Mobile quantum gravity sensor with unprecedented stability

Changes of surface gravity on Earth are of great interest in geodesy, earth sciences and natural resource exploration. They are indicative of Earth system's mass redistributions and vertical surface motion, and are usually measured with falling corner-cube- and superconducting gravimeters (FCCG and SCG). Here we report on absolute gravity measurements with a mobile quantum gravimeter based on atom interferometry. The measurements were conducted in Germany and Sweden over periods of several days with simultaneous SCG and FCCG comparisons. They show the best-reported performance of mobile atomic gravimeters to date with an accuracy of 39nm/s2, long-term stability of 0.5nm/s2 and short-term noise of 96nm/s2/√Hz. These measurements highlight the unique properties of atomic sensors. The achieved level of performance in a transportable instrument enables new applications in geodesy and related fields, such as continuous absolute gravity monitoring with a single instrument under rough environmental conditions.Peer Reviewe

Mobile quantum gravity sensor with unprecedented stability

Changes of surface gravity on Earth are of great interest in geodesy, earth sciences and natural resource exploration. They are indicative of Earth system's mass redistributions and vertical surface motion, and are usually measured with falling corner-cube- and superconducting gravimeters (FCCG and SCG). Here we report on absolute gravity measurements with a mobile quantum gravimeter based on atom interferometry. The measurements were conducted in Germany and Sweden over periods of several days with simultaneous SCG and FCCG comparisons. They show the best-reported performance of mobile atomic gravimeters to date with an accuracy of 39 nm/s^2, long-term stability of 0,5 nm/s^2 and short-term noise of 96 nm/s^2/(Hz)^1/2. These measurements highlight the unique properties of atomic sensors. The achieved level of performance in a transportable instrument enables new applications in geodesy and related Fields, such as continuous absolute gravity monitoring with a single instrument under rough environmental conditions

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures

Predicting microenvironment in CXCR4- and FAP-positive solid tumors - a pan-cancer machine learning workflow for theranostic target structures

Simple Summary Imaging based on positron emission tomography (PET) is a crucial part of up-to-date cancer care. For this purpose, PET employs and marks target structures at the cellular surface. Recently, C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) emerged as clinically relevant PET targets. However, it is unclear whether high levels of CXCR4 and FAP represent distinct cancer states—especially in solid tumors. Therefore, we established a machine learning model based on 9242 samples from 29 different cancer entities. Our analysis revealed that—in most solid tumors—high levels of CXCR4 were associated with immune cells infiltrating these tumors. Instead, FAP-positive tumors were characterized by high amounts of tumor vessels. Our machine learning approach potentially can identify the Achilles’ heel of tumors in a non-invasive manner—by performing PET without having to obtain tumor tissue beforehand. Abstract (1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database—representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets

An automated optimization pipeline for clinical-grade computer-assisted planning of high tibial osteotomies under consideration of weight-bearing

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs

IRX-2, a Novel Immunotherapeutic, Enhances Functions of Human Dendritic Cells

Background: In a recent phase II clinical trial for HNSCC patients, IRX-2, a cell-derived biologic, promoted T-cell infiltration into the tumor and prolonged overall survival. Mechanisms responsible for these IRX-2-mediated effects are unknown. We hypothesized that IRX-2 enhanced tumor antigen-(TA)-specific immunity by up-regulating functions of dendritic cells (DC). Methodology/Principal Findings: Monocyte-derived DC obtained from 18 HNSCC patients and 12 healthy donors were matured using IRX-2 or a mix of TNF-α, IL-1β and IL-6 ("conv. mix"). Multicolor flow cytometry was used to study the DC phenotype and antigen processing machinery (APM) component expression. ELISPOT and cytotoxicity assays were used to evaluate tumor-reactive cytotoxic T lymphocytes (CTL). IL-12p70 and IL-10 production by DC was measured by Luminex® and DC migration toward CCL21 was tested in transwell migration assays. IRX-2-matured DC functions were compared with those of conv. mix-matured DC. IRX-2-matured DC expressed higher levels (p<0.05) of CD11c, CD40, CCR7 as well as LMP2, TAP1, TAP2 and tapasin than conv. mix-matured DC. IRX-2-matured DC migrated significantly better towards CCL21, produced more IL-12p70 and had a higher IL12p70/IL-10 ratio than conv. mix-matured DC (p<0.05 for all). IRX-2-matured DC carried a higher density of tumor antigen-derived peptides, and CTL primed with these DC mediated higher cytotoxicity against tumor targets (p<0.05) compared to the conv. mix-matured DC. Conclusion: Excellent ability of IRX-2 to induce ex vivo DC maturation in HNSCC patients explains, in part, its clinical benefits and emphasizes its utility in ex vivo maturation of DC generated for therapy. © 2013 Schilling et al

Ambulatory assessment for physical activity research. State of the science, best practices and future directions

Technological and digital progress benefits physical activity (PA) research. Here we compiled expert knowledge on how Ambulatory Assessment (AA) is utilized to advance PA research, i.e., we present results of the 2nd International CAPA Workshop 2019 "Physical Activity Assessment - State of the Science, Best Practices, Future Directions" where invited researchers with experience in PA assessment, evaluation, technology and application participated. First, we provide readers with the state of the AA science, then we give best practice recommendations on how to measure PA via AA and shed light on methodological frontiers, and we furthermore discuss future directions. AA encompasses a class of methods that allows the study of PA and its behavioral, biological and physiological correlates as they unfold in everyday life. AA includes monitoring of movement (e.g., via accelerometry), physiological function (e.g., via mobile electrocardiogram), contextual information (e.g., via geolocation-tracking), and ecological momentary assessment (EMA; e.g., electronic diaries) to capture self-reported information. The strengths of AA are data assessment that near real-time, which minimizes retrospective biases in real-world settings, consequentially enabling ecological valid findings. Importantly, AA enables multiple assessments across time within subjects resulting in intensive longitudinal data (ILD), which allows unraveling within-person determinants of PA in everyday life. In this paper, we show how AA methods such as triggered e-diaries and geolocation-tracking can be used to measure PA and its correlates, and furthermore how these findings may translate into real-life interventions. In sum, AA provides numerous possibilities for PA research, especially the opportunity to tackle within-subject antecedents, concomitants, and consequences of PA as they unfold in everyday life. In-depth insights on determinants of PA could help us design and deliver impactful interventions in real-world contexts, thus enabling us to solve critical health issues in the 21st century such as insufficient PA and high levels of sedentary behavior. (DIPF/Orig.