Miracle-dz/UniFAR-FTUs huggingface dataset:
Adhesion to the Brain Endothelium Selects Breast Cancer Cells with Brain Metastasis Potential

Tumor cells metastasize from a primary lesion to distant organs mainly through hematogenous dissemination, in which tumor cell re-adhesion to the endothelium is essential before extravasating into the target site. We thus hypothesize that tumor cells with the ability to adhere to the endothelium of a specific organ exhibit enhanced metastatic tropism to this target organ. This study tested this hypothesis and developed an in vitro model to mimic the adhesion between tumor cells and brain endothelium under fluid shear stress, which selected a subpopulation of tumor cells with enhanced adhesion strength. The selected cells up-regulated the genes related to brain metastasis and exhibited an enhanced ability to transmigrate through the blood–brain barrier. In the soft microenvironments that mimicked brain tissue, these cells had elevated adhesion and survival ability. Further, tumor cells selected by brain endothelium adhesion expressed higher levels of MUC1, VCAM1, and VLA-4, which were relevant to breast cancer brain metastasis. In summary, this study provides the first piece of evidence to support that the adhesion of circulating tumor cells to the brain endothelium selects the cells with enhanced brain metastasis potential.
Study of high efficiency semitransparent polymer solar cells with stable color render index

As we known, the color render index(CRI) and the power conversion efficiency (PCE) are two critical parameters of semitransparent polymer solar cells (PSCs), which are contradictory. The CRI are strongly depended on the absorption of the polymer: fullerene active layer. The PCE is not only rely on the absorption but also on the bulk-heterojunction structure. Here, the 1,8-diiodooctane (DIO) additive were added into the PCDTBT: PC71BM blend PSCs to improve the PCE with almost unchanged CRI. Based on the higher boiling point than host solvent ODB and better solubility of PC71BM, the device photovoltaic properties with DIO additive were changed obviously. The PCE improvement is attribute to the charge recombination and transportation process which means the utilization of the absorbed photon is improved. But this improvement cannot affect the transmitted light. Hence, the CRI of transmitted light is nearly invariable. With 3% v/v DIO, the Jsc, Voc and FF are both increased. Correspondingly, a highest PCE is achieved of 6.15%, while the reference device without DIO only has a PCE of 5.23%. At the same time, the CRI is slightly changed from 82 to 81.
ON SEQUENCES OF PAIRS OF DEPENDENT RANDOM VARIABLES

The generalized random variables $( {x,y} )$ have a given joint distribution. Pairs $( {x_i ,y_i } )$ are drawn independently. The observer of $( {x_1 , \cdots ,x_n } )$ and the observer of $( {y_1 , \cdots ,y_n } )$ each make a binary decision, of entropy bounded away from zero, with probability of disagreement $\varepsilon _n $. It is shown that $\varepsilon _n $ can be made to approach zero as $n \to \infty $ if and only if the maximum correlation of x and y is unity. Under a compactness condition, satisfied in particular when x and/or y takes only finitely many values, this occurs if and only if the joint distribution decomposes, that is when $\varepsilon _1 $ can be made to vanish by nontrivial decisions, as had been conjectured.Results are also obtained for nonidentically distributed pairs, for randomized decisions, for multivalued decisions and for decisions based on the infinite sequences.The question arose in the transmission of data from two dependent sources to two receivers. The results of Gac.
Electromagnetic wave propagation in a laterally nonuniform troposphere

Electromagnetic wave propagation in a nonuniform troposphere is studied for a cylindrical model of the earth. The refractive index of the troposphere is assumed to vary both with height and range. The formulation is facilitated by the fact that the field within a piecewise (laterally) uniform section can be represented by a discrete sum of modes that are orthogonal in the transverse plane. Using these radial wave functions, mode conversion at a junction between two laterally uniform structures is analyzed for both TE and TM waves.
Radiation Doses in Cardiovascular Computed Tomography

