fixed volume $V$, it is not possible to achieve $SNR > [SNR]_l$ even if $N_k$ is increased. The value of $[SNR]_l$ falls as reionization proceeds, dropping from $\sim 500$ at $\bar{x}_{HI} = 0.8-0.9$ to $\sim 10$ at $\bar{x}_{HI} = 0.15$ for a $[150.08\, {\rm Mpc}]^3$ simulation. We show that it is possible to interpret $[SNR]_l$ in terms of the trispectrum, and we expect $[SNR]_l \propto \sqrt{V}$ if the volume is increased. For $SNR \ll [SNR]_l$ we find $SNR = \sqrt{N_k}/A$ with $A \sim 0.95 - 1.75$, roughly consistent with the Gaussian prediction. We present a fitting formula for the $SNR$ as a function of $N_k$, with two parameters $A$ and $[SNR]_l$ that have to be determined using simulations. Our results are relevant for predicting the sensitivity of different instruments to measure the EoR 21-cm power spectrum, which till date have been largely based on the Gaussian assumption.Comment: Accepted for publication in MNRAS Letters. The definitive version is available at http://mnrasl.oxfordjournals.org/content/449/1/L4

Characterizing the Spectral Structure of Weak Impulsive Narrowband Quiet Sun Emissions

Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs) are a newly discovered class of radio emission from the solar corona. These emissions are characterized by their extremely impulsive, narrowband and ubiquitous nature. We have systematically been working on their detailed characterization, including their strengths, morphologies, temporal characteristics, energies, etc. This work is the next step in this series and focuses on the spectral nature of WINQSEs. Given that their strength is only a few percent of the background solar emission, we have adopted an extremely conservative approach to reliably identify WINQSES. Only a handful of WINQSEs meet all of our stringent criteria. Their flux densities lie in the 20 $-$ 50 Jy range and they have compact morphologies. For the first time, we estimate their bandwidths and find them to be less than 700 kHz, consistent with expectations based on earlier observations. Interestingly, we also find similarities between the spectral nature of WINQSEs and the solar radio spikes. This is consistent with our hypothesis that the WINQSEs are the weaker cousins of the type-III radio bursts and are likely to be the low-frequency radio counterparts of the nanoflares, originally hypothesized as a possible explanation for coronal heating.Comment: Accepted for publication in the Astrophysical Journa

Inflaton phenomenology via reheating in the light of PGWs and latest BICEP/KeckKeck data

We are in the era of precision cosmology which offers us a unique opportunity to investigate beyond standard model physics. Towards this endeavor, inflaton is assumed to be a perfect new physics candidate. In this submission, we explore the phenomenological impact of the latest observation of PLANCK and BICEP/$Keck$ data on the physics of inflation. We particularly study three different models of inflation, namely $\alpha$-attractor E, T, and the minimal plateau model. We further consider two different post-inflationary reheating dynamics driven by inflaton decaying into Bosons and Fermions. Given the latest data in the inflationary $(n_s-r)$ plane, we derive detailed phenomenological constraints on different inflaton parameters and the associated physical quantities, such as inflationary e-folding number, $N_{ k}$, reheating temperatures $T_{\rm re}$. Apart from considering direct observational data, we further incorporate the bounds from primordial gravitational waves (PGWs) and different theoretical constraints. Rather than in the laboratory, our results illustrate the potential of present and future cosmological observations to look for new physics in the sky.Comment: 17 pages, 8 tables and 10 figure

Magnetotransport properties of a twisted bilayer graphene in the presence of external electric and magnetic field

