Full Data Research Articles

The second data release from the European Pulsar Timing Array

We present the results of the search for an isotropic stochastic gravitational wave background (GWB) at nanohertz frequencies using the second data release of the European Pulsar Timing Array (EPTA) for 25 millisecond pulsars and a combination with the first data release of the Indian Pulsar Timing Array (InPTA). A robust GWB detection is conditioned upon resolving the Hellings-Downs angular pattern in the pairwise cross-correlation of the pulsar timing residuals. Additionally, the GWB is expected to yield the same (common) spectrum of temporal correlations across pulsars, which is used as a null hypothesis in the GWB search. Such a common-spectrum process has already been observed in pulsar timing data. We analysed (i) the full 24.7-year EPTA data set, (ii) its 10.3-year subset based on modern observing systems, (iii) the combination of the full data set with the first data release of the InPTA for ten commonly timed millisecond pulsars, and (iv) the combination of the 10.3-year subset with the InPTA data. These combinations allowed us to probe the contributions of instrumental noise and interstellar propagation effects. With the full data set, we find marginal evidence for a GWB, with a Bayes factor of four and a false alarm probability of 4%. With the 10.3-year subset, we report evidence for a GWB, with a Bayes factor of 60 and a false alarm probability of about 0.1% (≳3σ significance). The addition of the InPTA data yields results that are broadly consistent with the EPTA-only data sets, with the benefit of better noise modelling. Analyses were performed with different data processing pipelines to test the consistency of the results from independent software packages. The latest EPTA data from new generation observing systems show non-negligible evidence for the GWB. At the same time, the inferred spectrum is rather uncertain and in mild tension with the common signal measured in the full data set. However, if the spectral index is fixed at 13/3, the two data sets give a similar amplitude of (2.5 ± 0.7) × 10−15 at a reference frequency of 1 yr−1. Further investigation of these issues is required for reliable astrophysical interpretations of this signal. By continuing our detection efforts as part of the International Pulsar Timing Array (IPTA), we expect to be able to improve the measurement of spatial correlations and better characterise this signal in the coming years.

Interpretable Deep Learning Identified the Significance of 1 Gy Volume on Lymphopenia after Radiotherapy in Breast Cancer

Lymphopenia is common after radiotherapy (RT) and is known for its significance on poor survival outcomes in patients with breast cancer. Previous work has demonstrated the significance of point dosimetric factors like the volume receiving 5 Gy. Considering the full dosimetric data together, this study aimed to develop and validate predictive models for lymphopenia after RT in breast cancer. Patients with breast cancer treated with radiation therapy in adjuvant setting and with complete dosimetric data were eligible. Combining dose-volume histogram (DVH) dosimetric and clinical factors, dense neural network (DNN) models were developed to predict both the reduction in lymphocyte counts and the graded lymphopenia in breast cancer patients after adjuvant RT. A Shapley additive explanation was applied to explain each feature's directional contributions. The generalization of DNN models was validated in both internal and independent external validation cohorts. P<0.05 was considered to be significant. A total of 928 consecutive patients with invasive breast cancer were eligible for this study. Treatment volumes of nearly all irradiation dose levels of DVH were significant predictors for lymphopenia after RT, including volumes at very low-dose 1 Gy (V1) of all structures considered including the lung, heart and body. DNN models using full DVH dosimetric and clinical factors were built and a simplified model was further established and validated in both internal and external validation cohorts. This simplified DNN AI model, combining full DVH dosimetric parameters of all OARs and five key clinical factors including baseline lymphocyte counts, tumor stage, RT technique, RT fields and RT fractionation, showed a predictive accuracy of 77% and above. This study demonstrated and externally validated the significance of an AI model of combining clinical and full dosimetric data, especially the volume of low dose at as low as 1 Gy of all critical structures on lymphopenia after RT in patients with breast cancer. The significance of V1 deserves special attention, as modern arc RT technology often has relatively high value of this parameter. Further study warranted for breast cancer RT plan optimization.