We discussed the contemporary views on the effects of ionising radiation on living organisms and the process of estimating radiation doses in CT examinations and the definitions of the CTDI, CTDIvol, DLP, SSDE, ED. We reviewed the reports from large analyses on the radiation doses in CT examinations of the coronary arteries prior to TAVI procedures, including the CRESCENT, PROTECTION, German Cardiac CT Registry studies. These studies were carried out over the last 10 years and can help confront the daily practice of performing cardiovascular CT examinations in most centres. The reference dose levels for these examinations were also collected. The methods to optimise the radiation dose included tube voltage reduction, ECG-monitored tube current modulation, iterative and deep learning reconstruction techniques, a reduction in the scan range, prospective study protocols, automatic exposure control, heart rate control, rational use of the calcium score, multi-slices and dual-source and wide-field tomography. We also present the studies that indicated the need to raise the organ conversion factor for cardiovascular studies from the 0.014–0.017 mSv/mGy*cm used for chest studies to date to a value of 0.0264–0.03 mSv/mGy*cm.
Telecom and scintillation first data analysis for DOMINO: laser communication between SOTA, onboard SOCRATES satellite, and MEO optical ground station

In collaboration between CNES, NICT, Geoazur, the first successful lasercom link between the micro-satellite SOCRATES and an OGS in Europe has been established. This paper presents some results of telecom and scintillation first data analysis for 4 successful links in June & July 2015 between SOTA terminal and MEO optical ground station (OGS) at Caussols France. The telecom and scintillation data have been continuously recorded during the passes by using a detector developed at the laboratory. An irradiance of 190 nW/m2 and 430 nW/m2 has been detected for 1549 nm and 976 nm downlinks at 35° elevation. Spectrums of power fluctuation measured at OGS are analyzed at different elevation angles and at different diameters of telescope aperture to determine fluctuations caused by pointing error (due to satellite & OGS telescope vibrations) and caused by atmospheric turbulence. Downlink & Uplink budgets are analyzed, the theoretical estimation matches well to measured power levels. Telecom signal forms and bit error rates (BER) of 1549 nm and 976 nm downlink are also shown at different diameters of telescope aperture. BER is 'Error Free' with full-aperture 1.5m telescope, and almost in ‘good channel’ with 0.4 m sub-aperture of telescope. We also show the comparison between the expected and measured BER distributions.
Steganography Using Modern Arts (Extended Abstract)

In this paper we present a novel approach to information hiding. We investigate the possibility of embedding information using in some way the naturally pseudorandomness of some classes of cover- documents. In particular we provide algorithms for embedding any binary string in an image belonging to a particular class of images, the image mosaics. The algorithms presented allow dierent levels of security for the information hidden in the cover-document. We also show some tech- niques to reduce the amount of information the users have to secretly store.
Generalized linear models with covariate measurement error and unknown link function

ABSTRACT Generalized linear models (GLMs) with error-in-covariates are useful in epidemiological research due to the ubiquity of non-normal response variables and inaccurate measurements. The link function in GLMs is chosen by the user depending on the type of response variable, frequently the canonical link function. When covariates are measured with error, incorrect inference can be made, compounded by incorrect choice of link function. In this article we propose three flexible approaches for handling error-in-covariates and estimating an unknown link simultaneously. The first approach uses a fully Bayesian (FB) hierarchical framework, treating the unobserved covariate as a latent variable to be integrated over. The second and third are approximate Bayesian approach which use a Laplace approximation to marginalize the variables measured with error out of the likelihood. Our simulation results show support that the FB approach is often a better choice than the approximate Bayesian approaches for adjusting for measurement error, particularly when the measurement error distribution is misspecified. These approaches are demonstrated on an application with binary response.
Code-Dependent Beam Shaping and Polarization Control by Binary Transmitarray

