The molecular gas content of the advanced S+E merger NGC 4441 - Evidence for an extended decoupled nuclear disc?

Mergers between a spiral and an elliptical (S+E mergers) are poorly studied so far despite the importance for galaxy evolution. NGC4441 is a nearby candidate for an advanced remnant of such a merger, showing typical tidal structures like an optical tail and two shells as well as two HI tails. The study of the molecular gas content gives clues on the impact of the recent merger event on the star formation. Simulations of S+E mergers predict contradictory scenarios concerning the strength and the extent of an induced starburst. Thus, observations of the amount and the distribution of the molecular gas, the raw material of star formation, are needed to understand the influence of the merger on the star formation history. 12CO and 13CO (1-0) and (2-1) observations were obtained using the Onsala Space Observatory 20m and IRAM 30m telescope as well as the Plateau de Bure interferometer. These data allow us to carry out a basic analysis of the molecular gas properties such as estimates of the molecular gas mass, its temperature and density and the star formation efficiency. The CO observations reveal an extended molecular gas reservoir out to ~4kpc, with a total molecular gas mass of ~5x10^8 M_sun. Furthermore, high resolution imaging shows a central molecular gas feature, most likely a rotating disc hosting most of the molecular gas ~4x10^8 M_sun. This nuclear disc shows a different sense of rotation than the large-scale HI structure, indicating a kinematically decoupled core. (abbreviated)Comment: 11 pages, accepted by A&

Double-Helix Microscopy for Wide-Field 3-D Single-Molecule Fluorescence Imaging

We present methods to improve the localization accuracy in wide-field 3-D single-molecule double-helix microscopy. We analyze the optical efficiency of the system, the fundamental limit for 3-D localization, the estimation algorithms, and polarization sensitive detection

Electricity supply and use among rural and peri-urban households and small firms in Nigeria

Improving access to energy services among the underserved requires understanding the status quo in energy access and estimating future energy requirements of energy service provision. In this paper, we present a novel survey dataset collected in 2021 within the framework of the PeopleSuN project. Across three Nigerian geopolitical zones, a total of 3,599 households and 1,122 small and medium-sized enterprises were surveyed. The sample is representative of grid-electrified regions of each zone, excluding urban centres. Our surveys collect data on demographic and socioeconomic characteristics, energy access and supply quality, electrical appliance ownership and usage time, cooking solutions, capabilities, and preferences. We encourage academic use of the data presented and suggest three avenues of further research: (1) modelling appliance ownership likelihoods, electricity consumption levels and energy service needs in un-electrified regions; (2) modelling the integration of decentralised renewable and battery storage solutions to address high usage of diesel generators in peri-urban regions; (3) exploring broader issues of multi-dimensional energy access, access to decent living standards and climate vulnerability.Comment: Revised edition: Summary statistics moved to the end. Related datasets review table added. More technical details on data collection adde

3D Submillisecond tracking microscopy of single fluorescent particles with adaptive optics

Single particle tracking microscopy in combination with fluorescent labeling has opened the door to investigations of nanoscale dynamics in living cells. While conventional instruments feature temporal resolutions of typically 5–30 ms, nanoscale processes happen on a millisecond or submillisecond time scale. To overcome this limitation, I have developed a single particle tracking microscope with 130 μs temporal resolution and single-fluorophore sensitivity. The instrument acquires 3D trajectories by active tracking of a fluorescent particle with a focused laser beam. This is accomplished by fast beam steering, which is feedback-driven by the detected particle position in the focal volume. For translation of the laser focus along the optical axis, I have implemented a novel vibration-free remote focusing mechanism based on a deformable mirror, an adaptive optics wavefront correction device. In characterization experiments with fluorescent beads, I have found that the instrument is capable of tracking directed motion up to 150 μm/s and free 3D Brownian motion with diffusion coefficients of more than 2 μm²/s. The potential for biological applications is demonstrated by tracking fluorescently labeled viruses on cell membranes and transport vesicles in the cytoplasm of living cells