End-to-End Verification and Dosimetric Comparative Study of Brainlab Monte Carlo and Eclipse Acuros XB Photon Dose Calculation Algorithm for VMAT SRS Planning

We commissioned the Brainlab Monte Carlo Dose Calculation Algorithm for VMAT SRS planning and verified it by measurements and comparisons with two other algorithms: Pencil beam and Eclipse Acuros. The end-to-end verification and dosimetric comparative study results are reported in this abstract. Brainlab Elements Cranial SRS (v3.0) provides single lesion planning using VMAT optimization, thus allowing dose modulation with MLC leaf positioning, dose rate, and gantry speed. We commissioned and performed quality assurance for the available dose calculation algorithms, including Pencil Beam (PB) algorithm with only reference beam data (RBM PB) and PB with full beam data (Elements PB), and Monte Carlo (MC) algorithm XVMC. Calculation between the two were compared to each other and to Acuros XB dose calculation algorithm by Varian. Open field MLC, static cone, dynamic MLC, and conical cone plans were calculated with each algorithm. IBA CC01 ionization chamber was used for each measurement, and small field correction factors as determined in TRT-483 were applied. End-to-end tests were performed using an anthropomorphic stereotactic end-to-end verification (STEEV) phantom from CIRS and a phantom/diode array, StereoPHAN/SRSMapCHECK from SunNuclear. Each phantom was CT scanned with 0.5 mm slice thickness. Four clinical plans were optimized in Brainlab Elements Treatment Planning System (TPS). Each plan was calculated with the following algorithms: Elements PB, RBM PB, MC, and Acuros XB with 1 mm dose grid size. The delivered dose was measured with a SunNuclear SRSMapCHECK diode array detector. The calculated dose was compared with the measured dose by Gamma analysis with DTA 1%/1mm, 2%/1mm, and 3%/1mm. The results of point dose measurements for field size larger than 1 × 1 cm2 among measured dose, dose calculated by pencil beam algorithms, and dose calculated by Monte Carlo algorithm were in excellent agreement (1.45% ± 0.7%). For the small rectangular field sizes (0.4 × 1, 0.2 × 1 cm2), Monte Carlo calculation showed superior accuracy and robustness to pencil beam algorithm when compared with measured dose, with percent dose difference 13% and -2.2%, respectively. Monte Carlo calculation have higher gamma results compared with pencil beam algorithm due more accurate calculations with heterogenous materials. Acuros XB showed comparable accuracy with MC in terms of gamma passing rate. The SRS MapCHECK measurement agrees dosimetrically with the plans calculated by Pencil Beam algorithm and the Monte Carlo algorithm XVMC by Brainlab, as well as the Acuros XB dose calculation algorithm by Varian. Point dose calculation for small field dosimetry and gamma analysis using SRS MapCHECK both showed that Monte Carlo algorithm is more robust and is superior in regions of heterogenous materials. Therefore, we recommend that Monte Carlo algorithm for dose calculation be used in clinical SRS and SRT cases.

Investigating the amplitude and rotation of the phase spiral in the Milky Way outer disc

Context. With data releases from the astrometric space mission Gaia, exploration of the structure of the Milky Way is now possible in unprecedented detail, and has unveiled many previously unknown structures in the Galactic disc and halo. One such feature is the Gaia phase spiral where the stars in the Galactic disc form a spiral density pattern in the Z − VZ plane. Many questions regarding the phase spiral remain, particularly how its amplitude and rotation change with position in the Galaxy. Aims. We aim to characterize the shape, rotation, amplitude, and metallicity of the phase spiral in the outer disc of the Milky Way. This will allow us to better understand which physical processes caused the phase spiral and may provide further clues as to the Milky Way’s past and the events that contributed to its current state. Methods. We use Gaia data release 3 (DR3) to get full position and velocity data on approximately 31.5 million stars, and metallicity for a subset of them. We then compute the angular momenta of the stars and develop a model to characterise the phase spiral in terms of amplitude and rotation at different locations in the disc. Results. We find that the rotation angle of the phase spiral changes with Galactic azimuth and galactocentric radius, making the phase spiral appear to rotate about 3° per degree in Galactic azimuth. Furthermore, we find that the phase spiral in the 2200 − 2400 kpc km s−1 range of angular momentum is particularly strong compared to the phase spiral that can be observed in the solar neighbourhood. The metallicity of the phase spiral appears to match that of the field stars of the Milky Way disc. Conclusions. We created a new model capable of fitting several key parameters of the Gaia phase spiral. We have been able to determine the rotation rate of the phase spiral to be about 3° per degree in Galactic azimuth. We find a maximum in the amplitude of the phase spiral at LZ ≈ 2300 km kpc s−1, which makes the phase spiral clearly visible. These results provide insights into the physical processes that led to the formation of the phase spiral and contribute to our understanding of the Milky Way’s past and present state.

