Search

Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the spinal cord. Imaging biomarkers obtained from magnetic resonance imaging (MRI) scans can predict the functional recovery of individuals with SCI and help choose the optimal treatment strategy. Currently, most studies employ manual quantification of these MRI-derived biomarkers, which is a subjective and tedious task. In this work, we propose (i) a universal tool for the automatic segmentation of intramedullary SCI lesions, dubbed \texttt{SCIsegV2}, and (ii) a method to automatically compute the width of the tissue bridges from the segmented lesion. Tissue bridges represent the spared spinal tissue adjacent to the lesion, which is associated with functional recovery in SCI patients. The tool was trained and validated on a heterogeneous dataset from 7 sites comprising patients from different SCI phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic SCI, and degenerative cervical myelopathy). Tissue bridges quantified automatically did not significantly differ from those computed manually, suggesting that the proposed automatic tool can be used to derive relevant MRI biomarkers. \texttt{SCIsegV2} and the automatic tissue bridges computation are open-source and available in Spinal Cord Toolbox (v6.4 and above) via the \texttt{sct\_deepseg -task seg\_sc\_lesion\_t2w\_sci} and \texttt{sct\_analyze\_lesion} functions, respectively., Comment: Accepted at MICCAI AMAI 2024 workshop

Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods fail to consistently represent the hierarchical structure of WSIs due to a focus on high-fidelity patches. To tackle this, we propose Ultra-Resolution Cascaded Diffusion Models (URCDMs) which are capable of synthesising entire histopathology images at high resolutions whilst authentically capturing the details of both the underlying anatomy and pathology at all magnification levels. We evaluate our method on three separate datasets, consisting of brain, breast and kidney tissue, and surpass existing state-of-the-art multi-resolution models. Furthermore, an expert evaluation study was conducted, demonstrating that URCDMs consistently generate outputs across various resolutions that trained evaluators cannot distinguish from real images. All code and additional examples can be found on GitHub., Comment: arXiv admin note: text overlap with arXiv:2312.01152

We present an updated ephemeris and physical parameters for the exoplanet WASP-77 A b. In this effort, we combine 64 ground- and space-based transit observations, 6 space-based eclipse observations, and 32 radial velocity observations to produce the most precise orbital solution to date for this target, aiding in the planning of James Webb Space Telescope (JWST) and Ariel observations and atmospheric studies. We report a new orbital period of 1.360029395 +- 5.7e-8 days, a new mid-transit time of 2459957.337860 +- 4.3e-5 BJDTDB (Barycentric Julian Date in the Barycentric Dynamical Time scale; arXiv:1005.4415) and a new mid-eclipse time of 2459956.658192 +- 6.7e-5 BJDTDB. Furthermore, the methods presented in this study reduce the uncertainties in the planet mass to 1.6654 +- 4.5e-3 Mjup and orbital period to 1.360029395 +- 5.7e-8 days by factors of 15.1 and 10.9, respectively. Through a joint fit analysis comparison of transit data taken by space-based and citizen science-led initiatives, our study demonstrates the power of including data collected by citizen scientists compared to a fit of the space-based data alone. Additionally, by including a vast array of citizen science data from ExoClock, Exoplanet Transit Database (ETD), and Exoplanet Watch, we can increase our observational baseline and thus acquire better constraints on the forward propagation of our ephemeris than what is achievable with TESS data alone., Comment: Updated a co-author name. Added a co-author. Added an acknowledgement

Diagnoses from histopathology images rely on information from both high and low resolutions of Whole Slide Images. Ultra-Resolution Cascaded Diffusion Models (URCDMs) allow for the synthesis of high-resolution images that are realistic at all magnification levels, focusing not only on fidelity but also on long-distance spatial coherency. Our model beats existing methods, improving the pFID-50k [2] score by 110.63 to 39.52 pFID-50k. Additionally, a human expert evaluation study was performed, reaching a weighted Mean Absolute Error (MAE) of 0.11 for the Lower Resolution Diffusion Models and a weighted MAE of 0.22 for the URCDM., Comment: MedNeurIPS 2023 poster

