An open‑source low‑cost sensor for SNR‑based GNSS reflectometry : design and long‑term validation towards sea‑level altimetry

Monitoring sea level is critical due to climate change observed over the years. Global Navigation Satellite System Reflectometry (GNSS-R) has been widely demonstrated for coastal sea-level monitoring. The use of signal-to-noise ratio (SNR) observations from ground-based stations has been especially productive for altimetry applications. SNR records an interference pattern whose oscillation frequency allows retrieving the unknown reflector height. Here we report the development and validation of a complete hardware and software system for SNR-based GNSS-R. We make it available as open source based on the Arduino platform. It costs about US$200 (including solar power supply) and requires minimal assembly of commercial off-the-shelf components. As an initial validation towards applications in coastal regions, we have evaluated the system over approximately 1 year by the Guaíba Lake in Brazil. We have compared water-level altimetry retrievals with independent measurements from a co-located radar tide gauge (within 10 m). The GNSS-R device ran practically uninterruptedly, while the reference radar gauge suffered two malfunctioning periods, resulting in gaps lasting for 44 and 38 days. The stability of GNSS-R altimetry results enabled the detection of miscalibration steps (10 cm and 15 cm) inadvertently introduced in the radar gauge after it underwent maintenance. Excluding the radar gaps and its malfunctioning periods (reducing the time series duration from 317 to 147 days), we have found a correlation of 0.989 and RMSE of 2.9 cm in daily means. To foster open science and lower the barriers for entry in SNR-based GNSS-R research and applications, we make a complete bill of materials and build tutorials freely available on the Internet so that interested researchers can replicate the system

Creation of NOON states by double Fock-state/Bose-Einstein condensates

NOON states (states of the form $|N>_{a}|0>_{b}+|0>_{a}|N>_{b}$ where $a$ and $b$ are single particle states) have been used for predicting violations of hidden-variable theories (Greenberger-Horne-Zeilinger violations) and are valuable in metrology for precision measurements of phase at the Heisenberg limit. We show theoretically how the use of two Fock state/Bose-Einstein condensates as sources in a modified Mach Zender interferometer can lead to the creation of the NOON state in which $a$ and $b$ refer to arms of the interferometer and $N$ is the total number of particles in the two condensates. The modification of the interferometer involves making conditional ``side'' measurements of a few particles near the sources. These measurements put the remaining particles in a superposition of two phase states, which are converted into NOON states by a beam splitter. The result is equivalent to the quantum experiment in which a large molecule passes through two slits. The NOON states are combined in a final beam splitter and show interference. Attempts to detect through which ``slit'' the condensates passed destroys the interference.Comment: 8 pages 5 figure

Angular Forces Around Transition Metals in Biomolecules

Quantum-mechanical analysis based on an exact sum rule is used to extract an semiclassical angle-dependent energy function for transition metal ions in biomolecules. The angular dependence is simple but different from existing classical potentials. Comparison of predicted energies with a computer-generated database shows that the semiclassical energy function is remarkably accurate, and that its angular dependence is optimal.Comment: Tex file plus 4 postscript figure

Quantum non-demolition measurement of a superconducting two-level system

In quantum mechanics, the process of measurement is a subtle interplay between extraction of information and disturbance of the state of the quantum system. A quantum non-demolition (QND) measurement minimizes this disturbance by using a particular system - detector interaction which preserves the eigenstates of a suitable operator of the quantum system. This leads to an ideal projective measurement. We present experiments in which we perform two consecutive measurements on a quantum two -level system, a superconducting flux qubit, by probing the hysteretic behaviour of a coupled nonlinear resonator. The large correlation between the results of the two measurements demonstrates the QND nature of the readout method. The fact that a QND measurement is possible for superconducting qubits strengthens the notion that these fabricated mesoscopic systems are to be regarded as fundamental quantum objects. Our results are also relevant for quantum information processing, where projective measurements are used for protocols like state preparation and error correction.Comment: 14 pages, 4 figure

Optical Magnetometry

Some of the most sensitive methods of measuring magnetic fields utilize interactions of resonant light with atomic vapor. Recent developments in this vibrant field are improving magnetometers in many traditional areas such as measurement of geomagnetic anomalies and magnetic fields in space, and are opening the door to new ones, including, dynamical measurements of bio-magnetic fields, detection of nuclear magnetic resonance (NMR), magnetic-resonance imaging (MRI), inertial-rotation sensing, magnetic microscopy with cold atoms, and tests of fundamental symmetries of Nature.Comment: 11 pages; 4 figures; submitted to Nature Physic

Post-meiotic transcription in mouse testes detected with spermatid cDNA clones

cDNA clones to poly(A) + mRNA from spermatids have been obtained to study gene transcription in post-meiotic germ cells. Four cDNA clones detect mRNAs that increase in abundance in post-meiotic germ cells. One clone, pPM459, was shown to correspond to an mRNA that is transcribed after meiosis. Pulse-labelling experiments demonstrate transcription o5 the message in spermatids. These data constitute further evidence for post-meiotic gene transcription in spermatids.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44190/1/10540_2005_Article_BF01116696.pd

ILC3 function as a double-edged sword in inflammatory bowel diseases

Inflammatory bowel diseases (IBD), composed mainly of Crohn’s disease (CD) and ulcerative colitis (UC), are strongly implicated in the development of intestinal inflammation lesions. Its exact etiology and pathogenesis are still undetermined. Recently accumulating evidence supports that group 3 innate lymphoid cells (ILC3) are responsible for gastrointestinal mucosal homeostasis through moderate generation of IL-22, IL-17, and GM-CSF in the physiological state. ILC3 contribute to the progression and aggravation of IBD while both IL-22 and IL-17, along with IFN-γ, are overexpressed by the dysregulation of NCR− ILC3 or NCR+ ILC3 function and the bias of NCR+ ILC3 towards ILC1 as well as regulatory ILC dysfunction in the pathological state. Herein, we feature the group 3 innate lymphoid cells’ development, biological function, maintenance of gut homeostasis, mediation of IBD occurrence, and potential application to IBD therapy

SEGMA: an automatic SEGMentation Approach for human brain MRI using sliding window and random forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course