Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN

Simple Summary Contrast-enhanced MR has been used in diagnosing and treating liver patients. Recently, development in MR-guided radiation therapy calls for daily contrast MR for tumor targeting. However, frequent contrast injection is risky to patients. We developed a deep learning model (GRMM-GAN) to synthesize contrast-enhanced MR from pre-contrast images. GRMM-GAN adopts gradient regularization and multi-discrimination mechanisms. It shows superior performance compared with state-of-the-art deep learning models. Abstract Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts’ contours evaluated the image synthesis quality. Results: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the model’s effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91. Conclusion: We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.
Design and theoretical investigation of a silicon-on-insulator based electro-optical logic gate device

Abstract. A compact electro-optical “NOR” logic gate device based on silicon-on-insulator (SOI) platform is proposed and investigated theoretically. By introducing a hook-type waveguide, the signal could be coupled between the bus and hook-type waveguide to form an optical circuit and realize NOR logic gate. We can easily realize the NOR logical function by the voltage applied on the coupling components. The numerical simulation shows that a high coupling efficiency of more than 99% is obtained at the wavelength of 1550 nm, and the footprint of our device is smaller than 90 μm2. In addition, the response time of the proposed NOR logic gate is 3 ns with a switching voltage of 1.8 V. Moreover, it is demonstrated that such NOR logic gate device could obtain an extinction ratio of 21.8 dB. Thus, it has great potential to achieve high speed response, low power consumption, and small footprint, which fulfill the demands of next-generation on-chip computer multiplex processors.
INVERSE DESIGN OF 2-D AIRFOIL VIA VIBRATIONAL GENETIC ALGORITHM

Within this study, it is aimed to provide an efficient algorithm for inverse design of 2-D airfoil in different flow conditions. For this purpose, as a stochastic search method, current vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for inverse design. From the results obtained, it is concluded that VGA decreased the required time for optimal airfoil solution beside its simplicity. Low population rate and short generation cycle are the main benefits of vibrational genetic algorithm.
Dynamic Delay-Dispersive UWB-Radar Targets: Modeling and Estimation

This publication proposes a parametric data model and a gradient-based maximum likelihood estimator suitable for the description of delay-dispersive responses of multiple dynamic ultrawideband (UWB)-radar targets. The target responses are estimated jointly with the global target parameters range and velocity. The large relative bandwidth of UWB has consequences for model-based parameter estimation. On the one hand, the Doppler effect leads to a dispersive response in the Doppler spectrum and to a coupling of the target parameters that both need to be considered during modeling and estimation. On the other hand, the shape of an extended target results in a dispersive response in range, which can be resolved by the radar resolution. We consider this extended response as a parameter of interest, e.g., for the purpose of target recognition. Hence, we propose an efficient description and estimation of it by a finite impulse response (FIR) structure only imposing a restriction on the target’s dispersiveness in range. We evaluate the approach on simulations, compare it to state-of-the-art solutions, and provide a validation of the FIR model on measurements of a static scenario.
EMPI: Enhanced Message Passing Interface in Modern C++

Message Passing Interface (MPI) is a well-known standard for programming distributed and HPC systems. While the community has been continuously improving MPI to address the requirements of next-generation architectures and applications, its interface has not substantially evolved. In fact, MPI only provides an interface to C and Fortran and does not support recent features of modern C++. Moreover, MPI programs are error-prone and subject to different syntactic and semantic errors. This paper introduces EMPI, an Enhanced Message Passing Interface based on modern C++, which is directly mapped to the OpenMPI implementation and exploits modern C++ for safe and efficient distributed programming. EMPI proposes novel C++RAII-based semantics and constant specialization to prevent error-prone code patterns such as parameter mismatch, and reduce the overhead of handling multiple objects and perinvocation time. Consequently, EMPI programs are safer: six out of nine well-known MPI error patterns do not occur while correctly using EMPI semantics. Experimental results on five microbenchmarks and two applications on a large-scale cluster using up to 1024 processes show that EMPI's performance is very similar to native MPI and considerably faster than the MPL C++ interface.
Neurite orientation dispersion and density imaging of white matter microstructure in sensory processing dysfunction with versus without comorbid ADHD