We extensively investigate the electronic and transport properties of a twisted bilayer graphene when subjected to both an external perpendicular electric field and a magnetic field. Using a basic tight-binding model, we show the flat electronic band properties as well as the density of states (DOS), both without and with the applied electric field. In the presence of an electric field, the degeneracy at the Dirac points is lifted where the non-monotonic behavior of the energy gap exists, especially for twist angles below 3$^\circ$. We also study the behavior of the Landau levels (LL) spectra for different twist angles within a very low energy range. These LL spectra get modified under the influence of the external electric field. Moreover, we calculate the dc Hall conductivity ($\sigma_{xy}$) for a very large system using the Kernel Polynomial Method (KPM). Interestingly, $\sigma_{xy}$ makes a transition from a half-integer to an integer quantum Hall effect, \textit{i.e.} the value of $\sigma_{xy}$ shifts from $\pm 4(n+1/2) (2e^2/h)$ ($n$ is an integer) to $\pm 2n (2e^2/h)$ around a small twist angle of $\theta=2.005^\circ$. At this angle, $\sigma_{xy}$ acquires a Hall plateau at zero Fermi energy. However, the behavior of $\sigma_{xy}$ remains unaltered when the system is exposed to the electric field, particularly at the magic angle where the bands in both layers can hybridize and strong interlayer coupling plays a crucial role.Comment: 13 pages, 10 figure

An unsupervised machine learning based algorithm for detecting Weak Impulsive Narrowband Quiet Sun Emissions and characterizing their morphology

The solar corona is extremely dynamic. Every leap in observational capabilities has been accompanied by unexpected revelations of complex dynamic processes. The ever more sensitive instruments now allow us to probe events with increasingly weaker energetics. A recent leap in the low-frequency radio solar imaging ability has led to the discovery of a new class of emissions, namely Weak Impulsive Narrowband Quiet Sun Emissions \citep[WINQSEs;][]{mondal2020}. They are hypothesized to be the radio signatures of coronal nanoflares and could potentially have a bearing on the long standing coronal heating problem. In view of the significance of this discovery, this work has been followed up by multiple independent studies. These include detecting WINQSEs in multiple datasets, using independent detection techniques and software pipelines, and looking for their counterparts at other wavelengths. This work focuses on investigating morphological properties of WINQSEs and also improves upon the methodology used for detecting WINQSEs in earlier works. We present a machine learning based algorithm to detect WINQSEs, classify them based on their morphology and model the isolated ones using 2D Gaussians. We subject multiple datasets to this algorithm to test its veracity. Interestingly, despite the expectations of their arising from intrinsically compact sources, WINQSEs tend to be resolved in our observations. We propose that this angular broadening arises due to coronal scattering. WINQSEs can, hence, provide ubiquitous and ever-present diagnostic of coronal scattering (and, in turn, coronal turbulence) in the quiet sun regions, which has not been possible till date.Comment: Accepted for publication in the Astrophysical Journa

Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies

Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate

Investigating the Health of a Rice Field Ecosystem Using Thermodynamic Extremal Principles

This study investigates the dynamic behaviour of a rice field ecosystem and aims to define its integral features using the stability concept of an ecological goal function. This function is based on the extremal principles of thermodynamics, which assume that certain energetic processes of ecosystems—such as the rate of exergy destruction—are directed by the self-organizing informatics of the systems towards maxima or minima.  In our study, we exploit the availability of substantially long time-series data relating to a rice field ecosystem to gain an evocative understanding of its growth trajectory in light of the thermodynamic principles. We accomplished this by constructing a model based on the STELLA 9.0 software and calculating the extremal values of growth rates (storage) and those of exergy destruction and entropy creation. The results showed that the values of both maximum dissipation and maximum exergy progressed apace with that of maximum storage till the maturation of rice and became stable thereafter, whereas maximum residence time and maximum specific dissipation values initially decreased before their asymptotic rise. A similar pattern was also observed for the maximum specific exergy. However, the maximum power dissipation curve followed a highly fluctuated course before becoming stable on the maturation of rice

Lipocalin 2 Induces Neuroinflammation and Blood-Brain Barrier Dysfunction Through Liver-Brain Axis in Murine Model of Nonalcoholic Steatohepatitis