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Importance of harvest-driven trait changes for the management of invasive species: letter

International audienceAlthough intraspecific differences between the phenotypes of organisms are an important driver of ecological dynamics (Des Roches et al. 2018), research to help integrate phenotypic variation and its drivers with ecosystem management has been limited. For this reason, the novel conceptual framework proposed by Palkovacs et al. (2018) – which helps to clarify the ecological implications of harvest‐driven trait changes – is timely

LBT/LUCIFER Observations of the z~2 Lensed Galaxy J0900+2234

We present rest-frame optical images and spectra of the gravitationally lensed, star-forming galaxy J0900+2234 (z=2.03). The observations were performed with the newly commissioned LUCIFER1 near-infrared instrument mounted on the Large Binocular Telescope (LBT). We fit lens models to the rest-frame optical images and find the galaxy has an intrinsic effective radius of 7.4 kpc with a lens magnification factor of about 5 for the A and B components. We also discovered a new arc belonging to another lensed high-z source galaxy, which makes this lens system a potential double Einstein ring system. Using the high S/N rest-frame optical spectra covering H+K band, we detected Hbeta, OIII, Halpha, NII and SII emission lines. Detailed physical properties of this high-z galaxy were derived. The extinction towards the ionized HII regions (E_g(B-V)) is computed from the flux ratio of Halpha and Hbeta and appears to be much higher than that towards stellar continuum (E_s(B-V)), derived from the optical and NIR broad band photometry fitting. The metallicity was estimated using N2 and O3N2 indices. It is in the range of 1/5-1/3 solar abundance, which is much lower than the typical z~2 star-forming galaxies. From the flux ratio of SII 6717 and 6732, we found that the electron number density of the HII regions in the high-z galaxy were >1000 cm^-3, consistent with other z~2 galaxies but much higher than that in local HII regions. The star-formation rate was estimated via the Halpha luminosity, after correction for the lens magnification, to be about 365\pm69 Msun/yr. Combining the FWHM of Halpha emission lines and the half-light radius, we found the dynamical mass of the lensed galaxy is 5.8\pm0.9x10^10 Msun. The gas mass is 5.1\pm1.1x10^10~Msun from the H\alpha flux surface density by using global Kennicutt-Schmidt Law, indicating a very high gas fraction of 0.79\pm0.19 in J0900+2234.Comment: 11 pages, 6 figures accepted by ApJ, revised based on referee repor

Breast ultrasound lesions recognition::end-to-end deep learning approaches

Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. To assess the performance, we conduct fivefold cross validation using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results showed that our proposed method performed better on benign lesions, with a top "mean Dice" score of 0.7626 with FCN-16s, when compared with the malignant lesions with a top mean Dice score of 0.5484 with FCN-8s. When considering the number of images with Dice score >0.5 , 89.6% of the benign lesions were successfully segmented and correctly recognised, whereas 60.6% of the malignant lesions were successfully segmented and correctly recognized. We conclude the paper by addressing the future challenges of the work

Breast Ultrasound Region of Interest Detection and Lesion Localisation

© 2020 Elsevier B.V. In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human experts. Researchers attempting to automate the process of obtaining the ROIs have been relying on image processing and conventional machine learning methods. We propose the use of a deep learning method for breast ultrasound ROI detection and lesion localisation. We use the most accurate object detection deep learning framework – Faster-RCNN with Inception-ResNet-v2 – as our deep learning network. Due to the lack of datasets, we use transfer learning and propose a new 3-channel artificial RGB method to improve the overall performance. We evaluate and compare the performance of our proposed methods on two datasets (namely, Dataset A and Dataset B), i.e. within individual datasets and composite dataset. We report the lesion detection results with two types of analysis: (1) detected point (centre of the segmented region or the detected bounding box) and (2) Intersection over Union (IoU). Our results demonstrate that the proposed methods achieved comparable results on detected point but with notable improvement on IoU. In addition, our proposed 3-channel artificial RGB method improves the recall of Dataset A. Finally, we outline some future directions for the research