Introduction Sensory Processing Dysfunction (SPD) is common yet understudied, affecting up to one in six children with 40% experiencing co-occurring challenges with attention. The neural architecture of SPD with Attention Deficit and Hyperactivity Disorder (ADHD) (SPD+ADHD) versus SPD without ADHD (SPD-ADHD) has yet to be explored in diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) has yet to be examined. Methods The present study computed DTI and NODDI biophysical model parameter maps of one hundred children with SPD. Global, regional and voxel-level white matter tract measures were analyzed and compared between SPD+ADHD and SPD-ADHD groups. Results SPD+ADHD children had global WM Fractional Anisotropy (FA) and Neurite Density Index (NDI) that trended lower than SPD-ADHD children, primarily in boys only. Data-driven voxelwise and WM tract-based analysis revealed statistically significant decreases of NDI in boys with SPD+ADHD compared to those with SPD-ADHD, primarily in projection tracts of the internal capsule and commissural fibers of the splenium of the corpus callosum. Conclusion We conclude that WM microstructure is more delayed/disrupted in boys with SPD+ADHD compared to SPD-ADHD, with NODDI showing a larger effect than DTI. This may represent the combined WM pathology of SPD and ADHD, or it may result from a greater degree of SPD WM pathology causing the development of ADHD.
The Bolund experiment: Overview and background

Bolund experiment is a field campaign that provides a new dataset for validating numerical models of flow in complex terrain. It will be the basis for a unique blind comparison of flow models. The present work describes the background of the experiment and gives a detailed description of the measuring campaign. However, no experimental results will be presented! One aim of the project is to assess the accuracy of models for flow in complex terrain. This will be achieved through a blind comparison where model predictions are compared with measured results. Modellers will be challenged to predict the wind flow over the Bolund terrain based on the conditions of the free wind and the orography. The blind test will start at EWEC 2009.
Wave Absorptions at Critical Levels in Laterally Bounded Rotating Fluids

Behaviors of internal waves in a laterally bounded, uniformly or non-uniformly rotating fluids at Jones' critical level, where the Doppler-shifted wave frequency is equal to Coriolis frequency in magnitude are discussed. The solution is essentially singular at Jones' critical level, if the *-plane approximation is applied, denying the existence of such a non-zero regular solution at this level as suggested by Lindzen (1970). It is shown numerically that the wave is greatly attenuated and the zonal momentum flows into the mean zonal flow at this level. This opposes to a result by Lindzen (1970) that the attenuation of waves does not occur at this level for internal equatorial planetary-scale waves.
Influence of large-scale accelerating motions on turbulent pipe and channel flows

Direct numerical simulation data from turbulent pipe and channel flows at $\mathit{Re}_{\unicode[STIX]{x1D70F}}\approx 930$ are used to investigate their statistical difference by focusing on large-scale motions (LSMs). The contribution to the bulk production of turbulent kinetic energy shows marked differences in the overlap and core regions. These discrepancies arise from the dominant contributions of the LSMs ( $\unicode[STIX]{x1D706}_{x}>3\unicode[STIX]{x1D6FF}$ ) to the Reynolds shear stress in the channel flow. The spectrum of the net Reynolds shear force reveals that the LSMs accelerate the mean flow in the overlap region. The net force spectrum is further decomposed into the spectra of velocity–vorticity correlations, $\langle v\unicode[STIX]{x1D714}_{z}\rangle$ and $\langle -w\unicode[STIX]{x1D714}_{y}\rangle$ , which are related to the advective vorticity transport and the change-of-scale effect, respectively. The dominance of large-scale accelerating motions (LSAMs) in the overlap region of the channel flow is due to the contribution of $\langle -w\unicode[STIX]{x1D714}_{y}\rangle$ at longer wavelengths ( $\unicode[STIX]{x1D706}_{x}>3\unicode[STIX]{x1D6FF}$ ), The LSAMs are related to the long low-speed regions, and these regions are longer and wider in the channel flow than in the pipe flow. Due to the pipe curvature, the spanwise size of the LSMs is restricted by neighbouring LSMs and the spanwise velocity fluctuations are reduced. The contribution of $\langle -w\unicode[STIX]{x1D714}_{y}\rangle$ to the acceleration is prominent in the channel flow, leading to the dominance of the LSAMs associated with the change-of-scale effect.
Awareness of Polycystic Ovarian Syndrome among Jordanian Females