Environmental scan of current strategies to decrease sedative-hypnotic drug use and promote sleep in hospital patients.

Sedative-hypnotic drugs are often initiated in hospital to manage insomnia and anxiety. Guidelines discourage their use, particularly in older adults, due to risks of falls, fractures, and delirium. To identify publicly available resources to decrease the use of sedative-hypnotic drugs and promote sleep in hospital. An advanced Google search with 6 search strategies was conducted. Key websites were also identified and searched. Hospital- or community-based resources using non-pharmacologic measures to reduce sedative-hypnotic drug use and/or to promote sleep were included if they were publicly available in English within the past 5years. Full text screening and data extraction was performed independently by 2 reviewers; a third reviewer resolved disagreements by consensus. A total of 79 resources met inclusion criteria, with 65 (82.3%) providing education and 31 (39.2%) describing sleep hygiene strategies. Other resources included deprescribing (17, 21.5%), relaxation training (13, 16.5%), cognitive behavioural therapy for insomnia (9, 11.4%), and policies (7, 8.9%). The resources primarily targeted patients (59, 74.7%) followed by healthcare providers (9, 11.4%). There were 9 resources (11.4%) that applied to both community and hospital settings, and another 2 (2.5%) designed specifically for hospital. Many resources were available to patients and healthcare providers to reduce inappropriate or ineffective use of sedative-hypnotic drugs and promote better sleep. Specific resources for the hospital setting were infrequent and recommended that clinicians stop hospital-initiated sedatives when patients are discharged. Identified resources can be adapted by healthcare organizations to develop sedative-hypnotic prescribing programs and policies.

Donor-acceptor pair transitions in MgAl2O4 spinel

The luminescence of the well-known spinel-structured functional compound MgAl2O4 was studied at cryogenic temperatures below 10 K in a wide spectral range from near-IR to VUV under synchrotron radiation and electron beam excitation. The most intense luminescence band at ∼5 eV, which origin remained largely unknown up to now, showed characteristic behaviour of the donor-acceptor pair (DAP) centres and was assigned to the so-called antisite defects. The antisite DAPs are formed by position-swapped cations AlMg● and MgAl′, which define the inversion degree of the spinel structure and, to the best of our knowledge, always exceed 10% (>1.5 × 1021 cm−3) in the synthetic solids. Full data set allowed confirming that extremely high concentration of these intrinsic defects is the main reason for the strong 5-eV luminescence, which mechanism includes the electron transfer from MgAl′ to AlMg● (excitation) following by that from VO● to MgAl× (emission). Irradiation with He + ions up to fluences of 1017 cm−2 randomly displaced cations to empty sites of the spinel lattice resulting in more intragap defect levels, which induce a non-radiative energy relaxation channel quenching closely spaced while unaffecting distant DAP luminescence.

Theranostic Nanoparticle Uptake in Patient Brain Tumors as Quantified by MP2RAGE T1 Mapping

