Disruption of mesoderm formation during cardiac differentiation due to developmental exposure to 13-cis-retinoic acid.

13-cis-retinoic acid (isotretinoin, INN) is an oral pharmaceutical drug used for the treatment of skin acne, and is also a known teratogen. In this study, the molecular mechanisms underlying INN-induced developmental toxicity during early cardiac differentiation were investigated using both human induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs). Pre-exposure of hiPSCs and hESCs to a sublethal concentration of INN did not influence cell proliferation and pluripotency. However, mesodermal differentiation was disrupted when INN was included in the medium during differentiation. Transcriptomic profiling by RNA-seq revealed that INN exposure leads to aberrant expression of genes involved in several signaling pathways that control early mesoderm differentiation, such as TGF-beta signaling. In addition, genome-wide chromatin accessibility profiling by ATAC-seq suggested that INN-exposure leads to enhanced DNA-binding of specific transcription factors (TFs), including HNF1B, SOX10 and NFIC, often in close spatial proximity to genes that are dysregulated in response to INN treatment. Altogether, these results identify potential molecular mechanisms underlying INN-induced perturbation during mesodermal differentiation in the context of cardiac development. This study further highlights the utility of human stem cells as an alternative system for investigating congenital diseases of newborns that arise as a result of maternal drug exposure during pregnancy

Commissioning, clinical implementation, and initial experience with a new brain tumor treatment package on a low-field MR-linac

To evaluate the image quality, dosimetric properties, setup reproducibility, and planar cine motion detection of a high-resolution brain coil and integrated stereotactic brain immobilization system that constitute a new brain treatment package (BTP) on a low-field magnetic resonance imaging (MRI) linear accelerator (MR-linac). Image quality of the high-resolution brain coil was evaluated with the 17 cm diameter spherical phantom and the American College of Radiology (ACR) Large MRI Phantom. Patient imaging studies approved by the institutional review board (IRB) assisted in selecting image acquisition parameters. Radiographic and dosimetric evaluation of the high-resolution brain coil and the associated immobilization devices was performed using dose calculations and ion chamber measurements. End-to-end testing was performed simulating a cranial lesion in a phantom. Inter-fraction setup variability and motion detection tests were evaluated on four healthy volunteers. Inter-fraction variability was assessed based on three repeat setups for each volunteer. Motion detection was evaluated using three-plane (axial, coronal, and sagittal) MR-cine imaging sessions, where volunteers were asked to perform a set of specific motions. The images were post-processed and evaluated using an in-house program. Contrast resolution of the high-resolution brain coil is superior to the head/neck and torso coils. The BTP receiver coils have an average HU value of 525 HU. The most significant radiation attenuation (3.14%) of the BTP, occurs through the lateral portion of the overlay board where the high-precision lateral-profile mask clips attach to the overlay. The greatest inter-fraction setup variability occurred in the pitch (average 1.08 degree) and translationally in the superior/inferior direction (average 4.88 mm). Three plane cine imaging with the BTP was able to detect large and small motions. Small voluntary motions, sub-millimeter in magnitude (maximum 0.9 mm), from motion of external limbs were detected. Imaging tests, inter-fraction setup variability, attenuation, and end-to-end measurements were quantified and performed for the BTP. Results demonstrate better contrast resolution and low contrast detectability that allows for better visualization of soft tissue anatomical changes relative to head/neck and torso coil systems

An AgMIP framework for improved agricultural representation in integrated assessment models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications

An AgMIP Framework for Improved Agricultural Representation in Integrated Assessment Models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications

Nat1 Deficiency Is Associated with Mitochondrial Dysfunction and Exercise Intolerance in Mice

SummaryWe recently identified human N-acetyltransferase 2 (NAT2) as an insulin resistance (IR) gene. Here, we examine the cellular mechanism linking NAT2 to IR and find that Nat1 (mouse ortholog of NAT2) is co-regulated with key mitochondrial genes. RNAi-mediated silencing of Nat1 led to mitochondrial dysfunction characterized by increased intracellular reactive oxygen species and mitochondrial fragmentation as well as decreased mitochondrial membrane potential, biogenesis, mass, cellular respiration, and ATP generation. These effects were consistent in 3T3-L1 adipocytes, C2C12 myoblasts, and in tissues from Nat1-deficient mice, including white adipose tissue, heart, and skeletal muscle. Nat1-deficient mice had changes in plasma metabolites and lipids consistent with a decreased ability to utilize fats for energy and a decrease in basal metabolic rate and exercise capacity without altered thermogenesis. Collectively, our results suggest that Nat1 deficiency results in mitochondrial dysfunction, which may constitute a mechanistic link between this gene and IR

