Impact of dark matter models on the EoR 21-cm signal bispectrum

The nature of dark matter sets the timeline for the formation of first collapsed haloes and thus affects the sources of reionization. Here, we consider two different models of dark matter: cold dark matter (CDM) and thermal warm dark matter (WDM), and study how they impact the epoch of reionization (EoR) and its 21-cm observables. Using a suite of simulations, we find that in WDM scenarios, the structure formation on small scales gets suppressed, resulting in a smaller number of low-mass dark matter haloes compared to the CDM scenario. Assuming that the efficiency of sources in producing ionizing photons remains the same, this leads to a lower number of total ionizing photons produced at any given cosmic time, thus causing a delay in the reionization process. We also find visual differences in the neutral hydrogen (H I) topology and in 21-cm maps in case of the WDM compared to the CDM. However, differences in the 21-cm power spectra, at the same neutral fraction, are found to be small. Thus, we focus on the non-Gaussianity in the EoR 21-cm signal, quantified through its bispectrum. We find that the 21-cm bispectra (driven by the HI topology) are significantly different in WDM models compared to the CDM, even for the same mass-averaged neutral fractions. This establishes that the 21-cm bispectrum is a unique and promising way to differentiate between dark matter models, and can be used to constrain the nature of the dark matter in the future EoR observations

Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation

Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the universe during this time. However, modeling the evolution of these epochs is particularly challenging due to the complex interplay of many physical processes. This makes it difficult to perform the conventional statistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC) methods, which scales poorly with the dimensionality of the parameter space. In this paper, we show how the Simulation-Based Inference (SBI) through Marginal Neural Ratio Estimation (MNRE) provides a step towards evading these issues. We use 21cmFAST to model the 21-cm power spectrum during CD-EoR with a six-dimensional parameter space. With the expected thermal noise from the Square Kilometre Array (SKA), we are able to accurately recover the posterior distribution for the parameters of our model at a significantly lower computational cost than the conventional likelihood-based methods. We further show how the same training dataset can be utilized to investigate the sensitivity of the model parameters over different redshifts. Our results support that such efficient and scalable inference techniques enable us to significantly extend the modeling complexity beyond what is currently achievable with conventional MCMC methods.Comment: 15 pages, 9 figures. Accepted for publication in MNRA

Sky-averaged 21-cm signal extraction using multiple antennas with an SVD framework: the REACH case

In a sky-averaged 21-cm signal experiment, the uncertainty on the extracted signal depends mainly on the covariance between the foreground and 21-cm signal models. In this paper, we construct these models using the modes of variation obtained from the Singular Value Decomposition of a set of simulated foreground and 21-cm signals. We present a strategy to reduce this overlap between the 21-cm and foreground modes by simultaneously fitting the spectra from multiple different antennas, which can be used in combination with the method of utilizing the time dependence of foregrounds while fitting multiple drift scan spectra. To demonstrate this idea, we consider two different foreground models (i) a simple foreground model, where we assume a constant spectral index over the sky, and (ii) a more realistic foreground model, with a spatial variation of the spectral index. For the simple foreground model, with just a single antenna design, we are able to extract the signal with good accuracy if we simultaneously fit the data from multiple time slices. The 21-cm signal extraction is further improved when we simultaneously fit the data from different antennas as well. This improvement becomes more pronounced while using the more realistic mock observations generated from the detailed foreground model. We find that even if we fit multiple time slices, the recovered signal is biased and inaccurate for a single antenna. However, simultaneously fitting the data from different antennas reduces the bias and the uncertainty by a factor of 2-3 on the extracted 21-cm signal.Comment: 12 pages, 13 figures. Accepted for publication in MNRAS. Accompanying code is available https://github.com/anchal-009/SAVED21c

Nanostructured palladium-reduced graphene oxide platform for high sensitive, label free detection of a cancer biomarker

We report the results of studies related to the fabrication of a palladium nanoparticle decorated-reduced graphene oxide (Pd@rGO) based electrochemical immunosensor for the label free ultrasensitive detection of the prostate-specific antigen (PSA), a prostate cancer biomarker. The synergistic electrochemical activities of Pd and rGO result in an enhanced electron transfer used for the development of an ultrasensitive immunosensor. A facile approach was developed for the in situ synthesis of Pd@rGO using ascorbic acid as the reducing agent which enables the simultaneous reduction of both Pd+2 and GO into Pd nanoparticles and rGO, respectively. XRD, FTIR, SEM and TEM investigations were carried out to characterize the Pd@rGO material. A thin film of nanostructured Pd@rGO was electrophoretically deposited on an ITO coated glass electrode that was subsequently functionalized with anti-PSA antibodies. The electrochemical sensing results of the proposed immunosensor showed a high sensitivity {28.96 mu A ml ng(-1) cm(-2)}. The immunosensor is able to detect PSA at concentrations as low as 10 pg ml(-1). The simple fabrication method, high sensitivity, good reproducibility and long term stability with acceptable accuracy in human serum samples are the main advantages of this immunosensor

Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation

Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the Universe during this time. However, modelling the evolution of these epochs is particularly challenging due to the complex interplay of many physical processes. This makes it difficult to perform the conventional statistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC) methods, which scales poorly with the dimensionality of the parameter space. In this paper, we show how the Simulation-Based Inference through Marginal Neural Ratio Estimation (MNRE) provides a step towards evading these issues. We use 21cmFAST to model the 21-cm power spectrum during CD–EoR with a six-dimensional parameter space. With the expected thermal noise from the Square Kilometre Array, we are able to accurately recover the posterior distribution for the parameters of our model at a significantly lower computational cost than the conventional likelihood-based methods. We further show how the same training data set can be utilized to investigate the sensitivity of the model parameters over different redshifts. Our results support that such efficient and scalable inference techniques enable us to significantly extend the modelling complexity beyond what is currently achievable with conventional MCMC methods

De novo assembly, functional annotation and comparative alignment of whole genome of a halo-tolerant Exiguobacterium profundum PHM11 with related genomes

Advances in the next-generation sequencing (NGS) technologies have invigorated the exploration of microbial genomes in retrieving the hidden traits. In this study, high-throughput next generation whole genome sequencing of a halotolerant E. profundum PHM11 was performed on Illumina HiSeq paired end sequencing plateform and assembled through de novo Linux based approaches using Velvet (V 1.2.10.) algorithm package. Quality filtering in de novo sequencing produced 72,947,390 reads with total size of 7335195362 bp (7335.1 Mb). High quality reads having minimum and maximum contig lengths of 202 and 958471 (~0.95 Mb) bp were further considered for assembly. Final PHM11 genome has a size of ~2.92 Mb comprising 70 contigs, 47.93% G+C content with 761858 (26.08%), 757313 (25.92%), 699924 (23.96%), and 700172 (23.97%) percentages of adenine, thymine, cytosine and guanine nucleotides. Throughout micro-satellite mining of genome showed a total of 3005 SSRs, covering 0.1 of whole PHM11 genome, with relative abundance; 1029, relative density; 10951, and percentages of penta repeats; 65.75, hexa repeats; 28.75, mono repeats; 3.89, and tetra-repeats; 1.59, respectively. Gene networks related to the arrangement of key genes and presence of lysogenic phage DNA were reflected through generating the chromosome map of PHM11 genome. Functional annotations of genome reflected the different protein families, and hidden inherent metabolic pathways providing unusual features. A total of 3033 protein coding genes and 33 non-protein coding genes were identified; out of these only 2316 could be characterized and 737 were reported as hypothetical. Random genes of different metabolic pathways were amplified from its genome and authenticated through their sequencing. Genome-rearrangements in PHM11 could be deciphered through aligning its genome with thirteen other genomes of different Exiguobacterium species

Rottenstone - Fresh Metaseds. - Pegmatite

N wall of Bennett Quarry. View to the N.https://digitalmaine.com/mgs_geologic_field_photos/6801/thumbnail.jp

Halotolerant Exiguobacterium profundum PHM11 Tolerate Salinity by Accumulating L-Proline and Fine-Tuning Gene Expression Profiles of Related Metabolic Pathways

Salinity stress is one of the serious factors, limiting production of major agricultural crops; especially, in sodic soils. A number of approaches are being applied to mitigate the salt-induced adverse effects in agricultural crops through implying different halotolerant microbes. In this aspect, a halotolerant, Exiguobacterium profundum PHM11 was evaluated under eight different salinity regimes; 100, 250, 500, 1000, 1500, 2000, 2500, and 3000 mM to know its inherent salt tolerance limits and salt-induced consequences affecting its natural metabolism. Based on the stoichiometric growth kinetics; 100 and 1500 mM concentrations were selected as optimal and minimal performance limits for PHM11. To know, how salt stress affects the expression profiles of regulatory genes of its key metabolic pathways, and total production of important metabolites; biomass, carotenoids, beta-carotene production, IAA and proline contents, and expression profiles of key genes affecting the protein folding, structural adaptations, transportation across the cell membrane, stress tolerance, carotenoids, IAA and mannitol production in PHM11 were studied under 100 and 1500 mM salinity. E. profundum PHM11 showed maximum and minimum growth, biomass and metabolite production at 100 and 1500 mM salinity respectively. Salt-induced fine-tuning of expression profiles of key genes of stress pathways was determined in halotolerant bacterium PHM11

Partially reduced graphene oxide-gold nanorods composite based bioelectrode of improved sensing performance

The present work proposes partially reduced graphene oxide-gold nanorods supported by chitosan (CH-prGO-AuNRs) as a potential bioelectrode material for enhanced glucose sensing. Developed on ITO substrate by immobilizing glucose oxidase on CH-prGO-AuNRs composite, these CH-prGO-AuNRs/ITO bioelectrodes demonstrate high sensitivity of 3.2 mu A/(mg/dL)/cm(2) and linear range of 25-200 mg/dL with an ability to detect as low as 14.5 mg/dL. Further, these CH-prGO-AuNRs/ITO based electrodes attest synergistiacally enhanced sensing properties when compared to simple graphene oxide based CH-GO/ITO electrode. This is evident from one order higher electron transfer rate constant (K-s) value in case of CH-prGO-AuNRs modified electrode (12.4 x 10(-2) cm/s), in contrast to CH-GO/ITO electrode (6 x 10(-3) cm/s). Additionally, very low K-m value [15.4 mg/dL(0.85 mM)] ensures better binding affinity of enzyme to substrate which is desirable for good biosensor stability and resistance to environmental interferences. Hence, with better loading capacity, kinetics and stability, the proposed CH-prGO-AuNRs composite shows tremendous potential to detect several bio-analytes in the coming future