Modern radiation therapy techniques provide effective treatments for solid tumors, but there remain challenges with delivering high doses to elusive tumors without causing toxicity to surrounding normal tissue. Pre-clinical trials have demonstrated the theranostic properties of a recently developed gadolinium-based nanoparticle (Gd-NP). The first in-human clinical trial was conducted to assess the safety and dose tolerance of intravenous Gd-NPs in combination with whole brain radiotherapy and showed favorable results, including a significant correlation between tumor uptake and therapeutic response. The second, double-blinded, in-human clinical trial is underway in the US and aims to evaluate if brain-directed stereotactic radiation in conjunction with NPs will improve local tumor control compared to radiation alone. The current work investigates uptake patterns in brain tumors of 23 patients as quantified by magnetization prepared 2 rapid gradient echo (MP2RAGE) T1 mapping. A phantom containing eight vials of NP-saline solutions at varying concentrations was created to examine the relationship between NP concentration and longitudinal relaxation (T1, in seconds). This relationship is known as relaxivity and is dependent on the contrast agent, field strength and T1 mapping sequence. A 3T MAGNETOM Vida scanner and MP2RAGE sequence were used to image the phantom and MP2RAGE T1 maps were calculated using Bloch equations (QMRLab software). Relaxivity was determined and applied to 23 patient T1 maps (pre- and post- Gd-NP administration) to calculate uptake on an individual tumor basis. Theranostic NP uptake was calculated for every voxel in each of 129 brain metastases and examined for patterns in quantity and distribution. Average individual tumor uptake ranged from 0.02-0.12 mg/ml, where average overall uptake was equal to 0.05 mg/ml. A relationship between tumor diameter and mean NP concentration was observed and best represented by a power-based curve (R2 = 0.92). In contrast, patients with suspected placebo administration appeared to have no uptake and therefore no relationship with tumor diameter. The distribution of NP concentration within the tumor was also examined; on average, linear uptake profiles through tumor centroids (ant-post, left-right) demonstrated roughly gaussian patterns of uptake with lower concentrations at the tumor edges and higher concentrations at the tumor center. This pattern indicates robust tumor penetration and may have implications for amplifying radiation dose to hypoxic tumors. Gd-NP uptake in brain metastases can be quantified using MP2RAGE T1 mapping. Uptake was determined for each voxel in each tumor volume, where a gaussian pattern of spatial concentrations was observed. This analysis procedure will be applied to the full data set, when available, to evaluate the impact of NP uptake (in conjunction with radiation therapy) within individual patients and individual tumors.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

Multi-echo-based fat artifact correction for CEST MRI at 7 T.

CEST MRI is influenced by fat signal, which can reduce the apparent CEST contrast or lead to pseudo-CEST effects. Our goal was to develop a fat artifact correction based on multi-echo fat-water separation that functions stably for 7 T knee MRI data. Our proposed algorithm utilizes the full complex data and a phase demodulation with an off-resonance map estimation based on the Z-spectra prior to fat-water separation to achieve stable fat artifact correction. Our method was validated and compared to multi-echo-based methods originally proposed for 3 T by Bloch-McConnell simulations and phantom measurements. Moreover, the method was applied to in vivo 7 T knee MRI examinations and compared to Gaussian fat saturation and a published single-echo Z-spectrum-based fat artifact correction method. Phase demodulation prior to fat-water separation reduced the occurrence of fat-water swaps. Utilizing the complex signal data led to more stable correction results than working with magnitude data, as was proposed for 3 T. Our approach reduced pseudo-nuclear Overhauser effects compared to the other correction methods. Thus, the mean asymmetry contrast at 3.5 ppm in cartilage over five volunteers increased from -9.2% (uncorrected) and -10.6% (Z-spectrum-based) to -1.5%. Results showed higher spatial stability than with the fat saturation pulse. Our work demonstrates the feasibility of multi-echo-based fat-water separation with an adaptive fat model for fat artifact correction for CEST MRI at 7 T. Our approach provided better fat artifact correction throughout the entire spectrum and image than the fat saturation pulse or Z-spectrum-based correction method for both phantom and knee imaging results.