Many endocrine-related conditions have attracted the attention of medical societies worldwide. Many of them may have a serious impact on female re-productivity and overall health. Among them is the polycystic ovarian syndrome (PCOS). However, an early diagnosis is crucial in avoiding any potential complications. This study aimed at evaluating the awareness of PCOS among Jordanian females. The outcomes shall be implemented to improve public health through the various educational services, medical campaigns, and healthcare practitioners in Jordan and other societies. A cross-sectional study using a survey (questionnaires) was directed toward a sample of Jordanian females aged 18-40 years. The assessment of their knowledge and understanding included its signs, symptoms, and associated health risks. Thus, the collected data were subject to processing and statistical analysis. The majority of females (73.3%) were aware of the potentiality of PCOS, but the majority of the sample was not satisfactorily familiar with the signs and symptoms of the disease, and only 6% confirmed getting education directly from a doctor. The study outcome reveals that the current level of PCOS awareness among Jordanian females needs a comprehensive educational effort.
Principios y prácticas agroecológicas para la transición hacia una ganadería bovina sostenible. Revisión

El desarrollo del sector ganadero bovino es afectado por múltiples crisis como la ambiental, económica, alimentaria, energética, sanitaria y migratoria, que impactan en la alimentación y la salud de las personas. Estas crisis obligan a las y los productores a reflexionar y repensar sobre las prácticas ganaderas que aplican en el contexto sociocultural, ambiental, económico y político. La ganadería bovina sostenible, permite enfrentar estas crisis mediante la aplicación de prácticas agroecológicas. El objetivo de esta revisión es analizar los principios y prácticas agroecológicas que promueven una transición hacia la sostenibilidad del sector ganadero bovino, a través de un análisis bibliográfico se discutió el impacto de la crisis civilizatoria en la ganadería bovina, así como las perspectivas de la agroecología y sus alcances, esto con la finalidad de identificar sus contribuciones para alcanzar una producción de leche y carne accesible y de calidad para la sociedad. Se concluye que los principios y prácticas agroecológicas son universales y pueden ser aplicados a la ganadería bovina para transitar a sistemas de producción sustentable. Estos principios y prácticas pueden adaptarse a zonas climáticas y reducir el impacto que la crisis civilizatoria ha generado en la ganadería bovina. La aplicación de dichos principios y prácticas debe ser de acuerdo al nivel, cantidad y calidad de los recursos de cada dimensión. Las iniciativas analizadas muestran que los niveles de producción de carne y leche son llevados a cabo con el menor número de insumos posible, un bajo impacto al medio ambiente y con la formación de comunidades organizadas. Finalmente, su aplicación apropiada depende en mayor medida de la disposición, motivación y el empoderamiento de las y los productores.
Integrative Metabolic and Transcriptomic Profiling in Camellia oleifera and Camellia meiocarpa Uncover Potential Mechanisms That Govern Triacylglycerol Degradation during Seed Desiccation

Camellia seed oil is a top-end quality of cooking oil in China. The oil quality and quantity are formed during seed maturation and desiccation. So far, it remains largely unresolved whether lipid degradation occurs and contributes to Camellia oil traits. In this study, three different Camellia germplasms, C. oleifera cv. Min 43 (M43), C. meiocarpa var. Qingguo (QG), and C. meiocarpa cv Hongguo (HG) were selected, their seed oil contents and compositions were quantified across different stages of seed desiccation. We found that at the late stage of desiccation, M43 and QG lost a significant portion of seed oil, while such an event was not observed in HG. To explore the molecular bases for the oil loss In M43, the transcriptomic profiling of M43 and HG was performed at the early and the late seed desiccation, respectively, and differentially expressed genes (DEGs) from the lipid metabolic pathway were identified and analyzed. Our data demonstrated that different Camellia species have diverse mechanisms to regulate seed oil accumulation and degradation, and that triacylglycerol-to-terpenoid conversion could account for the oil loss in M43 during late seed desiccation.
ISAR Imaging Based on MEMP Method and Low Rank Matrix Denoising Technique

Matrix enhancement and matrix pencil (MEMP) method is an efficient approach for 2-D frequencies estimation in ISAR imaging. However, the MEMP method belongs to the category of spectral estimation algorithm and is sensitive to noise. In the MEMP method, an enhanced matrix formed using the echo matrix is leveraged to extract the 2-D frequencies. Based on the low rank property of this enhanced matrix, we propose a low rank matrix denoising technique for MEMP method. Simulation and quasi real data results demonstrate the effectiveness and robustness of the proposed denoising approach. With the proposed denoising technique, the tolerance to noise of MEMP method has been improved by 10dB.
Low and Mid-level Shape Priors for Image Segmentation

