Experimental estimates of the photon background in a potential light-by-light scattering study

High power short pulse lasers provide a promising route to study the strong field effects of the quantum vacuum, for example by direct photon-photon scattering in the all-optical regime. Theoretical predictions based on realistic laser parameters achievable today or in the near future predict scattering of a few photons with colliding Petawatt laser pulses, requiring single photon sensitive detection schemes and very good spatio-temporal filtering and background suppression. In this article, we present experimental investigations of this photon background by employing only a single high power laser pulse tightly focused in residual gas of a vacuum chamber. The focal region was imaged onto a single-photon sensitive, time gated camera. As no detectable quantum vacuum signature was expected in our case, the setup allowed for characterization and first mitigation of background contributions. For the setup employed, scattering off surfaces of imperfect optics dominated below the residual gas pressures of 1×10-4mbar. Extrapolation of the findings to intensities relevant for photon-photon scattering studies is discussed.

Regional Distribution of Brain Injury After Cardiac Arrest: Clinical and Electrographic Correlates

Background and Objectives:Disorders of consciousness, EEG background suppression and epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution of diffusion MRI-measured anoxic brain injury after cardiac arrest and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures.Methods:We analyzed patients from a single-center database of unresponsive patients who underwent diffusion MRI following cardiac arrest (n=204). We classified each patient based on recovery of consciousness (command-following) before discharge, the most continuous EEG background (burst suppression versus continuous), and the presence or absence of seizures. Anoxic brain injury was measured using the apparent diffusion coefficient (ADC) signal. We identified ADC abnormalities relative to control subjects without cardiac arrest (n=48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each outcome variable. Finally, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures.Results:Compared to controls, cardiac arrest patients demonstrated ADC signal reduction most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes, but also in deep structures. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter.Discussion:Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Regional patterns of anoxic brain injury are relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis.Classification of Evidence:This study provides Class IV evidence that disorders of consciousness after cardiac arrest are associated with widely lower ADC values on diffusion MRI and are most strongly associated with reductions in occipital ADC.

High-fidelity nano-FTIR spectroscopy by on-pixel normalization of signal harmonics

Scattering-type scanning near-field optical microscopy (s-SNOM) and Fourier transform infrared nanospectroscopy (nano-FTIR) are emerging tools for physical and chemical nanocharacterization of organic and inorganic composite materials. Being based on (i) diffraction-limited illumination of a scanning probe tip for nanofocusing of light and (ii) recording of the tip-scattered radiation, the efficient suppression of background scattering has been critical for their success. Here, we show that indirect tip illumination via far-field reflection and scattering at the sample can produce s-SNOM and nano-FTIR signals of materials that are not present at the tip position – despite full background suppression. Although these artefacts occur primarily on or near large sample structures, their understanding and recognition are of utmost importance to ensure correct interpretation of images and spectra. Detailed experimental and theoretical results show how such artefacts can be identified and eliminated by a simple signal normalization step, thus critically strengthening the analytical capabilities of s-SNOM and nano-FTIR spectroscopy.

Collaborative Local-Global Learning for Temporal Action Proposal

Temporal action proposal generation is an essential and challenging task in video understanding, which aims to locate the temporal intervals that likely contain the actions of interest. Although great progress has been made, the problem is still far from being well solved. In particular, prevalent methods can handle well only the local dependencies (i.e., short-term dependencies) among adjacent frames but are generally powerless in dealing with the global dependencies (i.e., long-term dependencies) between distant frames. To tackle this issue, we propose CLGNet, a novel Collaborative Local-Global Learning Network for temporal action proposal. The majority of CLGNet is an integration of Temporal Convolution Network and Bidirectional Long Short-Term Memory, in which Temporal Convolution Network is responsible for local dependencies while Bidirectional Long Short-Term Memory takes charge of handling the global dependencies. Furthermore, an attention mechanism called the background suppression module is designed to guide our model to focus more on the actions. Extensive experiments on two benchmark datasets, THUMOS’14 and ActivityNet-1.3, show that the proposed method can outperform state-of-the-art methods, demonstrating the strong capability of modeling the actions with varying temporal durations.

Dataset for predicting single-spot proton ranges in proton therapy of prostate cancer

The number of radiotherapy patients treated with protons has increased from less than 60,000 in 2007 to more than 220,000 in 2019. However, the considerable uncertainty in the positioning of the Bragg peak deeper in the patient raised new challenges in the proton therapy of prostate cancer (PCPT). Here, we describe and share a dataset where 43 single-spot anterior beams with defined proton energies were delivered to a prostate phantom with an inserted endorectal balloon (ERB) filled either with water only or with a silicon-water mixture. The nuclear reactions between the protons and the silicon yield a distinct prompt gamma energy line of 1.78 MeV. Such energy peak could be identified by means of prompt gamma spectroscopy (PGS) for the protons hitting the ERB with a three-sigma threshold. The application of a background-suppression technique showed an increased rejection capability for protons hitting the prostate and the ERB with water only. We describe each dataset, document the full processing chain, and provide the scripts for the statistical analysis.

Assessing Lymphatic Uptake of Lipids Using Magnetic Resonance Imaging: A Feasibility Study in Healthy Human Volunteers with Potential Application for Tracking Lymph Node Delivery of Drugs and Formulation Excipients

Dietary lipids and some pharmaceutical lipid excipients can facilitate the targeted delivery of drugs to the intestinal lymphatics. Here, the feasibility of magnetic resonance imaging (MRI) for imaging lipid uptake into the intestinal lymphatics was assessed, shedding light on which lymph nodes can be targeted using this approach. Three healthy male volunteers were scanned at 3.0 T at baseline, 120, 180, 240, and 300 min post high-fat meal. A sagittal multi-slice image was acquired using a diffusion-weighted whole-body imaging sequence with background suppression (DWIBS) (pre inversion TI = 260 ms). Changes in area, major, and minor axis length were compared at each time point. Apparent diffusion coefficient (ADC) was calculated (b = 0 and 600 s/mm2) across eight slices. An average of 22 nodes could be visualised across all time points. ADC increased at 120 and 180 min compared to the baseline in all three participants by an average of 9.2% and 6.8%, respectively. In two participants, mean node area and major axis lengths increased at 120 and 180 min relative to the baseline. In conclusion, the method described shows potential for repeated lymph node measurements and the tracking of lipid uptake into the lymphatics. Further studies should focus on methodology optimisation in a larger cohort.