Properties of the phage-shock-protein (Psp) regulatory complex that govern signal transduction and induction of the Psp response in Escherichia coli

The phage-shock-protein (Psp) response maintains the proton-motive force (pmf) under extracytoplasmic stress conditions that impair the inner membrane (IM) in bacterial cells. In Escherichia coli transcription of the pspABCDE and pspG genes requires activation of σ54-RNA polymerase by the enhancer-binding protein PspF. A regulatory network comprising PspF–A–C–B–ArcB controls psp expression. One key regulatory point is the negative control of PspF imposed by its binding to PspA. It has been proposed that under stress conditions, the IM-bound sensors PspB and PspC receive and transduce the signal(s) to PspA via protein–protein interactions, resulting in the release of the PspA–PspF inhibitory complex and the consequent induction of psp. In this work we demonstrate that PspB self-associates and interacts with PspC via putative IM regions. We present evidence suggesting that PspC has two topologies and that conserved residue G48 and the putative leucine zipper motif are determinants required for PspA interaction and signal transduction upon stress. We also establish that PspC directly interacts with the effector PspG, and show that PspG self-associates. These results are discussed in the context of formation and function of the Psp regulatory complex

The personal and contextual contributors to school belongingness among primary school students

School belongingness has gained currency among educators and school health professionals as an important determinant of adolescent health. The current cross-sectional study presents the 15 most significant personal and contextual factors that collectively explain 66.4% (two-thirds) of the variability in 12-year old students' perceptions of belongingness in primary school. The study is part of a larger longitudinal study investigating the factors associated with student adjustment in the transition from primary to secondary school. The study found that girls and students with disabilities had higher school belongingness scores than boys, and their typically developing counterparts respectively; and explained 2.5% of the variability in school belongingness. The majority (47.1% out of 66.4%) of the variability in school belongingness was explained by student personal factors, such as social acceptance, physical appearance competence, coping skills, and social affiliation motivation; followed by parental expectations (3% out of 66.4%), and school-based factors (13.9% out of 66.4%) such as, classroom involvement, task-goal structure, autonomy provision, cultural pluralism, and absence of bullying. Each of the identified contributors of primary school belongingness can be shaped through interventions, system changes, or policy reforms

Validating layer-specific VASO across species

Cerebral blood volume (CBV) has been shown to be a robust and important physiological parameter for quantitative interpretation of functional (f)MRI, capable of delivering highly localized mapping of neural activity. Indeed, with recent advances in ultra-high-field (≥7T) MRI hardware and associated sequence libraries, it has become possible to capture non-invasive CBV weighted fMRI signals across cortical layers. One of the most widely used approaches to achieve this (in humans) is through vascular-space-occupancy (VASO) fMRI. Unfortunately, the exact contrast mechanisms of layer-dependent VASO fMRI have not been validated for human fMRI and thus interpretation of such data is confounded. Here we validate the signal source of layer-dependent SS-SI VASO fMRI using multi-modal imaging in a rat model in response to neuronal activation (somatosensory cortex) and respiratory challenge (hypercapnia). In particular VASO derived CBV measures are directly compared to concurrent measures of total haemoglobin changes from high resolution intrinsic optical imaging spectroscopy (OIS). Quantified cortical layer profiling is demonstrated to be in agreement between VASO and contrast enhanced fMRI (using monocrystalline iron oxide nanoparticles, MION). Responses show high spatial localisation to layers of cortical processing independent of confounding large draining veins which can hamper BOLD fMRI studies, (depending on slice positioning). Thus, a cross species comparison is enabled using VASO as a common measure. We find increased VASO based CBV reactivity (3.1 ± 1.2 fold increase) in humans compared to rats. Together, our findings confirm that the VASO contrast is indeed a reliable estimate of layer-specific CBV changes. This validation study increases the neuronal interpretability of human layer-dependent VASO fMRI as an appropriate method in neuroscience application studies, in which the presence of large draining intracortical and pial veins limits neuroscientific inference with BOLD fMRI