Pathological alterations in the human vascular system underlie many chronic diseases, such as atherosclerosis and aneurysms. However, manually analyzing diagnostic images of the vascular system, such as computed tomographic angiograms (CTAs) is a time-consuming and tedious process. To address this issue, we propose a deep learning model to segment the vascular system in CTA images of patients undergoing surgery for peripheral arterial disease (PAD). Our study focused on accurately segmenting the vascular system (1) from the descending thoracic aorta to the iliac bifurcation and (2) from the descending thoracic aorta to the knees in CTA images using deep learning techniques. Our approach achieved average Dice accuracies of 93.5% and 80.64% in test dataset for (1) and (2), respectively, highlighting its high accuracy and potential clinical utility. These findings demonstrate the use of deep learning techniques as a valuable tool for medical professionals to analyze the health of the vascular system efficiently and accurately. Please visit the GitHub page for this paper at https://github.com/pip-alireza/TransOnet., Comment: Accepted for the 2023 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, USA

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants ($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with aggressive data augmentation and a regression-based loss function, were used to train a U-Net model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different loss functions and domain generalization methods. Our results show that using the soft segmentations along with a regression loss function reduces CSA variability ($p < 0.05$, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects., Comment: Revision Submitted to Medical Image Analysis

We provide a generalisation of Kripke semantics for Petr Hajek's Basic Logic and prove soundness and completeness of the same with respect to our semantics. We find this semantics easily specialises to the linearly-ordered Kripke frames for Godel-Dummett logic, which BL properly contains. Our soundness, deduction theorem and completeness arguments further strengthen this analogy. This paper extends the insights of our previous paper, "A Kripke Semantics for Intuitionistic Lukasiewicz logic," to the case of Hajeks' BL., Comment: In Proceedings AMSLO 2023, arXiv:2308.03679

To optimize the performance of the Collinear Resonance Ionization Spectroscopy (CRIS) experiment at CERN-ISOLDE, technical upgrades are continuously introduced, aiming to enhance its sensitivity, precision, stability, and efficiency. Recently, a voltage-scanning setup was developed and commissioned at CRIS, which improved the scanning speed by a factor of three as compared to the current laser-frequency scanning approach. This leads to faster measurements of the hyperfine structure for systems with high yields (more than a few thousand ions per second). Additionally, several beamline sections have been redesigned and manufactured, including a new field-ionization unit, a sharper electrostatic bend, and improved ion optics. The beamline upgrades are expected to yield an improvement of at least a factor of 5 in the signal-to-noise ratio by suppressing the non-resonant laser ions and providing time-of-flight separation between the resonant ions and the collisional background. Overall, the presented developments will further improve the selectivity, sensitivity, and efficiency of the CRIS technique., Comment: 10 pages. Under review at NIM B as part of the proceedings of EMIS 2022 at RAON, South Korea

A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44% and 86% respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed., Comment: 23 pages, 10 figures. For the original information sent to RR participants, see https://zenodo.org/record/7506365 . For the anonymized results and Jupyter notebook for analysis, see https://zenodo.org/record/7509710

Spin Hall oscillators (SHOs) based on bilayers of a ferromagnet (FM) and a non-magnetic heavy metal (HM) are electrically tunable nanoscale microwave signal generators. Achieving high output power in SHOs requires driving large-amplitude magnetization dynamics by a direct spin Hall current. The maximum possible amplitude of such oscillations with the precession cone angle nearing $90^\circ$ is predicted for FM layers with easy-plane magnetic anisotropy and spin Hall current polarization perpendicular to the easy plane. While many FMs exhibit natural easy-plane anisotropy in the FM film plane, the spin Hall current in a HM|FM bilayer is polarized in this plane and thus cannot drive large-amplitude magneto-dynamics. Here we present a new type of SHO engineered to have the easy-plane anisotropy oriented normal to the film plane, enabling large-amplitude easy-plane dynamics driven by spin Hall current. Our experiments and micromagnetic simulations demonstrate that the desired easy-plane anisotropy can be achieved by tuning the magnetic shape anisotropy and perpendicular magnetic anisotropy in a nanowire SHO, leading to a significant enhancement of the generated microwave power. The easy-plane SHO experimentally demonstrated here is an ideal candidate for realization of a spintronic spiking neuron. Our results provide a new approach to design of high-power SHOs for wireless communications, neuromorphic computing, and microwave assisted magnetic recording., Comment: Main: 12 pages, 6 figures. Supplementary Material: 5 pages, 6 figures

