Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Carrier-Frequency Division Multiple Access Radar: Waveform Design and Analysis

This paper proposes Single Carrier-FDMA radar, which is not only a solution to the shortcomings of OFDM radars but also offers improved Ambiguity Function and autocorrelation properties. Unlike OFDM, the proposed SC-FDMA radar (with interleaved subcarrier mapping) does not suffer from high peak to average power ratio (PAPR) issue and hence does not affect the efficiency of the transmitterin OFDM signals. Levanon and Mozeson’s power amplifier. SC-FDMA utilizes all the benefits of single-carrier modulation, orthogonal frequency division multiplexing and frequency domain equalization. The wideband characteristics of SC-FDMA are beneficial in achieving high range resolution for radar applications. The paper presents a complete architecture of SC-FDMA radar with interleaved subcarrier mapping. The proposed radar system is analyzed for its ambiguity function, auto-correlation properties, and PAPR. The performance of the proposed radar waveform is compared with those of the notable existing radar waveforms including a standard OFDM radar, CAN, Hadamard, and PWCIA. It is observed from the comparative analysis, that the proposed scheme outperforms OFDM and the other notable waveforms. The proposed work can be extended to develop multiple tasking radars as the different source signals can correspond to independent tasks to be performed simultaneously.
An Encoder-Decoder Neural Network With 3D Squeeze-and-Excitation and Deep Supervision for Brain Tumor Segmentation

Brain tumor segmentation from medical images is a prerequisite to provide a quantitative and intuitive reference for clinical diagnosis and treatment. Manual segmentation depends on clinicians’ experience, and is laborious and time-consuming. To tackle these issues, we proposed an encoder-decoder neural network, i.e. deep supervised 3D Squeeze-and-Excitation V-Net (DSSE-V-Net) to segment brain tumors automatically. We modified V-Net by adding batch normalization and using bottom residual block to make the network deeper. Then we incorporated a squeeze & excitation(SE) module in the modified V-Net by adding the SE block in each stage of the encoder and decoder, respectively. We also integrated 3D deep supervision seamlessly into the network to accelerate convergence. We evaluated our model on the public BraTS 2017 dataset for brain tumor segmentation. Our model outperformed both 3D U-Net and modified V-Net, and obtained highly competitive performance compared with those methods winning in the BraTS 2017 challenge.
An Optimized Broadband Waveguide Magic-T for X-band Applications

The design of a broadband waveguide magic-T, covering the X-band frequencies from 8 GHz to 12 GHz is presented. In order to obtain such a large bandwidth, disk loaded pins for the feed, ridges and a tapered conical post are used in our design. Extensive optimizations were performed to achieve a return loss better than 17 dB over the frequency range from 8 GHz to 12 GHz. Spline interpolation, quasi Newton and pattern search methods of HFSS software were employed to perform the optimization of the tapered post. The proposed structure was fabricated and measured to verify the design. There is a good agreement between measured and simulated results.
Laser-processed diffractive optics for terahertz waves

Diffractive optics such as Soret zone plate lenses, multi-level phase-correcting Fresnel lenses, Fibonacci lenses, and antire-flective structures for terahertz (THz) waves were developed for the frequency range of 0.6-4.7 THz by using a direct laser ablation (DLA) technology. The laser-processing of the surface and volume of different metals as well as highresistivity semiconductors were implemented to demonstrate the applicability and flexibility of the mask-less technology. Main performance of binary-phase profile lenses fabricated either on a silicon wafer or a metal foil was the focusing gain up to 25 dB and the focused beam size down to 1.1 λ. Possibility to increase of the phase quantisation level to eight steps provided higher focusing gain values up to 29 dB without a measurable increase of optical losses. Moreover, the diffractive optics demonstrated the tolerance to misalignment as large as 10 degrees of oblique incidence with a focusing power drop below 10%. We also highlighted some novel approaches of THz imaging with the multi-foci lenses.
The Epidemiology of Invasive Group B Streptococcus in Denmark From 2005 to 2018

Previous epidemiology reports on invasive Streptococcus agalactiae (GBS) infections in Denmark did not include all patient age groups. The aim of this study was therefore to analyze the GBS incidence in all age groups during the period 2005–2018 and to present the serotype distribution and the antibiotic susceptibility. Data were retrieved from the Danish laboratory surveillance system, and these included data on typing and susceptibility testing for erythromycin and clindamycin. Early-onset disease (EOD) (mean incidence 0.17 per 1,000 live births) and late-onset disease (LOD) (mean incidence 0.14 per 1,000 live births) showed a low level during the period. The incidence was stable in the age groups 91 days to 4 years, 5–19 years, and 20–64 years. From 2005 to 2018, the incidence in the elderly showed a significantly increasing trend (P < 0.05), that in the 65–74 years increased from 3.23 to 8.34 per 100,000, and that in the 75+ years increased from 6.85 to 16.01 per 100,000. Erythromycin and clindamycin resistance fluctuated over the period; however, the overall trend was increasing. Data showed that EOD and LOD incidence continued to be low, whereas an increasing trend in GBS infections in the elderly was observed. The prevalence of erythromycin and clindamycin resistance increased from 2005 to 2018.
Multi-Sensor Fusion Method Based on Artificial Neural Network for Mobile Robot Self-Localization

This paper presents a hierarchical sensor fusion (HSF) method with an artificial neural network (ANN) to solve the problem of mobile robot self-localization with sonars octagon, digital compass, and wireless network signal strength measure to determine the location of an autonomous mobile robot. The multilayer perceptron (MLP) is used with supervised learning, backpropagation technique, to train the network in hierarchical fusion step and determine the robot localization in a map. In order to validate this work, a comparison between the HSF methods, artificial intelligence, and the matching algorithm, using the same training and testing UFPR-RSFM Dataset. Finally, the HSF method with artificial intelligence technique can determine the robot localization in a different indoor environment, using low-cost sensors, and support the relevance of hierarchical sensor fusion in mobile robot localization.
Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

This study presents a shallow and robust road segmentation model. The computer-aided real-time applications, like driver assistance, require real-time and accurate processing. Current studies use Deep Convolutional Neural Networks (DCNN) for road segmentation. However, DCNN requires high computational power and lots of labeled data to learn abstract features for deeper layers. The deeper the layer is, the more abstract information it tends to learn. Moreover, the prediction time of the DCNN network is an important aspect of autonomous vehicles. To overcome these issues, a Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from the explicitly derived representations of the input image. Gradient information of the input image is used as additional channels to enhance the learning process of the proposed deep learning architecture. The multi-feature views are fed to a fully-connected neural network to accurately segment the road regions. The testing accuracy demonstrates that the proposed MVS-CNN achieved an improvement of 2.7% as compared to baseline CNN consisting of only RGB inputs. Furthermore, the comparison of the proposed method with the popular semantic segmentation network (SegNet) has shown that the proposed scheme performs better while being more efficient during training and evaluation. Unlike traditional segmentation techniques, which are based on the encoder-decoder architecture, the proposed MVS-CNN consists of only the encoder network. The proposed MVS-CNN has been trained and validated with two well-known datasets: the KITTI Vision Benchmark and the Cityscapes dataset. The results have been compared with the state-of-the-art deep learning architectures. The proposed MVS-CNN outperforms and shows supremacy in terms of model accuracy, processing time, and segmentation accuracy. Based on the experimental results, the proposed architecture can be considered as an efficient road segmentation architecture for autonomous vehicle systems.
Germline and Somatic BRCA1/2 Mutations in 172 Chinese Women With Epithelial Ovarian Cancer