Investigation of Fusarium damage in wheat using hyperspectral imaging: An independent component analysis approach

With the continuously growing world population in the 21st century, the agri-food industry is in dire need of adopting rapid, eco-friendly, and reliable technologies to improve the quantity, quality, and safety of agri-food products to fulfill the world's future food needs. Hyperspectral imaging (HSI), a technique to glean a sample's spectral and spatial information, is an emerging non-destructive technique that can characterize the quality parameters of agri-food products such as Fusarium damage. Despite its vast potential, HSI systems suffer from enormous data sizes, requiring high computational time and power. One potential solution to overcome the aforementioned challenge is to reduce the data size by removing redundant information. However, detecting small optimum features from a large dataset is not trivial. To this end, an exploratory novel HSI data reduction and analysis technique was investigated and validated to identify Fusarium damage in wheat kernels. Wheat samples at three moisture contents (19, 27, and 35%, wet basis) and seven infection levels (ranging from 0 to 56 days after infection) were imaged at 256 equally spaced wavelengths from 820 to 1666 nm. Firstly, complete HSI data was utilized to successfully characterize sound and Fusarium-damaged wheat kernels using independent component analysis (ICA) algorithm. Then, a genetic algorithm optimization approach was used to reduce the data to ten wavelengths for ICA-based analysis. This data reduction approach reduced the computation time to approximately 1.31% of the original time taken for analyzing the full HSI data without compromising the performance of the system. This preliminary study suggests that such wavelength tailoring could reduce the complexity and price of the imaging hardware, e.g., the use of inexpensive non-tunable filters, and less expensive computational hardware, thereby enabling fast and affordable real-time exploration and sorting of grains. This study, while exploratory, fosters advancements in HSI data processing and identifies certain limitations that open new avenues for future research.

Circadian rhythm disorders in patients with advanced cancer: a scoping review.

Circadian rhythms can be demonstrated in several biomarkers and behavioural activities, with rhythmical patterns occurring roughly over a 24-h period. Circadian disorders occur in patients with cancer and may be associated with poor clinical outcomes. This scoping review aimed to identify circadian rhythm research and reporting practices, circadian rhythm patterns, circadian rhythm disorders, and relevant associations of circadian rhythm disorders in patients with advanced cancer. Studies involved adult patients with locally advanced or metastatic cancer and used objective measures of circadian rhythmicity. Two independent authors completed initial screening of title and abstracts, full text reviews, data extraction, and data checking. A total of 98 articles were highlighted in the scoping review, which utilised physical activity measures (actigraphy and polysomnography), biomarkers (cortisol and melatonin), or a combination. Several circadian rhythms are commonly disordered amongst patients with advanced cancer and have significant implications for symptom burden, quality of life, and survival. It remains unclear which patients are most at risk of a circadian rhythm disorder. Significant heterogeneity exists in research and reporting practices. Standardising this approach may address discrepancies in the current literature and allow for research to focus on the most relevant parameters and approaches to improving circadian rhythmicity.

Determination of an efficient winglet for recessed tip and its loss map in aeroengine turbine cascade

In this investigation, (i) the losses for seven streamlined winglets mounted on the recessed tip are measured with the five-hole probe in a strong-reaction turbine cascade when h/s (tip clearance-to-span ratio) = 1.0 % and w/p (winglet width-to-pitch ratio) = 10.0 %, (ii) the best winglet coverage is selected based on the loss data, and then (iii) its loss map in wide ranges of h/s and w/p is obtained for the recessed tip. For the recessed tip, SW (full suction-side winglet) is found to be the best winglet, and the loss reduction capability of the recessed tip deteriorates in the presence of pressure-side winglet coverage. The reduction of loss by the recessed tip with SW is almost identical to the sum of that by the recessed tip without winglet and that by the flat tip with SW. The full loss data for the recessed tip with SW reveal that wider winglet leads to lower loss, regardless of h/s, but the loss generated by SW of w/p = 5.0 % is not so different from that by SW of w/p = 10.0 %. This tendency becomes more noticeable for higher h/s.

Transvenous Embolization of Dural Arteriovenous Fistulas Through the Galenic (Deep Venous) System: Multicenter Case Series and Meta-Analysis.