Ground-level particle detection is now a well-established approach to TeV gamma-ray astronomy. Detection of Cherenkov light produced in water-filled detection units is a proven and cost-effective method. Here we discuss the optimization of the units towards the future Southern Wide-field Gamma-ray Observatory (SWGO). In this context, we investigate a new type of configuration in which each water Cherenkov detector (WCD) unit in the array comprises two chambers with black or reflective walls and a single photomultiplier tube (PMT) in each chamber. We find that this is a cost-effective approach that improves the performance of the WCD array with respect to current approaches. A shallow lower chamber with a PMT facing downwards enables muon tagging and the identification of hadron-induced air showers, which are the primary source of background in gamma-ray astronomy. We investigate how gamma/hadron separation power and achievable angular resolution depend on the geometry and wall reflectivity of the detector units in this configuration. We find that excellent angular resolution, background rejection power and low-energy response are achievable in this double-layer configuration, with the aid of reflective surfaces in both chambers., Comment: 17 pages, 20 figures

We introduce a stochastic household model for vector-borne diseases, in particular as relevant to prominent vectors belonging to the Aedes genus and hence the Zika, chikungunya, and dengue viruses. In this model, vectors remain local to each household, while hosts mix for a proportion of their time in their household and the remaining proportion in the population at random. This is approximated with a two-type branching process, allowing us to efficiently calculate a number of useful epidemiological characteristics, such as reproductive numbers, early growth rates and household-type proportions, offspring distributions, probabilities of a major outbreak, and within-household final size distributions. We compare control interventions of spraying -- reducing the number of vectors in each household -- and social-distancing -- having individuals spend more time at home -- in terms of these characteristics.

We present results of the detailed study of several hundred Hamamatsu H12700 Multianode Photomultiplier Tubes (MaPMTs), characterizing their response to the Cherenkov light photons in the second Ring Imaging Cherenkov detector, a part of the CLAS12 upgrade at Jefferson Lab. The total number of pixels studied was 25536. The single photoelectron spectra were measured for each pixel at different high voltages and light intensities of the laser test setup. Using the same dedicated front-end electronics as in the first RICH detector, the setup allowed us to characterize each pixel's properties such as gain, quantum efficiency, signal crosstalk between neighboring pixels, and determine the signal threshold values to optimize their efficiency to detect Cherenkov photons. A recently published state-of-the-art mathematical model, describing photon detector response functions measured in low light conditions, was extended to include the description of the crosstalk contributions to the spectra. The database of extracted parameters will be used for the final selection of the MaPMTs, their arrangement in the new RICH detector, and the optimization of the operational settings of the front-end electronics. The results show that the characteristics of the H12700 MaPMTs satisfy our requirements for the position-sensitive single photoelectron detectors.