A code-dependent methodology is proposed to realize beam shaping and polarization control simultaneously by using binary transmitarray (BTA). The BTA unit elements are divided into two types, which are linearly polarized in orthogonal directions. By individually designing binary code for each type of unit elements, the radiation pattern and polarization of the synthetic field can be simultaneously defined. To verify this mechanism, four BTAs are designed at Ka-band with the same structures but with different binary codes to produce broadside directional beams with horizontal, vertical, left-handed circular, and right-handed circular polarizations, respectively. Furthermore, another BTA is designed to produce two directional beams pointing at different directions with left-handed and right-handed circular polarizations, respectively. Both simulations and measurements have verified that the proposed methodology provides a completely code-dependent mechanism to arbitrarily and independently design radiation patterns and polarizations of the BTAs. This methodology can be readily used to design binary programmable transmit arrays by integrating p-i-n or varactor diodes to the unit elements, so that radiation patterns and polarizations of the transmit arrays can be dynamically configured by merely changing the aperture codes, hence showing great values and potentials in communication and radar applications.
Physical Activity Patterns, Circadian Rhythms, and Aggressive and Suicidal Behavior among a Larger Sample of the General Population Aged 15 to 34 Years

Background: From a psychological perspective, aggressive behavior, non-suicidal self-injury and suicidal behavior could be considered dysfunctional coping strategies. Poor sleep patterns may further increase such dysfunctional coping. In contrast, regular physical activity may have the power to counteract such dysfunctional coping. Given this background, the aim of the present study was to combine categories of circadian rhythms as a proxy of normative sleep patterns and categories of physical activity patterns, and to associate these categories with aggressive behavior, non-suicidal self-injury and suicidal behavior among a larger sample of adolescents and young adults, aged 15 to 34 years. Method: A total of 2991 (55.6% females) individuals aged 15 to 34 years of the so-called Ravansar non-communicable disease cohort study (RaNCD) took part in this study. Participants completed self-rating questionnaires covering circadian-related sleep patterns, regular physical activity, socio-demographic information and dimensions of aggression, non-suicidal self-injury and suicidal behavior. Results: In a first step, both sleep patterns (circadian rhythm disorder: yes vs. no) and physical activity patterns (high vs. low) were dichotomized. Next, participants were assigned to one of four prototypical clusters: No circadian sleep disorders and high physical activity (“Hi-Sleep-Hi-PA”); no circadian sleep disorders and low physical activity (“Hi-Sleep-Lo-PA”); circadian sleep disorders and high physical activity (“Lo-Sleep-Hi-PA”); circadian sleep disorders and low physical activity (“Lo-Sleep-Lo-PA”). Projecting these four clusters on dimensions of aggressive behavior, non-suicidal self-injury and suicidal behavior, the following findings were observed: Participants of the “Hi-Sleep-Hi-PA” reported the lowest scores for aggressive behavior, self-injury and suicidal behavior, compared to participants of the “Lo-Sleep-Lo-PA” cluster. No differences for aggressive behavior, self-injury and suicidal behavior were observed among participants of the “Hi-Sleep-Lo-PA” and the “Lo-Sleep-Hi-PA” clusters. Conclusions: It appeared that the combination of favorable circadian sleep patterns and high physical activity patterns was associated with lower aggressive behavior, lower self-injury and suicidal behavior as proxies of favorable psychological functioning. In contrast, persons reporting high circadian sleep disorders and low physical activity patterns appeared to demand particular attention and counseling for both their lifestyle issues (sleep and physical activity) and their dysfunctional coping strategies.
Voltage Stability of Spacecraft Electric Power Systems for Deep Space Exploration

The future of deep space exploration requires high levels of reliability in critical subsystems such as the electrical power system. This paper provides an analysis of voltage stability of direct current (DC) microgrids for spacecraft applications. Bifurcation theory is used to determine the behavior of the system and identify the major causes of voltage instability. The analytical results of the bifurcation model are experimentally verified through a series of tests emulating probable operating conditions of the spacecraft. The findings of this paper are applicable to similar classes of islanded (grid forming) DC electric power systems including aerospace vehicles, shipboard systems, and terrestrial microgrids.
Upregulation of APAF1 and CSF1R in Peripheral Blood Mononuclear Cells of Parkinson’s Disease

