Noiseless Gravitational Lensing Simulations

The microphysical properties of the DM particle can, in principle, be constrained by the properties and abundance of substructures in DM halos, as measured through strong gravitational lensing. Unfortunately, there is a lack of accurate theoretical predictions for the lensing signal of substructures, mainly because of the discreteness noise inherent to N-body simulations. Here we present Recursive-TCM, a method that is able to provide lensing predictions with an arbitrarily low discreteness noise, without any free parameters or smoothing scale. This solution is based on a novel way of interpreting the results of N-body simulations, where particles simply trace the evolution and distortion of Lagrangian phase-space volume elements. We discuss the advantages of this method over the widely used cloud-in-cells and adaptive-kernel smoothing density estimators. Applying the new method to a cluster-sized DM halo simulated in warm and cold DM scenarios, we show how the expected differences in their substructure population translate into differences in the convergence and magnification maps. We anticipate that our method will provide the high-precision theoretical predictions required to interpret and fully exploit strong gravitational lensing observations.Comment: 13 pages, 13 figures. Updated fig 12, references adde

Inferring Maps of the Sun's Far-side Unsigned Magnetic Flux from Far-side Helioseismic Images using Machine Learning Techniques

Accurate modeling of the Sun's coronal magnetic field and solar wind structures require inputs of the solar global magnetic field, including both the near and far sides, but the Sun's far-side magnetic field cannot be directly observed. However, the Sun's far-side active regions are routinely monitored by helioseismic imaging methods, which only require continuous near-side observations. It is therefore both feasible and useful to estimate the far-side magnetic-flux maps using the far-side helioseismic images despite their relatively low spatial resolution and large uncertainties. In this work, we train two machine-learning models to achieve this goal. The first machine-learning training pairs simultaneous SDO/HMI-observed magnetic-flux maps and SDO/AIA-observed EUV 304$\r{A}$ images, and the resulting model can convert 304$\r{A}$ images into magnetic-flux maps. This model is then applied on the STEREO/EUVI-observed far-side 304$\r{A}$ images, available for about 4.3 years, for the far-side magnetic-flux maps. These EUV-converted magnetic-flux maps are then paired with simultaneous far-side helioseismic images for a second machine-learning training, and the resulting model can convert far-side helioseismic images into magnetic-flux maps. These helioseismically derived far-side magnetic-flux maps, despite their limitations in spatial resolution and accuracy, can be routinely available on a daily basis, providing useful magnetic information on the Sun's far side using only the near-side observations.Comment: Accepted by Ap

Phase Shifts Measured in Evanescent Acoustic Waves above the Solar Photosphere and Their Possible Impacts to Local Helioseismology

A set of 464-min high-resolution high-cadence observations were acquired for a region near the Sun's disk center using the Interferometric BI-dimensional Spectrometer (IBIS) installed at the Dunn Solar Telescope. Ten sets of Dopplergrams are derived from the bisector of the spectral line corresponding approximately to different atmospheric heights, and two sets of Dopplergrams are derived using MDI-like algorithm and center-of-gravity method. These data are then filtered to keep only acoustic modes, and phase shifts are calculated between Doppler velocities of different atmospheric heights as a function of acoustic frequency. The analysis of the frequency- and height-dependent phase shifts shows that for evanescent acoustic waves, oscillations in the higher atmosphere lead those in the lower atmosphere by an order of 1 s when their frequencies are below about 3.0 mHz, and lags behind by about 1 s when their frequencies are above 3.0 mHz. Non-negligible phase shifts are also found in areas with systematic upward or downward flows. All these frequency-dependent phase shifts cannot be explained by vertical flows or convective blueshifts, but are likely due to complicated hydrodynamics and radiative transfer in the non-adiabatic atmosphere in and above the photosphere. These phase shifts in the evanescent waves pose great challenges to the interpretation of some local helioseismic measurements that involve data acquired at different atmospheric heights or in regions with systematic vertical flows. More quantitative characterization of these phase shifts is needed so that they can either be removed during measuring processes or be accounted for in helioseismic inversions.Comment: accepted for publication in Ap

Effect and Neuroimaging Mechanism of Electroacupuncture for Vascular Cognitive Impairment No Dementia: Study Protocol for a Randomized, Assessor-Blind, Controlled Clinical Trial

Vascular cognitive impairment no dementia (VCIND) is likely to develop into vascular dementia (VD) without intervention. The clinical efficacy of electroacupuncture (EA) for VCIND has been previously demonstrated. However, the neuroimaging mechanism of EA for VCIND has not been elucidated clearly. This trial is designed to provide solid evidence for the efficacy and neuroimaging mechanism of EA treatment for patients with VCIND. This ongoing study is an assessor-blind, parallel-group, randomized controlled trial. 140 eligible subjects will be recruited from the General Hospital of Ningxia Medical University and randomized into either the electroacupuncture (EA) group or the control group (CG). All subjects will receive basic treatment, and participants in the CG will receive health education performed weekly. Except for basic treatment and health education, participants in the EA group will receive treatment 5 times per week for a total of 40 sessions over 8 weeks. The primary outcome in this study is Montreal Cognitive Assessment (MoCA), and the secondary outcomes are Auditory Verbal Learning Test (AVLT), Stroop color-naming condition (STROOP), Rey–Osterrieth Complex Graphics Testing, and resting-state functional magnetic resonance imaging (rs-fMRI). All of the outcome measures will be assessed at baseline and 8 weeks of intervention. The medical abstraction of adverse events will be done at each visit. The results of this trial will demonstrate the efficacy and neuroimaging mechanism of EA treatment for VCIND, thus supporting EA treatment as an ideal choice for VCIND treatment. The trial was registered at the Chinese Clinical Trial Registry on 28 July 2018 (ChiCTR1800017398)

Alterations in the fecal microbiota of patients with spinal cord injury.

ObjectivesSpinal cord injury (SCI) is associated with severe autonomic dysfunction. Patients with SCI often suffer from a lack of central nervous system control over the gastrointestinal system. Therefore, we hypothesized that patients with SCI would cause intestinal flora imbalance. We investigated alterations in the fecal microbiome in a group of patients with SCI.MethodsMicrobial communities in the feces of 23 patients and 23 healthy controls were investigated using high-throughput Illumina Miseq sequencing targeting the V3-V4 region of the 16S ribosomal RNA (rRNA) gene. The relative abundances between the fecal microbiota at the genus level in patients with SCI and healthy individuals were determined using cluster analysis.ResultsThe structure and quantity of fecal microbiota differed significantly between patients with SCI and healthy controls, but the richness and diversity were not significantly different. A two-dimensional heatmap showed that the relative abundances of forty-five operational taxonomic units (OTUs) were significantly enriched either in SCI or healthy samples. Among these, 18 OTUs were more abundant in healthy controls than in patients with SCI, and 27 OTUs were more abundant in the SCI group than in healthy controls.ConclusionOur study showed that patients with SCI exhibited microbiome dysbiosis