Arteriovenous fistulas involving the deep venous system have often been treated with microsurgery or transarterial embolization. Increasing familiarity with transvenous navigation and improved endovascular access systems may facilitate transvenous embolization (TVE) for these rare and challenging lesions. We performed a retrospective study of neurointerventional databases of 6 high-volume centers. We identified all cases of arteriovenous fistulas with deep transvenous embolizations for arteriovenous fistula. Details regarding demographics, fistula characteristics, treatment considerations, clinical outcomes, and fistula occlusion were obtained and analyzed. The meta-analysis used the same inclusion criteria. Seventeen cases of TVE were identified. The most common reasons for TVE included prior treatment failure with microsurgery (n = 2) or transarterial embolization (n = 3) or inaccessible arterial pedicles (n = 4). For patients with full clinical outcome data (n = 14), 2 patients had worsened modified Rankin Scale, 8 patients had no change, and 4 were improved at a median clinical follow-up of 3.5 months. Angiographic obliteration was achieved in 15/17 cases (88.2%). In 1 case, catheterization around a sharp turn in the basal vein of Rosenthal could not be performed. In another case, despite successful TVE, there was residual lesion which was treated 1 year later by microsurgical clipping and excision. Transvenous approaches for embolization of deep arteriovenous fistulas have become possible with modern endovascular catheter systems and liquid embolics. These lesions can be treated safely and effectively through endovascular approaches, which may spare patients the traversal of deep structures needed for microsurgical approaches to these regions. The outcomes of TVE are comparable with published outcomes of microsurgical interruption.

Extended decomposition ensemble framework based on full data analysis and optimized combination with relaxed boundary for carbon price forecasting

Extended decomposition ensemble framework based on full data analysis and optimized combination with relaxed boundary for carbon price forecasting

Full wavefield inversion with multiples: Nonlinear Bayesian inverse multiple scattering theory beyond the Born approximation

Imaging earth’s structures is fundamental to the earth’s interior, yet how to reconstruct earth’s heterogeneities using full-waveform inversion of full wavefield data that includes primary reflections and multiples remains enigmatic. I develop a nonlinear Bayesian approach for full-waveform inversion method with multiple scattering. Instead of using single scattering Born approximation to formulate the sensitivity kernels, I develop multiple scattering sensitivity kernels using multiple scattering-based Green’s functions. It is based on the Lippmann-Schwinger integral and Marchenko methods, for which the Green’s functions are retrieved from reflection data by solving a Marchenko equation. To estimate the uncertainty of velocities, I apply a Bayesian framework to the inverse problem. Our results indicate that the method does not depend on the earth’s scattering potential and the full-waveform inversion method with multiple scattering is a good alternative approach to image the earth’s structures.

The lithosphere–asthenosphere boundary structure at 11–21 Ma from wide-angle seismic data in the equatorial Atlantic Ocean

SUMMARYThe lithosphere–asthenosphere boundary (LAB) separates the rigid lithospheric plate above with the ductile and convective asthenosphere below and plays a fundamental role in plate tectonic processes. The LAB has been imaged using passive geophysical methods, but these methods only provide low-resolution images. Recently, seismic reflection imaging method has provided high-resolution images of the LAB, but imaging of the LAB at younger ages has been difficult. Here, we present the image of the LAB using wide-angle seismic reflection data covering 11–21 Ma old lithosphere in the equatorial Atlantic Ocean. Using ocean bottom seismometers (OBSs), we have observed wide-angle reflections between 150 and 400 km offsets along with crustal and mantle refraction arrivals. We first performed traveltime tomography to obtain the velocity in the crust and upper mantle. The Pn arrivals provide the information about P-wave velocity down to 4 km below the Moho. The disappearance of Pn arrivals beyond 130 km offset suggests that vertical P-wave velocity gradient is negligible or negative below this depth. We extended these velocities down to 90 km depth and then applied two imaging techniques to wide-angle reflection data, namely traveltime mapping of picked reflection arrivals and pre-stack depth migration of full wavefield data. We find that these reflections originate between 34 and 67 km depth, possibly from the LAB system. We have carried out extensive modelling to show that these reflections are real and not artefacts of imaging. Comparison of our results with coincident passive seismological and magnetotelluric results suggests that wide-angle imaging technique can be successfully used to study the lithosphere and the LAB system. We find that the LAB gradually deepens with age, but becomes very deep at 17–19 Ma, which we interpret to be due to the anomalous geology along this part of the profile.

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning.

Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics. The purpose of this study was to explore the potential application value of pathomics signatures. Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links. The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile+ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.

