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In recent years, the formation of primordial black holes (PBH) in the early universe inflationary cosmology has garnered significant attention. One plausible scenario for primordial black hole (PBH) formation arises during the preheating stage following inflation. Notably, this scenario does not necessitate any ad-hoc fine-tuning of the scalar field potential. This paper focuses on the growth of primordial density perturbation and the consequent possibility of PBH formation in the preheating stage of the Starobinsky model for inflation. The typical mechanism for PBH formation during preheating is based on the collapse of primordial fluctuations that become super-horizon during inflation (type I) and re-enter the particle horizon in the different phases of cosmic expansion. In this work, we show that there exists a certain range of modes that remain in the sub-horizon (not exited) during inflation (type II modes) but evolve identically to type I modes if they fall into the instability band, leading to large density perturbation above the threshold and can potentially also contribute to the PBH formation. We outline the conditions that govern the potential collapse of typeI and type II modes with wavelengths exceeding the Jeans length,which we derive based on the effective sound speed of scalar fieldfluctuations. Since the preheating stage is an `inflaton' (approximately) matter-dominated phase, we follow the framework of the critical collapse of fluctuations and compute the mass fraction using the well-known Press-Schechter and the Khlopov-Polnarev formalisms, and compare the two. Finally, we comment on the implications of our study for the investigations concerned with primordial accretion and consequent PBH contribution to the dark matter. 

Ferritin is a 24-mer protein nanocage that stores iron and regulates intracellular iron homeostasis. The nuclear receptor coactivator-4 (NCOA4) binds specifically to ferritin H subunits and facilitates the autophagic trafficking of ferritin to the lysosome for degradation and iron release. Using isothermal titration calorimetry (ITC), we studied the thermodynamics of the interactions between ferritin and the soluble fragment of NCOA4 (residues 383–522), focusing on the effects of the recently identified Fe–S cluster bound to NCOA4, ferritin subunit composition, and ferritin-iron loading. Our findings show that in the presence of the Fe–S cluster, the binding is driven by a more favorable enthalpy change and a decrease in entropy change, indicating a key role for the Fe–S cluster in the structural organization and stability of the complex. The ferritin iron core further enhances this association, increasing binding enthalpy and stabilizing the NCOA4-ferritin complex. The ferritin subunit composition primarily affects binding stoichiometry of the reaction based on the number of H subunits in the ferritin H/L oligomer. Our results demonstrate that both the Fe–S cluster and the ferritin iron core significantly affect the binding thermodynamics of the NCOA4-ferritin interactions, suggesting regulatory roles for the Fe–S cluster and ferritin iron content in ferritinophagy. 

Hydrogen spillover involves the migration of H atom equivalents from metal nanoparticles to a support. While well documented, H spillover is poorly understood and largely unquantified. Here we measure weak, reversible H2 adsorption on Au/TiO2 catalysts, and extract the surface concentration of spilled-over hydrogen. The spillover species (H*) is best described as a loosely coupled proton/electron pair distributed across the titania surface hydroxyls. In stark contrast to traditional gas adsorption systems, H* adsorption increases with temperature. This unexpected adsorption behaviour has two origins. First, entropically favourable adsorption results from high proton mobility and configurational surface entropy. Second, the number of spillover sites increases with temperature, due to increasing hydroxyl acid–base equilibrium constants. Increased H* adsorption correlates with the associated changes in titania surface zwitterion concentration. This study provides a quantitative assessment of how hydroxyl surface chemistry impacts spillover thermodynamics, and contributes to the general understanding of spillover phenomena. 

We report a new measurement of flux-integrated differential cross sections for charged-current (CC) muon neutrino interactions with argon nuclei that produce no final-state pions ( ${\nu }_{\mu }\mathrm{CC}0\pi$). These interactions are of particular importance as a topologically defined signal dominated by quasielasticlike interactions. This measurement was performed with the MicroBooNE liquid argon time projection chamber detector located at the Fermilab Booster Neutrino Beam and uses an exposure of $1.3\times {10}^{21}$protons on target collected between 2015 and 2020. The results are presented in terms of single- and double-differential cross sections as a function of the final-state muon momentum and angle. The data are compared with widely used neutrino event generators. We find good agreement with the single-differential measurements, while only a subset of generators are also able to adequately describe the data in double-differential distributions. This work facilitates comparison with Cherenkov detector measurements, including those located at the Booster Neutrino Beam. 

Abstract Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers’ cutoffs established on the basis of severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India, from January to May 2021. Samples were tested for SARS-CoV-2 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence of 64.9% (95% credible interval (CrI): 63.8, 66.0) and 51.5% (95% CrI: 50.2, 52.9), respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cutoffs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted. 

The emergence of Audio-based Augmented Reality has been calling for increasing data-rates for audio signals, with significant reduction in power to enable extremely energy-constrained sensor nodes. Typically, the communication power dominates sensing and computing power in a node [1]. For highly energy constrained scenarios, compressive sensing (CS) have been demonstrated (Fig. 1), where samples are first compressed at the sensor to contain the same information in a smaller number of samples, before transmitting to a receiver, where the signal is reconstructed. Previous CS works [2]-[5] have focused entirely on “sparse” physiological signals, operating in low speed regime. This work illustrates the first CS design, enabled with a discrete wavelet transform (DWT) sparsifier for catering to non-sparse signals such as high definition audio. Audio recording and playback are quite sensitive to quality, thereby requiring audio codecs, such as. aac, for efficient compression and decompression of audio streams, which usually consume power in the order of mW [6]. Audio inferencing operated in intelligent assistants are more tolerant to input quality, functioning effectively when the Perceptual Evaluation of Audio Quality Mean Opinion Score (PAEQ MOS) [7], an ITU-R standard objective metric for characterizing perceived audio quality, exceeds 1.5. CS presents an opportunity to achieve >10X reduction in transmitted audio data with orders of magnitude lower power, as compared to codecs. The design is implemented in 65 nm CMOS and consumes 238 uW power at 0.65 V and 15 Mbps.