Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

A variety of physical and biomedical imaging techniques, such as digital holography, interferometric synthetic aperture radar (InSAR), or magnetic resonance imaging (MRI) enable measurement of the phase of a physical quantity additionally to its amplitude. However, the phase can commonly only be measured modulo 2π, as a so called wrapped phase map. Phase unwrapping is the process of obtaining the underlying physical phase map from the wrapped phase. Tile-based phase unwrapping algorithms operate by first tessellating the phase map, then unwrapping individual tiles, and finally merging them to a continuous phase map. They can be implemented computationally efficiently and are robust to noise. However, they are prone to failure in the presence of phase residues or erroneous unwraps of single tiles. We tried to overcome these shortcomings by creating novel tile unwrapping and merging algorithms as well as creating a framework that allows to combine them in modular fashion. To increase the robustness of the tile unwrapping step, we implemented a model-based algorithm that makes efficient use of linear algebra to unwrap individual tiles. Furthermore, we adapted an established pixel-based unwrapping algorithm to create a quality guided tile merger. These original algorithms as well as previously existing ones were implemented in a modular phase unwrapping C++ framework. By examining different combinations of unwrapping and merging algorithms we compared our method to existing approaches. We could show that the appropriate choice of unwrapping and merging algorithms can significantly improve the unwrapped result in the presence of phase residues and noise. Beyond that, our modular framework allows for efficient design and test of new tile-based phase unwrapping algorithms. The software developed in this study is freely available

Endogenous circatidal rhythm in the Manila clam Ruditapes philippinarum (Bivalvia: Veneridae)

Manila clams, Ruditapes philippinarum, removed from their natural environment and maintained for 9 weeks in continuously immersed conditions exhibited a clear endogenous circatidal rhythm in oxygen consumption. The clams exhibited a semidiurnal rhythmicity in oxygen consumption after showing a diurnal pattern in the first few days (5 to 7 d) of the experiment. The results of the present study indicate that activity rhythms of clams are controlled not only by exogenous factors, but also by an endogenous circatidal periodicity

An empirical vegetation correction for soil water content quantification using cosmic ray probes

Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0-calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0-calibration function was able to explain 95% of the overall variability in fast neutron intensity

Industrial Cyber-Physical Systems-based Cloud IoT Edge for Federated Heterogeneous Distillation.

Deep convoloutional networks have achieved remarkable performance in a wide range of vision-based tasks in modern internet of things (IoT). Due to privacy issue and transmission cost, mannually annotated data for training the deep learning models are usually stored in different sites with fog and edge devices of various computing capacity. It has been proved that knowledge distillation technique can effectively compress well trained neural networks into light-weight models suitable to particular devices. However, different fog and edge devices may perform different sub-tasks, and simplely performing model compression on powerful cloud servers failed to make use of the private data sotred at different sites. To overcome these obstacles, we propose an novel knowledge distillation method for object recognition in real-world IoT sencarios. Our method enables flexible bidirectional online training of heterogeneous models distributed datasets with a new ``brain storming'' mechanism and optimizable temperature parameters. In our comparison experiments, this heterogeneous brain storming method were compared to multiple state-of-the-art single-model compression methods, as well as the newest heterogeneous and homogeneous multi-teacher knowledge distillation methods. Our methods outperformed the state of the arts in both conventional and heterogeneous tasks. Further analysis of the ablation expxeriment results shows that introducing the trainable temperature parameters into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of our knowledge, this is the IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks

Textural Characterisation on Regions of Interest: A Useful Tool for the Study of Small Vessel Disease

Proponemos un marco para investigar las propiedades de los tejidos aparentemente normales en las imágenes de resonancia magnética de la estructura cerebral de pacientes con enfermedad de vasos pequeños (SVD). Implica la extracción de entidades texturales en regiones de interés (ROI) obtenidas a partir de una plantilla anatómicamente relevante, combinada con un análisis estadístico que considere la distribución relativa de marcadores SVD (por ejemplo, microsangrados, espacios perivasculares e hiperintensidades de materia blanca) con respecto a las características texturales de las regiones de interés, en los territorios arteriales derivados de otra plantilla. Aplicamos este enfoque a los datos de 42 pacientes de un estudio de accidente cerebrovascular leve para investigar si los tejidos normales en diferentes regiones cerebrales son homogéneos independientemente de la presencia de marcadores y variedades de SVD específicos en las manifestaciones de la patología (accidente cerebrovascular en diferentes territorios arteriales). Nuestros resultados sugieren que este no es el caso: que los tejidos normales son heterogéneos y que las variaciones locales (representadas por la entropía) están asociadas con marcadores SVD, de acuerdo con los informes clínicos