New atomic data for C i Rydberg states compared with solar UV spectra

ABSTRACT We use the Breit–Pauli R-matrix method to calculate accurate energies and radiative data for states in C i up to n = 30 and with l ≤ 3. We provide the full data set of decays to the five 2s2 2p2 ground configuration states 3P0,1,2, 1D2, and 1S0. This is the first complete set of data for transitions from n ≥ 5. We compare oscillator strengths and transition probabilities with the few previously calculated values for such transitions, finding generally good agreement (within 10 per cent) with the exception of values recently recommended by National Institute of Standards and Technology, where significant discrepancies are found. We then calculate spectral line intensities originating from the Rydberg states using typical chromospheric conditions and assuming local thermal equilibrium, and compare them with well-calibrated Solar and Heliospheric Observatory Solar Ultraviolet Measurements of Emitted Radiation ultraviolet (UV) spectra of the quiet Sun. The relative intensities of the Rydberg series are in excellent agreement with observation, which provides firm evidence for the identifications and blends of nearly 200 UV lines. Such comparison also resulted in a large number of new identifications of C i lines in the spectra. We also estimate optical depth effects and find that these can account for much of the absorption noted in the observations. The atomic data can be applied to model a wide range of solar and astrophysical observations.

Conditional Effect of Environmental Degradation and Institutional Environment on Human Development in Developing Countries: Evidence from Method of the Moment-Quantile Regression with Fixed Effect

The study examines the conditional effect of environmental degradation and institutional environment on the conditional distribution of human development, using the Method of the Moment Quantile Regression (MM-QR) technique with fixed effect, Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) estimators. The study uses strongly balanced data from 20 developing countries with the full data from 1996 to 2021. The findings confirmed heterogenous effects of environmental degradation and institutional environment on human development. The results reveals that lower environmental degradation promote human development across all levels of human development, but the effect higher in countries with low human development. The study shows that institutional environment has positive effect across all levels of human development, with higher effect in countries with low human development. The findings for the control variables show that financial development, population growth and FDI promote human development across all levels of human development. However, the strength of the coefficients also increases with higher quantiles. The results also confirmed unidirectional causality running from environmental degradation and institutional environment to human development, while bidirectional causality between financial development and human development. To promote human development, policymakers at developing countries should focus on climate action policies and building strong institutions.

Fast high-resolution electric properties tomography using three-dimensional quantitative transient-state imaging-based water fraction estimation.

In this study, we aimed to develop a fast and robust high-resolution technique for clinically feasible electrical properties tomography based on water content maps (wEPT) using Quantitative Transient-state Imaging (QTI), a multiparametric transient state-based method that is similar to MR fingerprinting. Compared with the original wEPT implementation based on standard spin-echo acquisition, QTI provides robust electrical properties quantification towards B1 + inhomogeneities and full quantitative relaxometry data. To validate the proposed approach, 3D QTI data of 12 healthy volunteers were acquired on a 1.5 T scanner. QTI-provided T1 maps were used to compute water content maps of the tissues using an empirical relationship based on literature ex-vivo measurements. Assuming that electrical properties are modulated mainly by tissue water content, the water content maps were used to derive electrical conductivity and relative permittivity maps. The proposed technique was compared with a conventional phase-only Helmholtz EPT (HH-EPT) acquisition both within whole white matter, gray matter, and cerebrospinal fluid masks, and within different white and gray matter subregions. In addition, QTI-based wEPT was retrospectively applied to four multiple sclerosis adolescent and adult patients, compared with conventional contrast-weighted imaging in terms of lesion delineation, and quantitatively assessed by measuring the variation of electrical properties in lesions. Resultsobtained with the proposed approach agreed well with theoretical predictions and previous in vivo findings in both white and gray matter. The reconstructed maps showed greater anatomical detail and lower variability compared with standard phase-only HH-EPT. The technique can potentially improve delineation of pathology when compared with conventional contrast-weighted imaging and was able to detect significant variations in lesions with respect to normal-appearing tissues. In conclusion, QTI can reliably measure conductivity and relative permittivity of brain tissues within a short scan time, opening the way to the study of electric properties in clinical settings.