Increased oxidative stress and neuroinflammation play a crucial role in the pathogenesis of Parkinson’s disease (PD). In this study, the expression levels of 52 genes related to oxidative stress and inflammation were measured in peripheral blood mononuclear cells of the discovery cohort including 48 PD patients and 25 healthy controls. Four genes, including ALDH1A, APAF1, CR1, and CSF1R, were found to be upregulated in PD patients. The expression patterns of these genes were validated in a second cohort of 101 PD patients and 61 healthy controls. The results confirmed the upregulation of APAF1 (PD: 0.34 ± 0.18, control: 0.26 ± 0.11, p < 0.001) and CSF1R (PD: 0.38 ± 0.12, control: 0.33 ± 0.10, p = 0.005) in PD patients. The expression level of APAF1 was correlated with the scores of the Unified Parkinson’s Disease Rating Scale (UPDRS, r = 0.235, p = 0.018) and 39-item PD questionnaire (PDQ-39, r = 0.250, p = 0.012). The expression level of CSF1R was negatively correlated with the scores of the mini-mental status examination (MMSE, r = −0.200, p = 0.047) and Montréal Cognitive Assessment (MoCA, r = −0.226, p = 0.023). These results highly suggest that oxidative stress biomarkers in peripheral blood may be useful in monitoring the progression of motor disabilities and cognitive decline in PD patients.
Organizational Responses to Product Cycles

We use daily administrative data from a leading automobile manufacturer to study the organizational impacts of introducing new models to the auto assembly line. We first show that costly defects per vehicle spike when new models are introduced. As a response, the firm trains in problem-solving skills and promotes lowerand midlevel employees to solve the more complex problems that arise, thus moving to a less pyramidal knowledge hierarchy with fewer layers. We develop an extension to the classic theory of knowledge-based hierarchies that reconciles our novel empirical results by allowing the firm to also invest in its training resources. JEL Codes: D22, M12, M53, O3
An Improved Joint Bayesian Method using Mirror Image's Features

The Joint Bayesian[1] method was published in 2012. Since then, it has been used for binary classification in almost all state-of-the-art face recognition methods. However, no improved methods have been published so far except 2D-JB[2]. In this paper we propose an improved version of the JB method that considers the features of both the given face image and its mirror image. In pattern classification, it is very likely to make a mistake when the value of the decision function is close to the decision boundary or the threshold. By making the value of the decision function far from the decision boundary, the proposed method reduces the errors. The experimental results show that the proposed method outperforms the JB and 2D-JB methods by more than 1% in the challenging LFW DB. Many state-of-the-art methods required tons of training data to improve 1% in the LFW DB, but the proposed method can make it in an easy way.
Some remarks on Fitzpatrick and Flynn's Gr¨ obner

In Fitzpatrick and Flynn (J. Symbolic Comput. 13 (1992) 133), a Grobner basis technique for multivariable Pade approximation problems was developed under a rather restrictive hypothesis on the shape of the numerator and denominator in relation to the approximation conditions desired. In this article, we show that their hypotheses can be replaced by other less stringent conditions, and we show how to compute some standard forms of multivariable approximants through several examples. © 2003 Elsevier Science Ltd. All rights reserved.
Recent Advances in the Synthesis of Propargyl Derivatives, and Their Application as Synthetic Intermediates and Building Blocks

The propargyl group is a highly versatile moiety whose introduction into small-molecule building blocks opens up new synthetic pathways for further elaboration. The last decade has witnessed remarkable progress in both the synthesis of propargylation agents and their application in the synthesis and functionalization of more elaborate/complex building blocks and intermediates. The goal of this review is to highlight these exciting advances and to underscore their impact.
Auto Sizing of CANDU Nuclear Reactor Fuel Channel Flaws from UT Scans