TXS 0506+056 with Updated IceCube Data

Past results from the IceCube Collaboration have suggested that the blazar TXS 0506+056 is a potential source of astrophysical neutrinos. However, in the years since there have been numerous updates to event processing and reconstruction, as well as improvements to the statistical methods used to search for astrophysical neutrino sources. These improvements in combination with additional years of data have resulted in the identification of NGC 1068 as a second neutrino source candidate. This talk will re-examine time-dependent neutrino emission from TXS 0506+056 using the most recent northern-sky data sample that was used in the analysis of NGC 1068. The results of using this updated data sample to obtain a significance and flux fit for the 2014 TXS 0506+056 "untriggered" neutrino flare are reported

Galactic Core-Collapse Supernovae at IceCube: “Fire Drill” Data Challenges and follow-up

The next Galactic core-collapse supernova (CCSN) presents a once-in-a-lifetime opportunity to make astrophysical measurements using neutrinos, gravitational waves, and electromagnetic radiation. CCSNe local to the Milky Way are extremely rare, so it is paramount that detectors are prepared to observe the signal when it arrives. The IceCube Neutrino Observatory, a gigaton water Cherenkov detector below the South Pole, is sensitive to the burst of neutrinos released by a Galactic CCSN at a level >10σ. This burst of neutrinos precedes optical emission by hours to days, enabling neutrinos to serve as an early warning for follow-up observation. IceCube\u27s detection capabilities make it a cornerstone of the global network of neutrino detectors monitoring for Galactic CCSNe, the SuperNova Early Warning System (SNEWS 2.0). In this contribution, we describe IceCube\u27s sensitivity to Galactic CCSNe and strategies for operational readiness, including "fire drill" data challenges. We also discuss coordination with SNEWS 2.0

All-Energy Search for Solar Atmospheric Neutrinos with IceCube

The interaction of cosmic rays with the solar atmosphere generates a secondary flux of mesons that decay into photons and neutrinos – the so-called solar atmospheric flux. Although the gamma-ray component of this flux has been observed in Fermi-LAT and HAWC Observatory data, the neutrino component remains undetected. The energy distribution of those neutrinos follows a soft spectrum that extends from the GeV to the multi-TeV range, making large Cherenkov neutrino telescopes a suitable for probing this flux. In this contribution, we will discuss current progress of a search for the solar neutrino flux by the IceCube Neutrino Observatory using all available data since 2011. Compared to the previous analysis which considered only high-energy muon neutrino tracks, we will additionally consider events produced by all flavors of neutrinos down to GeV-scale energies. These new events should improve our analysis sensitivity since the flux falls quickly with energy. Determining the magnitude of the neutrino flux is essential, since it is an irreducible background to indirect solar dark matter searches

Recent neutrino oscillation results with the IceCube experiment

The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube’s primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes it possible to lower the energy threshold of IceCube and observe GeV-scale neutrinos, opening the window to atmospheric neutrino oscillations studies. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector. In this contribution, the recent IceCube result from the atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample are presented and compared to the existing worldwide measurements

Angular dependence of the atmospheric neutrino flux with IceCube data

IceCube Neutrino Observatory, the cubic kilometer detector embedded in ice of the geographic South Pole, is capable of detecting particles from several GeV up to PeV energies enabling precise neutrino spectrum measurement. The diffuse neutrino flux can be subdivided into three components: astrophysical, from extraterrestrial sources; conventional, from pion and kaon decays in atmospheric Cosmic Ray cascades; and the yet undetected prompt component from the decay of charmed hadrons. A particular focus of this work is to test the predicted angular dependence of the atmospheric neutrino flux using an unfolding method. Unfolding is a set of methods aimed at determining a value from related quantities in a model-independent way, eliminating the influence of several assumptions made in the process. In this work, we unfold the muon neutrino energy spectrum and employ a novel technique for rebinning the observable space to ensure sufficient event numbers within the low statistic region at the highest energies. We present the unfolded energy and zenith angle spectrum reconstructed from IceCube data and compare the result with model expectations and previous measurements