Dynamic susceptibility contrast MRI with localized arterial input functions

Compared to gold-standard measurements of cerebral perfusion with positron emission tomography (PET) using H2[15O] tracers, measurements with dynamic susceptibility contrast (DSC) MR are more accessible, less expensive and less invasive. However, existing methods for analyzing and interpreting data from DSC MR have characteristic disadvantages that include sensitivity to incorrectly modeled delay and dispersion in a single, global arterial input function (AIF). We describe a model of tissue microcirculation derived from tracer kinetics which estimates for each voxel a unique, localized AIF (LAIF). Parameters of the model were estimated using Bayesian probability theory and Markov-chain Monte Carlo, circumventing difficulties arising from numerical deconvolution. Applying the new method to imaging studies from a cohort of fourteen patients with chronic, atherosclerotic, occlusive disease showed strong correlations between perfusion measured by DSC MR with LAIF and perfusion measured by quantitative PET with H2[15O]. Regression to PET measurements enabled conversion of DSC MR to a physiological scale. Regression analysis for LAIF gave estimates of a scaling factor for quantitation which described perfusion accurately in patients with substantial variability in hemodynamic impairment

Training students as interprofessional learning facilitators: An exploratory study highlighting the need to build confidence

Interprofessional learning (IPL) aims to equip students for future interprofessional and collaborative practice. Involving students as IPL facilitators is becoming increasingly commonplace as an attempt to catalyse the necessary transformation of our workforce needed to deliver truly integrated and person-centred care. Evidence in the literature highlights the key role of trained facilitators in reaching successful IPL outcomes. Some guidelines are available as to how we train staff facilitators, but little evidence is available that describes how to appropriately prepare student IPL facilitators. The aim of this exploratory study was to investigate whether student IPL facilitators felt that they were sufficiently prepared for their role. Data in the form of open-ended text-based responses from student facilitators (n = 9) were collated after an intervention where student facilitators had been given the role of supporting IPL. Data were analysed using principles of thematic analysis. Three main themes emerged: i) building confidence; ii) purpose of IPL; iii) learning moments. Student IPL facilitators who took part in this study felt that they were adequately prepared for their role. Findings show that preparing students for IPL facilitation has similar, yet unique, components compared to the training staff. In particular, this study highlighted a need for student facilitators to receive further preparation to help build their confidence. Involving students as IPL facilitators has great potential in staff and students joining forces to equip students for future interprofessional and collaborative practice that can deliver high-quality care

Epitaxial Graphene Growth on SiC Wafers

An in vacuo thermal desorption process has been accomplished to form epitaxial graphene (EG) on 4H- and 6H-SiC substrates using a commercial chemical vapor deposition reactor. Correlation of growth conditions and the morphology and electrical properties of EG are described. Raman spectra of EG on Si-face samples were dominated by monolayer thickness. This approach was used to grow EG on 50 mm SiC wafers that were subsequently fabricated into field effect transistors with fmax of 14 GHz.Comment: 215th Meeting of the Electrochemical Society, 8 pages, 8 figure

Learning and Long-Term Retention of Large-Scale Artificial Languages

Recovering discrete words from continuous speech is one of the first challenges facing language learners. Infants and adults can make use of the statistical structure of utterances to learn the forms of words from unsegmented input, suggesting that this ability may be useful for bootstrapping language-specific cues to segmentation. It is unknown, however, whether performance shown in small-scale laboratory demonstrations of “statistical learning” can scale up to allow learning of the lexicons of natural languages, which are orders of magnitude larger. Artificial language experiments with adults can be used to test whether the mechanisms of statistical learning are in principle scalable to larger lexicons. We report data from a large-scale learning experiment that demonstrates that adults can learn words from unsegmented input in much larger languages than previously documented and that they retain the words they learn for years. These results suggest that statistical word segmentation could be scalable to the challenges of lexical acquisition in natural language learning.National Science Foundation (U.S.) (NSF DDRIG #0746251

Accuracy and reliability of diffusion imaging models

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain\u27s white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL\u27s BedpostX [BPX], DSI Studio\u27s Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3\u27s Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI