Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Involvement of eNAMPT/TLR4 inflammatory signaling in progression of non-alcoholic fatty liver disease, steatohepatitis, and fibrosis.

Although the progression of non-alcoholic fatty liver disease (NAFLD) from steatosis to steatohepatitis (NASH) and cirrhosis remains poorly understood, a critical role for dysregulated innate immunity has emerged. We examined the utility of ALT-100, a monoclonal antibody (mAb), in reducing NAFLD severity and progression to NASH/hepatic fibrosis. ALT-100 neutralizes eNAMPT (extracellular nicotinamide phosphoribosyltransferase), a novel damage-associated molecular pattern protein (DAMP) and Toll-like receptor 4 (TLR4) ligand. Histologic and biochemical markers were measured in liver tissues and plasma from human NAFLD subjects and NAFLD mice (streptozotocin/high-fat diet-STZ/HFD, 12 weeks). Human NAFLD subjects (n = 5) exhibited significantly increased NAMPT hepatic expression and significantly elevated plasma levels of eNAMPT, IL-6, Ang-2, and IL-1RA compared to healthy controls, with IL-6 and Ang-2 levels significantly increased in NASH non-survivors. Untreated STZ/HFD-exposed mice displayed significant increases in NAFLD activity scores, liver triglycerides, NAMPT hepatic expression, plasma cytokine levels (eNAMPT, IL-6, and TNFα), and histologic evidence of hepatocyte ballooning and hepatic fibrosis. Mice receiving the eNAMPT-neutralizing ALT-100 mAb (0.4 mg/kg/week, IP, weeks 9 to 12) exhibited marked attenuation of each index of NASH progression/severity. Thus, activation of the eNAMPT/TLR4 inflammatory pathway contributes to NAFLD severity and NASH/hepatic fibrosis. ALT-100 is potentially an effective therapeutic approach to address this unmet NAFLD need.

Gender Differences in Core Muscle Morphology of Elite Alpine Skiers: Insights from Ultrasonography.

This study investigates gender differences in core muscle morphology among elite alpine skiers using ultrasonography, highlighting significant disparities that could influence training and injury prevention strategies. METHODS: A cross-sectional design was employed, examining ultrasound imaging (USI) in 22 elite skiers (11 male, 11 female) to assess the thickness of the external oblique (EO), internal oblique (IO), transversus abdominis (TrAb), and rectus abdominis (RA) muscles. RESULTS: Significant differences were noted, with male skiers displaying greater muscle thickness, particularly in the right IO and RA and left IO, EO, TrAb, and RA. CONCLUSIONS: These findings suggest that male and female skiers may require different training approaches to optimize performance and reduce injury risks. This research contributes to a deeper understanding of the physical demands on elite skiers and underscores the need for gender-specific training regimens to enhance athletic outcomes and prevent injuries.

Reduction in the incidence of invasive infections caused by encapsulated bacteria after the onset of the COVID-19 pandemic.

Interaction between severe acute respiratory coronavirus 2 (SARS-CoV-2) and IIEB remains under investigation. Objective: to compare IIEB incidence before and during COVID-19 pandemic, and assess incidence of coinfection with COVID-19 and case fatality. A cross-sectional study was performed on data from a centralized microbiology laboratory serving a network of healthcare centers comprising 713 pediatric and adult inpatient beds, expanded by 20% during the pandemic. Three periods were evaluated: (1) pre-pandemic: March 1, 2019-February 29, 2020; (2) pandemic year 1: March 1, 2020-February 28, 2021; (3) pandemic year 2: March 1, 2021-July 31, 2021. Descriptive statistical analysis was performed. 56 502 samples (96% blood cultures) from 27224 patients were analyzed. Of these, 54 samples (from 54 patients) were positive for encapsulated bacteria. IIEB incidence was: 167.4, 32.6, and 50.4 per 100000 samples for periods 1, 2, and 3, respectively. Twelve IIEB episodes occurred during the pandemic period: 10 Streptococcus pneumoniae, and 2 Haemophilus influenzae, of which 7 were SARS-CoV-2/S. pneumoniae coinfections, with an incidence of 5.68 per 10000 COVID-19-related hospitalizations (0.056%). IIEB case fatality was 31%, 29%, and 60% for each period, respectively, 3/7 patients with coinfection died (43%). Case fatality for invasive pneumococcal disease (IPD) in patients without COVID-19, was 32.5%. Significant reduction in IIEB incidence was observed during the pandemic, coinciding with implementation of containment measures. The incidence of SARS-CoV-2/S. pneumoniae coinfection was low, with higher case fatality than IPD patients without COVID-19.

A limited set of transcriptional programs define major cell types.

We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease.

Postnatal catch-up growth in term newborns with altered fetal weight patterns. The GROWIN study.

BACKGROUND: Small for gestational age (SGA) perform a postnatal catch-up growth to recover their genetic trajectory. We studied the postnatal catch-up growth pattern of fetuses born with an appropriate-for-gestational-age (AGA) weight but with fetal growth deceleration (FGD) to explore whether they catch up. METHODS: Nine hundred and sixty-six newborns at Villalba University General Hospital (HUGV), were followed from 34 to 37 weeks to birth. Z-scores, adjusted for sex and age, of weight, length, and BMI at 3, 6, 9, and 12 months were calculated. We define catch-up as an increase in z-score greater than 0.67 SD in the growth curves. RESULTS: AGA FGD had lower mean weight and length than AGA non-FGD at all time points; BMI was lower until 3 months. AGA FGD had a lower weight, length, and BMI z-score (until 9, 6 months, and at birth, respectively) than AGA non-FGD. AGA FGD newborns had a significantly increased likelihood of weight catch-up at 3 months (OR 1.79; 95% CI: 1.16, 2.78; p = 0.009) and BMI in all investigated periods (OR 1.90; 95% CI 1.30, 2.78; p < 0.001 at 3 months), compared to AGA non-FGD newborns. CONCLUSIONS: AGA FGD newborns perform catch-up growth, especially in weight and BMI, in the first year of life, compared to AGA non-FGD. IMPACT: Appropriate-for-gestational-age (AGA) newborns with fetal growth deceleration (FGD), between the third trimester of pregnancy and delivery, present a lower weight and height, during the first year of life, compared to AGA non-FGD. Appropriate-for-gestational-age (AGA) newborns with fetal growth deceleration (FGD), between the third trimester of pregnancy and delivery, present a higher likelihood of weight catch-up in the first 3 months of life and of BMI in the first year compared to AGA non-FGD. AGA FGD experienced early weight and BMI catch-up, especially in the first 3 months of life, like SGA. This finding should be considered in the future follow-up.

COSIBAS Platform-Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange.

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.

A Novel Multi-Objective Binary Chimp Optimization Algorithm for Optimal Feature Selection: Application of Deep-Learning-Based Approaches for SAR Image Classification.

Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric synthetic aperture radar (POLSAR) representation models have been suggested, most of which are based on deep convolutional neural networks (DCNNs). In this paper, we propose a hybrid approach of a new multi-objective binary chimp optimization algorithm (MOBChOA) and DCNN for optimal feature selection. We implemented the proposed method to classify POLSAR images from San Francisco, USA. To do so, we first performed the necessary preprocessing, including speckle reduction, radiometric calibration, and feature extraction. After that, we implemented the proposed MOBChOA for optimal feature selection. Finally, we trained the fully connected DCNN to classify the pixels into specific land-cover labels. We evaluated the performance of the proposed MOBChOA-DCNN in comparison with nine competitive methods. Our experimental results with the POLSAR image datasets show that the proposed architecture had a great performance for different important optimization parameters. The proposed MOBChOA-DCNN provided fewer features (27) and the highest overall accuracy. The overall accuracy values of MOBChOA-DCNN on the training and validation datasets were 96.89% and 96.13%, respectively, which were the best results. The overall accuracy of SVM was 89.30%, which was the worst result. The results of the proposed MOBChOA on two real-world benchmark problems were also better than the results with the other methods. Furthermore, it was shown that the MOBChOA-DCNN performed better than methods from previous studies.

Abdominal T2-Weighted Imaging and T2 Mapping Using a Variable Flip Angle Radial Turbo Spin-Echo Technique.

BACKGROUND: T2 mapping is of great interest in abdominal imaging but current methods are limited by low resolution, slice coverage, motion sensitivity, or lengthy acquisitions. PURPOSE: Develop a radial turbo spin-echo technique with refocusing variable flip angles (RADTSE-VFA) for high spatiotemporal T2 mapping and efficient slice coverage within a breath-hold and compare to the constant flip angle counterpart (RADTSE-CFA). STUDY TYPE: Prospective technical efficacy. SUBJECTS: Testing performed on agarose phantoms and 12 patients. Focal liver lesion classification tested on malignant (N = 24) and benign (N = 11) lesions. FIELD STRENGTH/SEQUENCE: 1.5 T/RADTSE-VFA, RADTSE-CFA. ASSESSMENT: A constrained objective function was used to optimize the refocusing flip angles. Phantom and/or in vivo data were used to assess relative contrast, T2 estimation, specific absorption rate (SAR), and focal liver lesion classification. STATISTICAL TESTS: t-Tests or Mann-Whitney Rank Sum tests were used. RESULTS: Phantom data did not show significant differences in mean relative contrast (P = 0.10) and T2 accuracy (P = 0.99) between RADTSE-VFA and RADTSE-CFA. Adding noise caused T2 overestimation predominantly for RADTSE-CFA and low T2 values. In vivo results did not show significant differences in mean spleen-to-liver (P = 0.62) and kidney-to-liver (P = 0.49) relative contrast between RADTSE-VFA and RADTSE-CFA. Mean T2 values were not significantly different between the two techniques for spleen (T2VFA  = 109.2 ± 12.3 msec; T2CFA  = 110.7 ± 11.1 msec; P = 0.78) and kidney-medulla (T2VFA  = 113.0 ± 8.7 msec; T2CFA  = 114.0 ± 8.6 msec; P = 0.79). Liver T2 was significantly higher for RADTSE-CFA (T2VFA  = 52.6 ± 6.6 msec; T2CFA  = 60.4 ± 8.0 msec) consistent with T2 overestimation in the phantom study. Focal liver lesion classification had comparable T2 distributions for RADTSE-VFA and RADTSE-CFA for malignancies (P = 1.0) and benign lesions (P = 0.39). RADTSE-VFA had significantly lower SAR than RADTSE-CFA increasing slice coverage by 1.5. DATA CONCLUSION: RADTSE-VFA provided noise-robust T2 estimation compared to the constant flip angle counterpart while generating T2-weighted images with comparable contrast. The VFA scheme minimized SAR improving slice efficiency for breath-hold imaging. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems.

The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.

Endothelial eNAMPT amplifies pre-clinical acute lung injury: efficacy of an eNAMPT-neutralising monoclonal antibody.

RATIONALE: The severe acute respiratory syndrome coronavirus 2/coronavirus disease 2019 pandemic has highlighted the serious unmet need for effective therapies that reduce acute respiratory distress syndrome (ARDS) mortality. We explored whether extracellular nicotinamide phosphoribosyltransferase (eNAMPT), a ligand for Toll-like receptor (TLR)4 and a master regulator of innate immunity and inflammation, is a potential ARDS therapeutic target. METHODS: Wild-type C57BL/6J or endothelial cell (EC)-cNAMPT -/- knockout mice (targeted EC NAMPT deletion) were exposed to either a lipopolysaccharide (LPS)-induced ("one-hit") or a combined LPS/ventilator ("two-hit")-induced acute inflammatory lung injury model. A NAMPT-specific monoclonal antibody (mAb) imaging probe (99mTc-ProNamptor) was used to detect NAMPT expression in lung tissues. Either an eNAMPT-neutralising goat polyclonal antibody (pAb) or a humanised monoclonal antibody (ALT-100 mAb) were used in vitro and in vivo. RESULTS: Immunohistochemical, biochemical and imaging studies validated time-dependent increases in NAMPT lung tissue expression in both pre-clinical ARDS models. Intravenous delivery of either eNAMPT-neutralising pAb or mAb significantly attenuated inflammatory lung injury (haematoxylin and eosin staining, bronchoalveolar lavage (BAL) protein, BAL polymorphonuclear cells, plasma interleukin-6) in both pre-clinical models. In vitro human lung EC studies demonstrated eNAMPT-neutralising antibodies (pAb, mAb) to strongly abrogate eNAMPT-induced TLR4 pathway activation and EC barrier disruption. In vivo studies in wild-type and EC-cNAMPT -/- mice confirmed a highly significant contribution of EC-derived NAMPT to the severity of inflammatory lung injury in both pre-clinical ARDS models. CONCLUSIONS: These findings highlight both the role of EC-derived eNAMPT and the potential for biologic targeting of the eNAMPT/TLR4 inflammatory pathway. In combination with predictive eNAMPT biomarker and NAMPT genotyping assays, this offers the opportunity to identify high-risk ARDS subjects for delivery of personalised medicine.

A New Variance-Covariance Matrix for Improving Positioning Accuracy in High-Speed GPS Receivers.

One of the main challenges in using GPS is reducing the positioning accuracy in high-speed conditions. In this contribution, by considering the effect of spatial correlation between observations in estimating the covariances, we propose a model for determining the variance-covariance matrix (VCM) that improves the positioning accuracy without increasing the computational load. In addition, we compare the performance of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) combined with different dynamic models, along with the proposed VCM in GPS positioning at high speeds. To review and test the methods, we used six motion scenarios with different speeds from medium to high and examined the positioning accuracy of the methods and some of their statistical characteristics. The simulation results demonstrate that the EKF algorithm based on the Gauss-Markov model, along with the proposed VCM (based on the sinusoidal function and considering spatial correlations), performs better and provides at least 30% improvement in the positioning, compared to the other methods.

Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks.

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

Towards Outlier Sensor Detection in Ambient Intelligent Platforms-A Low-Complexity Statistical Approach.

Sensor networks in real-world environments, such as smart cities or ambient intelligent platforms, provide applications with large and heterogeneous sets of data streams. Outliers-observations that do not conform to an expected behavior-has then turned into a crucial task to establish and maintain secure and reliable databases in this kind of platforms. However, the procedures to obtain accurate models for erratic observations have to operate with low complexity in terms of storage and computational time, in order to attend the limited processing and storage capabilities of the sensor nodes in these environments. In this work, we analyze three binary classifiers based on three statistical prediction models-ARIMA (Auto-Regressive Integrated Moving Average), GAM (Generalized Additive Model), and LOESS (LOcal RegrESSion)-for outlier detection with low memory consumption and computational time rates. As a result, we provide (1) the best classifier and settings to detect outliers, based on the ARIMA model, and (2) two real-world classified datasets as ground truths for future research.

Measuring the Tangle of Three-Qubit States.

We present a quantum circuit that transforms an unknown three-qubit state into its canonical form, up to relative phases, given many copies of the original state. The circuit is made of three single-qubit parametrized quantum gates, and the optimal values for the parameters are learned in a variational fashion. Once this transformation is achieved, direct measurement of outcome probabilities in the computational basis provides an estimate of the tangle, which quantifies genuine tripartite entanglement. We perform simulations on a set of random states under different noise conditions to asses the validity of the method.

First-Line Diagnostic Evaluation with MRI of Children Suspected of Having Acute Appendicitis.

Background Advances in abdominal MRI have enabled rapid, free-breathing imaging without the need for intravenous or oral contrast material. The use of MRI as the primary imaging modality for suspected appendicitis has not been previously studied. Purpose To determine the diagnostic performance of MRI as the initial imaging modality in children suspected of having acute appendicitis. Materials and Methods The study included consecutive patients 18 years of age and younger presenting with acute abdominal pain at a tertiary care institution from January 2013 through June 2016 who subsequently underwent an unenhanced MRI examination as the primary diagnostic imaging modality. Electronic medical records and radiology reports were retrospectively evaluated for the feasibility and diagnostic performance of MRI, with surgical pathology and follow-up electronic records as reference standards. Statistical analyses were performed by using simple binomial proportions to quantify sensitivity, specificity, and accuracy, and exact 95% confidence intervals (CIs) were obtained. Results After exclusions, 402 patients (median age: 13 years; interquartile range [IQR], 9-15 years; 235 female patients; 167 male patients) were included. Sedation for MRI was required in 13 of 402 patients (3.2%; 95% CI: 1.7%, 5.5%). The appendix was visualized in 349 of 402 patients (86.8%; 95% CI: 83.1%, 90%); for the remaining patients, a diagnosis was provided on the basis of secondary signs of appendicitis. The sensitivity, specificity, and accuracy of MRI as the primary diagnostic imaging modality for the evaluation of acute appendicitis were 97.9% (95 of 97; 95% CI: 92.8%, 99.8%), 99% (302 of 305; 95% CI: 97.2%, 99.8%), and 98.8% (397 of 402; 97.1%, 99.6%), respectively. Among patients with negative findings for appendicitis at MRI, an alternate diagnosis was provided in 113 of 304 patients (37.2%; 95% CI: 31.7%, 42.9%). Conclusion When performed as the initial imaging modality in children suspected of having acute appendicitis, MRI examinations had high diagnostic performance for the diagnosis of acute appendicitis and in providing alternative diagnoses. © RSNA, 2019 See also the editorial by Dillman and Trout in this issue.

Radial streak artifact reduction using phased array beamforming.

PURPOSE: A new method for streak artifact reduction in radial MRI based on phased array filtering. THEORY: Radial imaging in applications that require large fields-of-view can be susceptible to streaking artifacts due to gradient nonlinearities. Coil removal methods prune the coils contributing the most to streaking artifacts at the expense of signal loss. Phased array beamforming is a form of spatial filtering used to suppress unwanted signals. The proposed method uses interference covariance generated from the streaking artifact samples which are manually extracted with phased array beamforming to suppress streaking in the images. METHODS: The performance of the proposed method was evaluated on abdomen radial fast spin echo images acquired on a 1.5T Siemens scanner and compared with previously proposed methods. RESULTS: Our results demonstrate that the proposed method can effectively suppress streaking artifacts without any noticeable loss in signal levels. Coil removal methods can suppress streaks as well but they may incur significant signal loss due to coil pruning. Quantitative metrics also demonstrate the superiority of the proposed method over earlier methods. CONCLUSION: The use of interference covariance with phased array beamforming can help reduce streaking artifacts.

An efficient 3D stack-of-stars turbo spin echo pulse sequence for simultaneous T2-weighted imaging and T2 mapping.

PURPOSE: To design a pulse sequence for efficient 3D T2-weighted imaging and T2 mapping. METHODS: A stack-of-stars turbo spin echo pulse sequence with variable refocusing flip angles and a flexible pseudorandom view ordering is proposed for simultaneous T2-weighted imaging and T2 mapping. An analytical framework is introduced for the selection of refocusing flip angles to maximize relative tissue contrast while minimizing T2 estimation errors and maintaining low specific absorption rate. Images at different echo times are generated using a subspace constrained iterative reconstruction algorithm. T2 maps are obtained by modeling the signal evolution using the extended phase graph model. The technique is evaluated using phantoms and demonstrated in vivo for brain, knee, and carotid imaging. RESULTS: Numerical simulations demonstrate an improved point spread function with the proposed pseudorandom view ordering compared to golden angle view ordering. Phantom experiments show that T2 values estimated from the stack-of-stars turbo spin echo pulse sequence with variable refocusing flip angles have good concordance with spin echo reference values. In vivo results show the proposed pulse sequence can generate qualitatively comparable T2-weighted images as conventional Cartesian 3D SPACE in addition to simultaneously generating 3D T2 maps. CONCLUSION: The proposed stack-of-stars turbo spin echo pulse sequence with pseudorandom view ordering and variable refocusing flip angles allows high resolution isotropic T2 mapping in clinically acceptable scan times. The optimization framework for the selection of refocusing flip angles improves T2 estimation accuracy while generating T2-weighted contrast comparable to conventional Cartesian imaging.

Rapid high-resolution T1 mapping using a highly accelerated radial steady-state free-precession technique.

BACKGROUND: T1 mapping is often used in some clinical protocols. Existing techniques are limited in slice coverage, and/or spatial-temporal resolution, or require long acquisitions. Here we present a multi-slice inversion-recovery (IR) radial steady-state free precession (radSSFP) pulse sequence combined with a principal component (PC) based reconstruction that overcomes these limitations. PURPOSE: To develop a fast technique for multi-slice high-resolution T1 mapping. STUDY TYPE: Technical efficacy study done prospectively. PHANTOM/SUBJECTS: IR-radSSFP was tested in phantoms, five healthy volunteers, and four patients with abdominal lesions. FIELD STRENGTH/SEQUENCE: IR-radSSFP was implemented at 3T. ASSESSMENT: Computer simulations were performed to optimize the flip angle for T1 estimation; testing was done in phantoms using as reference an IR spin-echo pulse sequence. T1 mapping with IR-radSSFP was also assessed in vivo (brain and abdomen) and T1 values were compared with literature. T1 maps were also compared with a radial IR-FLASH technique. STATISTICAL TESTS: A two-tailed t-test was used to compare T1 values in phantoms. A repeatability study was carried out in vivo using Bland-Altman analysis. RESULTS: Simulations and phantom experiments showed that a flip angle of 20˚ was optimal for T1 mapping. When comparing single to multi-slice experiments in phantoms there were no significant differences between the means T1 values (P = 0.0475). In vivo results show that T1 maps with spatial resolution as high as 0.69 mm × 0.69 mm × 2.00 mm (brain) and 0.83 mm × 0.83 mm × 3.00 mm (abdomen) can be generated for 84 brain slices in 3 min and 10 abdominal slices in a breath-hold; T1 values were comparable to those reported in literature. The coefficients of variation from the repeatability study were 1.7% for brain and 2.5-2.7% in the abdomen. DATA CONCLUSION: A multi-slice IR-radSSFP technique combined with a PC-based reconstruction was demonstrated for higher resolution T1 mapping. This technique is fast, motion-insensitive and yields repeatable T1 values comparable to those in literature. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:239-252.

ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization.

We present ggsashimi, a command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments, a feature unique to this software. Compared to the existing versions of programs generating sashimi plots, it uses popular bioinformatics file formats, it is annotation-independent, and allows the visualization of splicing events even for large genomic regions by scaling down the genomic segments between splice sites. ggsashimi is freely available at https://github.com/guigolab/ggsashimi. It is implemented in python, and internally generates R code for plotting.