This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective of the proposed TREEFAM procedure is to quantitatively “filter out” the effects of stimulus unfamiliarity in the estimation of an ultrametric tree. A conditional, alternating maximum likelihood procedure is formulated to simultaneously estimate an ultrametric tree, under the unobserved condition of complete stimulus familiarity, and subject-specific parameters capturing the adjustments due to differential unfamiliarity. We demonstrate the performance of the TREEFAM procedure under a variety of alternative conditions via a modest Monte Carlo experimental study. An empirical application provides evidence that the TREEFAM outperforms traditional models that ignore the effects of unfamiliarity in terms of superior tree recovery and overall goodness-of-fit.

Background: MRI forms an imperative part of the diagnostic and treatment protocol for primary brain tumors and metastasis. Though conventional T1W MRI forms the basis for diagnosis at present, it faces several limitations. Machine learning (ML) algorithms require less expertise and provide better diagnostic accuracy. Methods: A systematic review of PubMed, Google Scholar, and Cochrane databases along with registries through 1980-2021 was done. Original articles in English evaluating Conventional MRI or ML algorithms. Data was extracted by 2 reviewers and meta-analysis was performed using bivariate regression model. Results: The study protocol was registered under PROSPERO. Twelve studies with 1247 participants were included for systematic analysis and three studies for meta-analysis. ML algorithms had better aggregate sensitivity and specificity (80%, 83.14%) than Conventional MRI (81.84%, 74.78%).The pooled sensitivity, specificity, DOR for the studies were 0.926 (95% CI, 0.840-0.926), 0.991 (95% CI, 0.955-0.998) and 1446.946 (312.634-6692.646) with AUC=0.904 under HSROC. On subgroup analysis, MRS and Random Forest Model had highest sensitivity and specificity (100%,100%;100%,100%), DSC MRI and Deep Neural Network had highest AUC (0.98,0.986). Conclusions: ML algorithm has superior diagnostic performance and faster diagnostic capability once trained than conventional imaging for brain tumors. It has immense potential to be the standard of care in the future.

The Variables and Slow Transients Survey (VAST) on the Australian Square Kilometre Array Pathfinder (ASKAP) is designed to detect highly variable and transient radio sources on timescales from 5 s to $\sim\!5$ yr. In this paper, we present the survey description, observation strategy and initial results from the VAST Phase I Pilot Survey. This pilot survey consists of $\sim\!162$ h of observations conducted at a central frequency of 888 MHz between 2019 August and 2020 August, with a typical rms sensitivity of $0.24\ \mathrm{mJy\ beam}^{-1}$ and angular resolution of $12-20$ arcseconds. There are 113 fields, each of which was observed for 12 min integration time, with between 5 and 13 repeats, with cadences between 1 day and 8 months. The total area of the pilot survey footprint is 5 131 square degrees, covering six distinct regions of the sky. An initial search of two of these regions, totalling 1 646 square degrees, revealed 28 highly variable and/or transient sources. Seven of these are known pulsars, including the millisecond pulsar J2039–5617. Another seven are stars, four of which have no previously reported radio detection (SCR J0533–4257, LEHPM 2-783, UCAC3 89–412162 and 2MASS J22414436–6119311). Of the remaining 14 sources, two are active galactic nuclei, six are associated with galaxies and the other six have no multi-wavelength counterparts and are yet to be identified.

Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.

India is a de facto continent in the garb of a country. COVID-19 is an unprecedented global pandemic spanning continents. Being the second most populous country in the world, experts regard how India deals with the outbreak will have enormous impact on the world’s ability to deal with it. The country has been in lockdown since March 25, 2020 until the current time of early May 2020, and despite several challenges, there has been early success. The major conflict now is the health benefits weighed up against the deleterious social and economic consequences of prolonged lockdown, that is, life versus livelihood. This unprecedented calamity could potentially cause or exacerbate various psychiatric disorders. It is recognized that lifestyle changes and limited screen time may help reduce mental health difficulties. Considering the physical barriers to consultation, development of telemedicine services is needed. This pandemic, like other previous pandemics, will pass, and until this happens, we must remain extremely vigilant.

Over a hundred millisecond radio pulsars (MSPs) have been observed in globular clusters (GCs), motivating theoretical studies of the formation and evolution of these sources through stellar evolution coupled to stellar dynamics. Here we study MSPs in GCs using realistic N-body simulations with our Cluster Monte Carlo code. We show that neutron stars (NSs) formed in electron-capture supernovae can be spun up through mass transfer to form MSPs. Both NS formation and spin-up through accretion are greatly enhanced through dynamical interaction processes. We find that our models for average GCs at the present day with masses ≍ 2 × 105M⊙ can produce up to 10 – 20 MSPs, while a very massive GC model with mass ≍ 106M⊙ can produce close to 100. We show that the number of MSPs is anti-correlated with the total number of stellar-mass black holes (BHs) retained in the host cluster. As a result, the number of MSPs in a GC could be used to place constraints on its BH population. Some intrinsic properties of MSP systems in our models (such as the magnetic fields and spin periods) are in good overall agreement with observations.

As self-gravitating systems, dense star clusters exhibit a natural diffusion of energy from their innermost to outermost regions, leading to a slow and steady contraction of the core until it ultimately collapses under gravity. However, in spite of the natural tendency toward “core collapse,” the globular clusters (GCs) in the Milky Way exhibit a well-observed bimodal distribution in core radii separating the core-collapsed and non-core-collapsed clusters. This suggests an internal energy source is at work, delaying the onset of core collapse in many clusters. Over the past decade, a large amount of work has suggested that stellar black holes (BHs) play a dynamically-significant role in clusters throughout their entire lifetimes. Here we review our latest understanding of BH populations in GCs and demonstrate that, through their dynamical interaction with their host cluster, BHs can naturally explain the distinction between core-collapsed and non-core-collapsed clusters through a process we call “black hole burning.”

Possible relationships between groundwater arsenic concentration and alluvial sediment characteristics in a ∼19 km2 area in West Bengal have been investigated using a combination of hydrogeochemical, lithogeochemical and geophysical techniques. Arsenic hotspots, typically associated with elevated groundwater Fe and Mn, were found to be correlated to some extent with old river channels (abandoned meanders, oxbow lakes), where sandy aquifers included intercalated fine-grained overbank deposits, rich in As, Fe, Mn and Corg. Otherwise no demonstrably significant overall differences in any of lithology, grain-size distribution, mineral composition or Fe, Mn and organic C content of the sediments were found between two representative sites with contrastingly low (<50 μg 1—1) and high (>200 μg 1—1) As groundwater contents.

Our results are consistent with microbially mediated redox reactions controlled by the presence of natural organic matter within the aquifer and the occurrence of As-bearing redox traps, primarily formed by Fe and Mn oxides/hydroxides, being the most important factors which control the release of As into shallow groundwaters at the study site.

Knowledge of the solid-phase speciation of As in Bengali sediments associated with hazardous As-rich groundwaters is crucial to understanding the processes controlling As release. The local coordination environment of As in such a sediment has been probed using K-edge As EXAFS. This revealed that As exists predominantly in its oxidized form, As(V), probably adsorbed as bidentate arsenate tetrahedra on metal (Fe and/or Al) oxide/hydroxide surfaces, although incorporation of As into a metal oxide structure cannot be ruled out. Arsenic was found to occur in several different coordination environments and this, together with the low concentration (<10 μg g–1) of As in the sediment prevented the unambiguous assignment of the second coordination sphere. The EXAFS analysis of the sediment after incubation under anaerobic conditions in the presence of added electron donor for metal reduction indicated changes in the relative concentrations of different solid-phase As species, providing circumstantial evidence for differential susceptibility to microbial action.

In many areas of south and south-eastern Asia, concentrations of As in ground water have been found to exceed the WHO maximum concentration limit of 10 μg/l. This is adversely affecting the health of millions of people and has grave current and future health implications. It has recently been suggested that extensive abstraction of ground water in these areas may accelerate the release of As to ground water. This study uses geochemical and isotopic data to assess this hypothesis. The area investigated in this study is in the Chakdaha block of the Nadia District, West Bengal. The ground water is predominantly of the Ca-Mg-HCO3 type, although some samples were found to contain elevated concentrations of Na, Cl and SO4. This is thought to reflect a greater degree of water-rock interaction at the locations of these particular samples. Arsenic concentrations exceeded the national limit of 50 μg/l in 13 of the 22 samples collected. Four of the 13 samples with high As were recovered from tubewells with depths of 60 m or more. Shallow ground water samples were found to have a stable isotopic composition which falls subparallel to the Global Meteoric Water Line. This probably represents a contribution of evaporated surface water to the ground water, possibly from surface ponds or re-infiltrating irrigation water. Deep ground water, conversely, was shown to have a composition that closely reflects that of meteoric water. The data presented in this study suggest that, whilst the drawdown of surface waters may drive As release in shallow ground waters, it is not responsible for driving As release in deep ground water. However, local abstraction may have resulted in changes in the ground water flow regime of the area, with contaminated shallow ground waters being drawn into previously uncontaminated deep aquifers.

Arsenic mobilization and Fe(III) reduction in acetate-amended sediments collected from a range of depths from an aquifer with elevated groundwater arsenic concentrations in West Bengal were monitored over a 1 month period. Significant arsenic release was noted in sediment collected from 24 m and 45 m depth, with some Fe(III) reduction also observed in the 24 m sample. The structure of the microbial communities present in the sediments prior to incubation showed marked differences down the sediment column. Profiling of the microbial community in the 24 m and 45 m samples revealed a relatively complex make-up, with Acinetobacter species comprising the bulk of the 24 m sedimentary bacterial population, but no previously characterized As(V)-reducers were detected in either sample.

Phased VLA observations of the Galactic center magnetar J1745-2900 over 8-12 GHz reveal rich single pulse behavior. The average profile is comprised of several distinct components and is fairly stable over day timescales and GHz frequencies. The average profile is dominated by the jitter of relatively narrow pulses. The pulses in each of the four profile components are uncorrelated in phase and amplitude, although the occurrence of pulse components 1 and 2 appear to be correlated. Using a collection of the brightest individual pulses, we verify that the index of the dispersion law is consistent with the expected cold plasma value of 2. The scattering time is weakly constrained, but consistent with previous measurements, while the dispersion measure DM = 1763+3−10 pc cm−3 is lower than previous measurements, which could be a result of time variability in the line-of-sight column density or changing pulse profile shape over time or frequency.