Perceptual grouping is essential to manage the complexity of real world scenes. We explore bottom-up grouping at three different levels. Starting from low-level grouping, we propose a novel method for oversegmenting an image into compact superpixels, reducing the complexity of many high-level tasks. Unlike most low-level segmentation techniques, our geometric flow formulation enables us to impose additional compactness constraints, resulting in a fast method with minimal undersegmentation. Our subsequent work utilizes compact superpixels to detect two important mid-level shape regularities, closure and symmetry. Unlike the majority of closure detection approaches, we transform the closure detection problem into one of finding a subset of superpixels whose collective boundary has strong edge support in the image. Building on superpixels, we define a closure cost which is a ratio of a novel learned boundary gap measure to area, and show how it can be globally minimized to recover a small set of promising shape hypotheses. In our final contribution, motivated by the success of shape skeletons, we recover and group symmetric parts without assuming prior figure-ground segmentation. Further exploiting superpixel compactness, superpixels are this time used as an approximation to deformable maximal discs that comprise a medial axis. A learned measure of affinity between neighboring superpixels and between symmetric parts enables the purely bottom-up recovery of a skeleton-like structure, facilitating indexing and generic object recognition in complex real images.
Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features

Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF) instead of PCA to reduce the dimensionality of the hyperspectral data cube to be classified. We keep all the rest steps from the PCA-based EPFs for HSI classification. Since MNF can preserve fine features of a HSI data cube better than PCA, our new method can outperform PCA-EPFs for HSI classification significantly. Experimental results show that our new method performs better than the PCA-based EPFs under such noisy environment as Gaussian white noise and shot noise. In addition, our MNF+EPFs outperform the PCA+EPFs even when no noise is added to the HSI data cubes for most testing cases, which is very desirable in remote sensing.
Rare forms of cerebral amyloid angiopathy: pathogenesis, biological and clinical features of CAA-ri and iCAA

Thanks to a more widespread knowledge of the disease, and improved diagnostic techniques, the clinical spectrum of cerebral amyloid angiopathy (CAA) is now broad. Sporadic CAA, hereditary CAA, CAA-related inflammation (CAA-ri) and iatrogenic CAA (iCAA) create a clinical and radiological continuum which is intriguing and only partially discovered. Despite being relatively rare, CAA-ri, an aggressive subtype of CAA with vascular inflammation, has gained growing attention also because of the therapeutic efficacy of anti-inflammatory and immunomodulating drugs. More recently, diagnostic criteria have been proposed for an unusual variant of CAA, probably related to an iatrogenic origin (iCAA), toward which there is mounting scientific interest. These atypical forms of CAA are still poorly known, and their recognition can be challenging and deserve to be pursued in specialized referral centres. The aim of this brief review is to focus current developments in the field of rare forms of CAA, its pathogenesis as well as clinical and biological features in order to increase awareness of these rare forms.
Predicting HIV resistance with the 3D neighborhood kernel

Recently, we developed the 3D Neighborhood Kernel (3DNK), which acts on 3D structures of small molecules and proteins. We showed its state-of-the-art performance on several biological datasets. However, 3D data are in many cases dicult to obtain. For this reason, we adopt a dierent strategy: instead of requiring actual 3D structures, we use as input protein sequences, of which we approximate the 3D structure through homology modelling. Then, we apply 3DNK on the approximated 3D protein structures and show that, on the task of predicting HIV resistance, we obtain better results than when using a kernel function based on the protein sequences alone.
Test of Randomness of Residuals for Modified Gompertz Model used for Modelling the Growth of Callus Cultures from Glycine wightii (Wight & Arn.) Verdc.

One of the most important preliminary investigations of callus attributes is the growth characteristics. Most often than not, callus growth curve is sigmoidal in characteristics. Frequently, plant scientists studying callus growth neglect the utilization of mathematical growth that are useful in obtaining important growth constants such as lag period, maximum specific growth rate and maximum growth or asymptote. Formerly, we model callus growth of Glycine wightii from published literature to obtain vital growth constants. We discovered that themodified Gompertz model via nonlinear regression utilizing the least square method was the best to explain the growth curve. Nevertheless, an important thing to consider, that has not been stated more than enough, is the residual of the model needs to be random. To make sure that randomness being fulfilled we carry out the Wald-Wolfowitz runs test. The results demonstrated that the number of runs was 5, and the expected number of runs within the assumption of randomness was 5, suggesting the series of residuals had perfect runs. The p-value obtained was higher than 0.05, hence the null hypothesis is not rejected suggesting no persuading proof of nonrandomnessof the residuals plus they do stand for noise.