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Abstract Equation of state measurements at Jovian or stellar conditions are currently conducted by dynamic shock compression driven by multi-kilojoule multi-beam nanosecond-duration lasers. These experiments require precise design of the target and specific tailoring of the spatial and temporal laser profiles to reach the highest pressures. At the same time, the studies are limited by the low repetition rate of the lasers. Here, we show that by the irradiation of a thin wire with single-beam Joule-class short-pulse laser, a converging cylindrical shock is generated compressing the wire material to conditions relevant to the above applications. The shockwave was observed using Phase Contrast Imaging employing a hard X-ray Free Electron Laser with unprecedented temporal and spatial sensitivity. The data collected for Cu wires is in agreement with hydrodynamic simulations of an ablative shock launched by highly impulsive and transient resistive heating of the wire surface. The subsequent cylindrical shockwave travels toward the wire axis and is predicted to reach a compression factor of 9 and pressures above 800 Mbar. Simulations for astrophysical relevant materials underline the potential of this compression technique as a new tool for high energy density studies at high repetition rates.

Abstract Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning.

Abstract The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso). Case selection was based on the DICOM series description, gender, and imaging protocol, resulting in 882 patients (438 female) for a total of 900 CTs. Manual review and correction of proposals were conducted in a continuous quality control cycle. Only every fifth axial slice was annotated, yielding 20150 annotated slices from 28 data collections. For the reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. The SAROS dataset serves as an open-access resource for training and evaluating novel segmentation models, covering various scanner vendors and diseases.

Abstract We present structure and equation of state (EOS) measurements of biaxially orientated polyethylene terephthalate (PET, $$({\hbox {C}}_{10} {\hbox {H}}_8 {\hbox {O}}_4)_n$$ ( C 10 H 8 O 4 ) n , also called mylar) shock-compressed to ( $$155 \pm 20$$ 155 ± 20 ) GPa and ( $$6000 \pm 1000$$ 6000 ± 1000 ) K using in situ X-ray diffraction, Doppler velocimetry, and optical pyrometry. Comparing to density functional theory molecular dynamics (DFT-MD) simulations, we find a highly correlated liquid at conditions differing from predictions by some equations of state tables, which underlines the influence of complex chemical interactions in this regime. EOS calculations from ab initio DFT-MD simulations and shock Hugoniot measurements of density, pressure and temperature confirm the discrepancy to these tables and present an experimentally benchmarked correction to the description of PET as an exemplary material to represent the mixture of light elements at planetary interior conditions.

Abstract RNA interference (RNAi) technologies have recently been developed to control a growing number of agronomically significant fungal phytopathogens, including the white mold pathogen, Sclerotinia sclerotiorum. Exposure of this fungus to exogenous double-stranded RNA (dsRNA) results in potent RNAi-mediated knockdown of target genes’ transcripts, but it is unclear how the dsRNA can enter the fungal cells. In nematodes, specialized dsRNA transport proteins such as SID-1 facilitate dsRNA uptake, but for many other eukaryotes in which the dsRNA uptake mechanisms have been examined, endocytosis appears to mediate the uptake process. In this study, using live cell imaging, transgenic fungal cultures and endocytic inhibitors, we determined that the uptake mechanism in S. sclerotiorum occurs through clathrin-mediated endocytosis. RNAi-mediated knockdown of several clathrin-mediated endocytic genes’ transcripts confirmed the involvement of this cellular uptake process in facilitating RNAi in this fungus. Understanding the mode of dsRNA entry into the fungus will prove useful in designing and optimizing future dsRNA-based control methods and in anticipating possible mechanisms by which phytopathogens may develop resistance to this novel category of fungicides.

In this paper, we present an experiment that explores the plasma dynamics of a $7\,\mu\textrm{m}$ diameter carbon wire after being irradiated with a near-relativistic-intensity short pulse laser. Using an x-ray free electron laser pulse to measure the small angle x-ray scattering signal, we observe that the scattering surface is bent and prone to instability over tens of picoseconds. The dynamics of this process are consistent with the presence of a sharp, propagating shock front inside the wire, moving at a speed close to the hole boring velocity or that expected from a thermal shock at a few tens of Mbar.

Detection and diagnosis of early and subclinical stages of Alzheimer's Disease (AD) play an essential role in the implementation of intervention and prevention strategies. Neuroimaging techniques predominantly provide insight into anatomic structure changes associated with AD. Deep learning methods have been extensively applied towards creating and evaluating models capable of differentiating between cognitively unimpaired, patients with Mild Cognitive Impairment (MCI) and AD dementia. Several published approaches apply information fusion techniques, providing ways of combining several input sources in the medical domain, which contributes to knowledge of broader and enriched quality. The aim of this paper is to fuse sociodemographic data such as age, marital status, education and gender, and genetic data (presence of an apolipoprotein E (APOE)-ε4 allele) with Magnetic Resonance Imaging (MRI) scans. This enables enriched multi-modal features, that adequately represent the MRI scan visually and is adopted for creating and modeling classification systems capable of detecting amnestic MCI (aMCI). To fully utilize the potential of deep convolutional neural networks, two extra color layers denoting contrast intensified and blurred image adaptations are virtually augmented to each MRI scan, completing the Red-Green-Blue (RGB) color channels. Deep convolutional activation features (DeCAF) are extracted from the average pooling layer of the deep learning system Inception_v3. These features from the fused MRI scans are used as visual representation for the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) classification model. The proposed approach is evaluated on a sub-study containing 120 participants (aMCI = 61 and cognitively unimpaired = 59) of the Heinz Nixdorf Recall (HNR) Study with a baseline model accuracy of 76%. Further evaluation was conducted on the ADNI Phase 1 dataset with 624 participants (aMCI = 397 and cognitively unimpaired = 227) with a baseline model accuracy of 66.27%. Experimental results show that the proposed approach achieves 90% accuracy and 0.90 F1-Score at classification of aMCI vs. cognitively unimpaired participants on the HNR Study dataset, and 77% accuracy and 0.83 F1-Score on the ADNI dataset.

Adenovirus executes a finely tuned transcriptional program upon infection of a cell. To better understand the temporal dynamics of the viral transcriptional program we performed highly sensitive digital PCR on samples extracted from arrested human lung fibroblasts infected with human adenovirus 5 strain dl309. We show that the first transcript made from viral genomes is the virus associated non-coding RNA, in particular we detected abundant levels of virus associated RNA II four hours after infection. Activation of E1 and E4 occurred nearly simultaneously later in infection, followed by other early genes as well as late genes. Our study determined that genomes begin to replicate between 29 and 30 hours after infection. This study provides a comprehensive view of viral mRNA steady-state kinetics in arrested human cells using digital PCR.

The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes.

Human adenovirus has evolved to infect and replicate in terminally differentiated human epithelial cells, predominantly those within the airway, the gut, or the eye. To overcome the block to viral DNA replication present in these cells, the virus expresses the Early 1A proteins (E1A). These immediate early proteins drive cells into S-phase and induce expression of all other viral early genes. During infection, several E1A isoforms are expressed with proteins of 289, 243, 217, 171, and 55 residues being present for human adenovirus type 5. Here we examine the contribution that the two largest E1A isoforms make to the viral life cycle in growth-arrested normal human fibroblasts. Viruses that express E1A289R were found to replicate better than those that do not express this isoform. Importantly, induction of several viral genes was delayed in a virus expressing E1A243R, with several viral structural proteins undetectable by western blot. We also highlight the changes in E1A isoforms detected during the course of viral infection. Furthermore, we show that viral DNA replication occurs more efficiently, leading to higher number of viral genomes in cells infected with viruses that express E1A289R. Finally, induction of S-phase specific genes differs between viruses expressing different E1A isoforms, with those having E1A289R leading to, generally, earlier activation of these genes. Overall, we provide an overview of adenovirus replication using modern molecular biology approaches and further insights into the contribution that E1A isoforms make to the life cycle of human adenovirus in arrested human fibroblasts.

Modern high-energy density facilities allow us to bring matter to extreme states of density, temperature and velocity. Rigorous scaling laws proved that the relevant regimes could be reached, and those regimes are reproducibly achievable. Using powerful lasers and adapted target designs, similarity experiments in the POLAR project aim at studying the formation and dynamics of accretion shocks as found in magnetic cataclysmic variables. At the astrophysical scale, the system we consider is a column of infalling plasma collimated by a magnetic field onto the surface of a white dwarf. As matter hits the surface with supersonic velocity, a shock forms at the basis of the column and propagates upstream. In this paper, numerical simulations are presented in order to describe the experience and to give expectations concerning physical regimes reachable for future experiments on a kilojoule facility. In particular, our target design is discussed and improvements are detailed.