Objective: Despite several nationwide cohort studies of germline BRCA1/2 mutations and several small cohort studies of somatic BRCA1/2 mutations in Chinese epithelial ovarian cancer (EOC) patients, little is known about the impact of these findings on survival outcomes in this population. In this study of 172 retrospectively recruited Chinese EOC patients, germline and somatic BRCA1/2 mutations and their value for predicting survival outcomes were evaluated. Methods: Unselected patients who visited the study center from January 1, 2011, to January 1, 2015, were recruited and asked to provide peripheral blood samples for this study if they were pathologically confirmed to have primary EOC. All patients received staging surgeries or debulking surgeries involving systemic platinum-based chemotherapy, and the patients were then followed up to December 1, 2017. DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) sections and peripheral blood and sequenced for somatic and germline testing, respectively. The demographic and clinicopathological characteristics of the patients were collected to analyze the distribution of BRCA mutations in subgroups. Survival outcomes were compared among various BRCA mutation statuses using univariate and multivariate models. Results: In 58 (33.7%) patients, 63 variants were identified, including variants of unknown significance (VUS) in 18 patients (10.5%) and pathogenic or likely pathogenic variants in a partially overlapping set of 41 patients (23.8%). Germline BRCA mutations, somatic BRCA mutations, BRCA1 mutations in general, and BRCA2 mutations in general were found in 35 (20.3%), 7 (4.1%), 28 (16.3%), and 13 (7.6%) patients, respectively. Five recurrent mutations were identified. Personal and family cancer histories as well as hereditary breast and ovarian cancer (HBOC) criteria were associated with deleterious BRCA mutations both overall and in the germline specifically, whereas only age at diagnosis of EOC was associated with somatic BRCA mutations. In univariate and Cox regression analyses, patients with BRCA1/2 mutations in general had significant improvements in progression-free survival (PFS) and overall survival (OS). Conclusions: In Chinese EOC patients, the distributions and risk factors associated with germline and somatic BRCA1/2 mutations were similar to those previously reported in international studies. Deleterious BRCA mutations in general were associated with improved survival outcomes in this cohort.
The Differential Expression of ERAP1/ERAP2 and Immune Cell Activation in Pre-eclampsia

Pre-eclampsia (PE) is a disorder of pregnancy, often leading to serious and fatal complications. Endoplasmic reticulum aminopeptidase 1 and 2 (ERAP1/ERAP2) are present in the placenta. They are involved in processes regulating blood pressure, angiogenesis, cytokine receptor shedding, and immune recognition. Previous studies have associated both ERAP1/ERAP2 genetic variants with PE, although the underlying mechanisms remain unknown. Less is known about the roles for these enzymes in early placentation, which could be a contributory factor to PE. To ascertain whether ERAP1/ERAP2 change in PE and whether such a change is present before PE is clinically diagnosed, we analyzed mRNA and ERAP1/2 protein expression in the placenta in the early first trimester (8–14 weeks) and at delivery in normotensive or PE women (n = 12/group). Gene expression was analyzed using qPCR, and protein expression and localization were assessed by immunohistochemistry. Additionally, we profiled peripheral immune cells from normotensive and PE (n = 5/group) women for activation and expression of cytotoxic markers using flow cytometry to investigate a possible correlation with placental expression of ERAP1/2. Finally, we characterized the cytokines released from immune cells isolated from normotensive women and those with PE, stimulated ex vivo by JEG-3 trophoblast cells. The ERAP1 protein was significantly upregulated in first trimester placentae compared to placentae at delivery from both normotensive and PE women (p < 0.05): expression of placental ERAP1 protein was also relatively higher in normotensive than PE women. Although the protein expression of both ERAP1/ERAP2 was significantly lower in women with PE compared to normotensive controls (p < 0.05), ERAP2 protein expression remained unchanged in normotensive women at delivery compared to expression in the first trimester. Flow cytometry analysis revealed an increase in activation and cytotoxic natural killer (NK) cells in peripheral blood of PE compared to normotensive women. Intriguingly, there was a notable difference in cytokine release from the activated immune cells when further stimulated by trophoblast cells. The immune cells from PE released elevated expressions of interleukin (IL)-2, IL-4, and most notably, pro-inflammatory IL-13 and IL-17α, inflammatory cytokines tumor necrosis factor (TNF)-α and interferon (IFN)-γ, and granulocyte-macrophage colony-stimulating factor (GM-CSF) compared to normal peripheral blood mononuclear cells (PBMCs). Taken together, these findings suggest that differential lymphocyte activation could be associated with altered ERAP1/ERAP2 expression.
Aperture-Level Simultaneous Transmit and Receive With Digital Phased Arrays