The inspection of nuclear power plants is an essential process that occurs during plant outages. During this process, various systems are inspected, including the reactor’s fuel channels to ensure that they are safe and reliable for the plant’s operation. The inspection of Canada Deuterium Uranium (CANDU®) reactor pressure tubes, which are the core component of the fuel channels and house the reactor fuel bundles, is performed using Ultrasonic Testing (UT). Based on the current process that is followed by Canadian nuclear operators, the UT scans are manually examined by analysts to locate, measure, and characterize pressure tube flaws. This paper proposes solutions for the auto-detection and sizing of pressure tube flaws using two deterministic algorithms, the first uses segmented linear regression, while the second uses the average time of flight (ToF) within ±σ of µ. When compared against a manual analysis stream, the linear regression algorithm and the average ToF achieved an average depth difference of 0.0180 mm and 0.0206 mm, respectively. These results are very close to the depth difference of 0.0156 mm when comparing two manual streams. Therefore, the proposed algorithms can be adopted in production, which can lead to significant cost savings in terms of time and labor.
Hierarchical bilateral filtering based disparity estimation for view synthesis

In this paper, we introduce a high efficient and practical disparity estimation using hierarchical bilateral filtering for real-time view synthesis. The proposed method is based on hierarchical stereo matching with hardware-efficient bilateral filtering. Hardware-efficient bilateral filtering is different from the exact bilateral filter. The purpose of the method is to design an edge-preserving filter that can be efficiently parallelized on hardware. The proposed hierarchical bilateral filtering based disparity estimation is essentially a coarse-to-fine use of stereo matching with bilateral filtering. It works as follows: firstly, the hierarchical image pyramid are constructed; the multi-scale algorithm then starts by applying a local stereo matching to the downsampled images at the coarsest level of the hierarchy. After the local stereo matching, the estimated disparity map is refined with the bilateral filtering. And then the refined disparity map will be adaptively upsampled to the next finer level. The upsampled disparity map used as a prior of the corresponding local stereo matching at the next level, and filtered and so on. The method we propose is essentially a combination of hierarchical stereo matching and hardware-efficient bilateral filtering. As a result, visual comparison using real-world stereoscopic video clips shows that the method gives better results than one of state-of-art methods in terms of robustness and computation time.
Brain Tumor Segmentation Network with Multi-View Ensemble Discrimination and Kernel-Sharing Dilated Convolution

Accurate segmentation of brain tumors from magnetic resonance 3D images (MRI) is critical for clinical decisions and surgical planning. Radiologists usually separate and analyze brain tumors by combining images of axial, coronal, and sagittal views. However, traditional convolutional neural network (CNN) models tend to use information from only a single view or one by one. Moreover, the existing models adopt a multi-branch structure with different-size convolution kernels in parallel to adapt to various tumor sizes. However, the difference in the convolution kernels’ parameters cannot precisely characterize the feature similarity of tumor lesion regions with various sizes, connectivity, and convexity. To address the above problems, we propose a hierarchical multi-view convolution method that decouples the standard 3D convolution into axial, coronal, and sagittal views to provide complementary-view features. Then, every pixel is classified by ensembling the discriminant results from the three views. Moreover, we propose a multi-branch kernel-sharing mechanism with a dilated rate to obtain parameter-consistent convolution kernels with different receptive fields. We use the BraTS2018 and BraTS2020 datasets for comparison experiments. The average Dice coefficients of the proposed network on the BraTS2020 dataset can reach 78.16%, 89.52%, and 83.05% for the enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively, while the number of parameters is only 0.5 M. Compared with the baseline network for brain tumor segmentation, the accuracy was improved by 1.74%, 0.5%, and 2.19%, respectively.
Research of the multimodal brain-tumor segmentation algorithm

It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.