High-Precision Mass-Dependent Molybdenum Isotope Variations in Magmatic Rocks Determined by Double-Spike MC-ICP-MS

Small mass‐dependent variations of molybdenum isotope ratios in oceanic and island arc rocks are expected as a result of recycling altered oceanic crust and sediments into the mantle at convergent plate margins over geological timescales. However, the determination of molybdenum isotope data precise and accurate enough to identify these subtle isotopic differences remains challenging. Large sample sizes – in excess of 200 mg – need to be chemically processed to isolate enough molybdenum in order to allow sufficiently high‐precision isotope analyses using double‐spike MC‐ICP‐MS techniques. Established methods are either unable to process such large amounts of silicate material or require several distinct chemical processing steps, making the analyses very time‐consuming. Here, we present a new and efficient single‐pass chromatographic exchange technique for the chemical isolation of molybdenum from silicate and metal matrices. To test our new method, we analysed USGS reference materials BHVO‐2 and BIR‐1. Our new data are consistent with those derived from more involved and time‐consuming methods for these two reference materials previously published. We also provide the first molybdenum isotope data for USGS reference materials AGV‐2, the GSJ reference material JB‐2 as well as metal NIST SRM 361.ISSN:1639-4488ISSN:1751-908

Analysis of dynamic texture and spatial spectral descriptors of dynamic contrast-enhanced brain magnetic resonance images for studying small vessel disease

Cerebral small vessel disease (SVD) comprises various pathological processes affecting small brain vessels and damaging white and grey matter. In this paper, we propose a framework comprising region of interest sampling, dynamic spectral and texture description, functional principal component analysis, and statistical analysis to study exogenous contrast agent distribution over time in various brain regions in patients with recent mild stroke and SVD features.We compared our results against current semi-quantitative surrogates of dysfunction such as signal enhancement area and slope. Biological sex, stroke lesion type and overall burden of white matter hyperintensities (WMH) were significant predictors of intensity, spectral, and texture features extracted from the ventricular region (p-value < 0.05), explaining between a fifth and a fourth of the data variance (0.20 ≤Adj.R2 ≤ 0.25). We observed that spectral feature reflected more the dysfunction compared to other descriptors since the overall WMH burden explained consistently the power spectra variability in blood vessels, cerebrospinal fluid, deep grey matter and white matter. Our preliminary results show the potential of the framework for the analysis of dynamic contrast-enhanced brain magnetic resonance imaging acquisitions in SVD since significant variation in our metrics was related to the burden of SVD features. Therefore, our proposal may increase sensitivity to detect subtle features of small vessel dysfunction. A public version of the code will be released on our research website

Blood-brain barrier failure as a core mechanism in cerebral small vessel disease and dementia: evidence from a cohort study

Introduction: Small vessel disease (SVD) is a common contributor to dementia. Subtle blood-brain barrier (BBB) leakage may be important in SVD-induced brain damage. Methods: We assessed imaging, clinical variables, and cognition in patients with mild (i.e., nondisabling) ischemic lacunar or cortical stroke. We analyzed BBB leakage, interstitial fluid, and white matter integrity using multimodal tissue-specific spatial analysis around white matter hyperintensities (WMH). We assessed predictors of 1 year cognition, recurrent stroke, and dependency. Results: In 201 patients, median age 67 (range 34–97), BBB leakage, and interstitial fluid were higher in WMH than normal-appearing white matter; leakage in normal-appearing white matter increased with proximity to WMH (P , .0001), with WMH severity (P 5 .033), age (P 5 .03), and hypertension (P , .0001). BBB leakage in WMH predicted declining cognition at 1 year. Discussion: BBB leakage increases in normal-appearing white matter with WMH and predicts worsening cognition. Interventions to reduce BBB leakage may prevent SVD-associated dementia

Tracer kinetic assessment of blood–brain barrier leakage and blood volume in cerebral small vessel disease: Associations with disease burden and vascular risk factors

Funding Information: The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Wellcome Trust [grant number WT088134/Z/09/A ; SDJM, FC]; Row Fogo Charitable Trust (MCVH, FC, AKH, PAA); Scottish Funding Council Scottish Imaging Network A Platform for Scientific Excellence collaboration (JMW); NHS Lothian R + D Department (MJT); the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer’s Research UK and the Alzheimer’s Society (MS, FC, ES, JMW); the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease [reference number 16 CVD 05] (MS); and European Union Horizon 2020 [project number 666881, SVDs@Target] (MS, FC). We acknowledge the participants, their relatives, and carers for their participation in this study, and the staff of NHS Lothian Stroke Services and Brain Research Imaging Centre Edinburgh for their assistance in recruiting and assessing the patients.Peer reviewedPublisher PD