While purely digital phased arrays were once discarded as simultaneous transmit and receive (STAR) capable platforms, this notion has recently been reconsidered. Previous work demonstrated that adaptive digital beamforming and digital self-interference cancellation (SIC) can enable transmitting and receiving subapertures in an array to operate simultaneously in the same frequency band. This approach, referred to as Aperture-Level Simultaneous Transmit and Receive (ALSTAR), uses only adaptive digital beamforming and digital SIC techniques. The ALSTAR architecture does not require custom radiators or analog canceling circuits that can increase front end losses and add significant size, weight, and cost to the array. This paper extends the previously proposed effective isotropic isolation (EII) metric to account for fixed dynamic range transmit and receive channels. An alternating optimization procedure that exploits the interdependence of the transmit and receive beamformers is proposed based on the symmetry of the EII metric, achieving higher EII than in previous work. This optimization procedure balances the goal of null-placement for interference and noise rejection with the goal of maintaining high transmit and receive gain. Simulated results are presented for a <inline-formula><tex-math notation="LaTeX">$\mathbf {50}$</tex-math></inline-formula>-element array that achieves <inline-formula><tex-math notation="LaTeX">$\mathbf {187.1}$</tex-math></inline-formula> dB of EII in narrowband operation with <inline-formula><tex-math notation="LaTeX">$\mathbf {2500}$</tex-math></inline-formula> W of transmit power. We explore the effectiveness of the architecture and proposed optimization methods by demonstrating the high EII achieved across the full scan space of the array at several transmit power levels. Results are also presented for a regularized version of the beamformer optimization problem that allows the designer to trade EII for array gain.
The Effects of Family Functioning and Psychological Suzhi Between School Climate and Problem Behaviors

Problem behaviors have always been a hot topic in the field of adolescent research. It is particularly important to study how problem behaviors are developed. Empirical evidence examining problem behaviors has shared the premise that perceived school climate and family functioning play a role in the development of problem behaviors in adolescents. However, it is less clear whether the interaction of perceived school climate and family functioning can predict problem behaviors in adolescents and which mechanisms within the process it might affect. The present study developed a moderated mediation model to investigate the relationship between perceived school climate, family functioning, psychological suzhi, and problem behaviors in early adolescents. Participants were 1,072 Chinese junior high school students who completed the Perceived School Climate Questionnaire, the Strengths and Difficulties Questionnaire, the Psychological Suzhi Questionnaire for Middle School Students, and the Family APGAR scales. Data were analyzed using IBM SPSS Statistics version 22.0, including descriptive statistics and correlation analysis. The mediating effect and moderating effect were tested by SPSS PROCESS. Results showed that there was a significant negative correlation between perceived school climate and problem behaviors and a partial mediating role of psychological suzhi between perceived school climate and problem behaviors. Moreover, the influence of perceived school climate on psychological suzhi was moderated by family functioning. Indirect effects were significant in participants with high versus low family functioning. There was an interaction between family and school, and psychological suzhi played an important role between environment and adolescent behaviors. This study validates the combined effect of family systems, school systems, and personal systems on problem behaviors and has certain guiding significance for the prevention and intervention of problem behaviors among adolescents.
High Net Primary Production of Mediterranean Seagrass (Posidonia oceanica) Meadows Determined With Aquatic Eddy Covariance