BACKGROUND: Recent clinical and basic research implicated a strong correlation between NAFLD/NASH phenotypes with ectopic manifestations including neuroinflammation and neurodegeneration, but the mediators and critical pathways involved are not well understood. Lipocalin 2 (Lcn2) is one of the important mediators exclusively produced in the liver and circulation during NASH pathology. METHODS: Using murine model of NASH, we studied the role of Lcn2 as a potent mediator of neuroinflammation and neurodegeneration in NASH pathology via the liver-brain axis. RESULTS: Results showed that high circulatory Lcn2 activated 24p3R (Lipocalin2 receptor) in the brain and induced the release of high mobility group box 1 (HMGB1) preferably from brain cells. Released HMGB1 acted as a preferential ligand to toll-like receptor 4 (TLR4) and induced oxidative stress by activation of NOX-2 signaling involving activated p65 protein of the NF-κB complex. Further, the HMGB1-derived downstream signaling cascade activated NLRP3 inflammasome and release of proinflammatory cytokines IL-6 and IL-1β from brain cells. In addition, to advance our present understanding, in vitro studies were performed in primary brain endothelial cells where results showed high circulatory Lcn2 influenced HMGB1 secretion. Mechanistically, we also showed that elevated Lcn2 level in underlying NASH might be a likely cause for induction of blood-brain barrier dysfunction since the adipokine decreased the expression of tight junction protein Claudin 5 and caused subsequent elevation of pro-inflammatory cytokines IL-6 and IL-1β. CONCLUSION: In conclusion, the NASH-induced brain pathology might be because of increased Lcn2-induced release of HMGB1 and accompanying neuroinflammation

Host Abundance Correlates With Gulf War Illness Symptom Persistence via NLRP3-Mediated Neuroinflammation and Decreased Brain-Derived Neurotrophic Factor

Neurological disorders are commonly reported among veterans who returned from the Gulf war. Veterans who suffer from Gulf War illness (GWI) complain of continued symptom persistence that includes neurological disorders, muscle weakness, headaches, and memory loss, that developed during or shortly after the war. Our recent research showed that chemical exposure associated microbial dysbiosis accompanied by a leaky gut connected the pathologies in the intestine, liver, and brain. However, the mechanisms that caused the symptoms to persist even 30 years after the war remained elusive to investigators. In this study, we used a rodent model of GWI to investigate the persistence of microbiome alterations, resultant chronic inflammation, and its effect on neurotrophic and synaptic plasticity marker BDNF. The results showed that exposure to GW chemicals (the pesticide permethrin and prophylactic drug pyridostigmine bromide) resulted in persistent pathology characterized by the low relative abundance of the probiotic bacteria in the gut, which correlated with high circulatory HMGB1 levels, blood-brain barrier dysfunction, neuroinflammation and lowered neurotrophin BDNF levels. Mechanistically, we used mice lacking the NLRP3 gene to investigate this inflammasome\u27s role in observed pathology. These mice had significantly decreased inflammation and a subsequent increase in BDNF in the frontal cortex. This suggests that a persistently low species abundance of and associated chronic inflammation due to inflammasome activation might be playing a significant role in contributing to chronic neurological problems in GWI. A therapeutic approach with various small molecules that can target both the restoration of a healthy microbiome and decreasing inflammasome activation might have better outcomes in treating GWI symptom persistence

Gut DNA Virome Diversity and Its Association with Host Bacteria Regulate Inflammatory Phenotype and Neuronal Immunotoxicity in Experimental Gulf War Illness

Gulf War illness (GWI) is characterized by the persistence of inflammatory bowel disease, chronic fatigue, neuroinflammation, headache, cognitive impairment, and other medically unexplained conditions. Results using a murine model show that enteric viral populations especially bacteriophages were altered in GWI. The increased viral richness and alpha diversity correlated positively with gut bacterial dysbiosis and proinflammatory cytokines. Altered virome signature in GWI mice also had a concomitant weakening of intestinal epithelial tight junctions with a significant increase in Claudin-2 protein expression and decrease in ZO1 and Occludin mRNA expression. The altered virome signature in GWI, decreased tight junction protein level was followed by the presence an activation of innate immune responses such as increased Toll-like receptor (TLR) signaling pathways. The altered virome diversity had a positive correlation with serum IL-6, IL-1β, and IFN-γ, intestinal inflammation (IFN-γ), and decreased Brain-Derived Neurotrophic Factor (BDNF), a neurogenesis marker. The co-exposure of Gulf War chemical and antibiotic (for gut sterility) or Gulf War chemical and Ribavirin, an antiviral compound to suppress virus alteration in the gut showed significant improvement in epithelial tight junction protein, decreased intestinal-, systemic-, and neuroinflammation. These results showed that the observed enteric viral dysbiosis could activate enteric viral particle-induced innate immune response in GWI and could be a novel therapeutic target in GWI