We present a joint experimental and theoretical study of parametric resonance of spin wave eigenmodes in Ni$_{80}$Fe$_{20}$/Pt bilayer nanowires. Using electrically detected magnetic resonance, we measure the spectrum of spin wave eigenmodes in transversely magnetized nanowires and study parametric excitation of these eigenmodes by a microwave magnetic field. We also develop an analytical theory of spin wave eigenmodes and their parametric excitation in the nanowire geometry that takes into account magnetic dilution at the nanowire edges. We measure tuning of the parametric resonance threshold by antidamping spin Hall torque from a direct current for the edge and bulk eigenmodes, which allows us to independently evaluate frequency, damping and ellipticity of the modes. We find good agreement between theory and experiment for parametric resonance of the bulk eigenmodes but significant discrepancies arise for the edge modes. The data reveals that ellipticity of the edge modes is significantly lower than expected, which can be attributed to strong modification of magnetism at the nanowire edges. Our work demonstrates that parametric resonance of spin wave eigenmodes is a sensitive probe of magnetic properties at edges of thin-film nanomagnets., Comment: 24 pages, 9 figures

Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often, investigators manually classify the polysomnography into vigilance states, which is time-consuming, requires extensive training, and is prone to inter-scorer variability. While many works have successfully developed automated vigilance state classifiers based on multiple EEG channels, we aim to produce an automated and open-access classifier that can reliably predict vigilance state based on a single cortical electroencephalogram (EEG) from rodents to minimize the disadvantages that accompany tethering small animals via wires to computer programs. Approximately 427 hours of continuously monitored EEG, electromyogram (EMG), and activity were labeled by a domain expert out of 571 hours of total data. Here we evaluate the performance of various machine learning techniques on classifying 10-second epochs into one of three discrete classes: paradoxical, slow-wave, or wake. Our investigations include Decision Trees, Random Forests, Naive Bayes Classifiers, Logistic Regression Classifiers, and Artificial Neural Networks. These methodologies have achieved accuracies ranging from approximately 74% to approximately 96%. Most notably, the Random Forest and the ANN achieved remarkable accuracies of 95.78% and 93.31%, respectively. Here we have shown the potential of various machine learning classifiers to automatically, accurately, and reliably classify vigilance states based on a single EEG reading and a single EMG reading., Comment: 6 pages, IEEE Annual Conf. on Computational Science & Computational Intelligence (CSCI), December 2021

The Education Endowment Foundation (EEF) review of the impact of digital technology on learning, "The Impact of Digital Technology on Learning: A Summary for the Education Endowment Foundation. Full Report" (Higgins et al., 2012) (ED612174), found positive benefits but noted that how technology is used (the pedagogy) is key and that future research should focus on identifying the specific conditions under which a positive impact is found. The authors sought to identify meta-analyses which summarise the effects of the use of digital technology in schools, and their effects on attainment. This review sought to identify relevant literature published since the previous work was completed in order to provide an update to that synthesis. The authors therefore searched for papers published since 2012 using a very similar approach to Higgins et al. (2012). Following the rapid assessment of meta-analyses, they conducted an evidence review (REA) of individual studies which described research undertaken in English second rapid schools which has been published since 2012. Meta-analyses synthesise the results from a number of primary studies, and they therefore offer a thorough methodology for synthesising knowledge within a particular area. By systematically searching for meta-analyses the review therefore offers a comprehensive overview of international research into the effect of technology on attainment in schools. [For the related guidance report "Using Digital Technology to Improve Learning. Guidance Report," see ED612112.]

The presence of current sheet instabilities, such as the tearing mode instability, are needed to account for the observed rate of energy release in solar flares. Insights into these current sheet dynamics can be revealed by the behaviour of flare ribbon substructure, as magnetic reconnection accelerates particles down newly reconnected field lines into the chromosphere to mark the flare footpoints. Behaviour in the ribbons can therefore be used to probe processes occurring in the current sheet. In this study, we use high-cadence (1.7 s) IRIS Slit Jaw Imager observations to probe for the growth and evolution of key spatial scales along the flare ribbons - resulting from dynamics across the current sheet of a small solar flare on December 6th 2016. Combining analysis of spatial scale growth with Si IV non-thermal velocities, we piece together a timeline of flare onset for this confined event, and provide evidence of the tearing-mode instability triggering a cascade and inverse cascade towards a power spectrum consistent with plasma turbulence., Comment: 10 pages, 5 figures; accepted for publication to ApJ