We report primary production and respiration of Posidonia oceanica meadows determined with the non-invasive aquatic eddy covariance technique. Oxygen fluxes were measured in late spring at an open-water meadow (300 m from shore), at a nearshore meadow (60 m from shore), and at an adjacent sand bed. Despite the oligotrophic environment, the meadows were highly productive and highly autotrophic. Net ecosystem production (54 to 119 mmol m–2 d–1) was about one-half of gross primary production. In adjacent sands, net primary production was a tenth- to a twentieth smaller (4.6 mmol m–2 d–1). Thus, P. oceanica meadows are an oasis of productivity in unproductive surroundings. During the night, dissolved oxygen was depleted in the open-water meadow. This caused a hysteresis where oxygen production in the late afternoon was greater than in the morning at the same irradiance. Therefore, for accurate measurements of diel primary production and respiration in this system, oxygen must be measured within the canopy. Generally, these measurements demonstrate that P. oceanica meadows fix substantially more organic carbon than they respire. This supports the high rate of organic carbon accumulation and export for which the ecosystem is known.
Integrative Analysis of Breast Cancer Cells Reveals an Epithelial-Mesenchymal Transition Role in Adaptation to Acidic Microenvironment

Early ducts of breast tumors are unequivocally acidic. High rates of glycolysis combined with poor perfusion lead to a congestion of acidic metabolites in the tumor microenvironment, and pre-malignant cells must adapt to this acidosis to thrive. Adaptation to acidosis selects cancer cells that can thrive in harsh conditions and are capable of outgrowing the normal or non-adapted neighbors. This selection is usually accompanied by phenotypic change. Epithelial mesenchymal transition (EMT) is one of the most important switches correlated to malignant tumor cell phenotype and has been shown to be induced by tumor acidosis. New evidence shows that the EMT switch is not a binary system and occurs on a spectrum of transition states. During confirmation of the EMT phenotype, our results demonstrated a partial EMT phenotype in our acid-adapted cell population. Using RNA sequencing and network analysis we found 10 dysregulated network motifs in acid-adapted breast cancer cells playing a role in EMT. Our further integrative analysis of RNA sequencing and SILAC proteomics resulted in recognition of S100B and S100A6 proteins at both the RNA and protein level. Higher expression of S100B and S100A6 was validated in vitro by Immunocytochemistry. We further validated our finding both in vitro and in patients' samples by IHC analysis of Tissue Microarray (TMA). Correlation analysis of S100A6 and LAMP2b as marker of acidosis in each patient from Moffitt TMA approved the acid related role of S100A6 in breast cancer patients. Also, DCIS patients with higher expression of S100A6 showed lower survival compared to lower expression. We propose essential roles of acid adaptation in cancer cells EMT process through S100 proteins such as S100A6 that can be used as therapeutic strategy targeting both acid-adapted and malignant phenotypes.
An Assessment of Idea Emergence in Subject-Matter Collaborative Learning

Idea emergence is critical in learning as knowledge creation. Although recent advancements make it possible to detect emergent ideas and evaluate how students engage in knowledge creation in collaborative learning contexts, the relationship between the learning processes and final conceptual understanding has not been well-studied. One confounding factor is how much students engage in their study topic during collaboration. In this study, therefore, we propose a new procedure for evaluating idea emergence in the context of jigsaw instruction by combining a socio-semantic network analysis of discourse and a text-mining algorithm, “the term-frequency. ” This procedure was used to evaluate how high-school learners engaged in their social process of knowledge creation as well as how much they discuss their study topics, the human immune system. Results showed that the weight of priority on a study topic did not significantly differ in both high and low conceptual understanding groups, but high conceptual understanding groups were more engaged in sharing and discussing ideas in the early stage of their collaboration. It is suggested that students' recognition of the study topic was not different depending on the levels of their conceptual understanding in a well-structured collaborative learning context such as jigsaw instruction. However, what matters is how students discuss their ideas through collaborative discourse.