Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromosomal Microarray Analysis in Fetuses Detected with Isolated Cardiovascular Malformation: A Multicenter Study, Systematic Review of the Literature and Meta-Analysis

Cardiovascular malformations (CVM) represent the most common structural anomalies, occurring in 0.7% of live births. The CVM prenatal suspicion should prompt an accurate investigation with fetal echocardiography and the assessment through genetic counseling and testing. In particular, chromosomal microarray analysis (CMA) allows the identification of copy number variations. We performed a systematic review and meta-analysis of the literature, studying the incremental diagnostic yield of CMA in fetal isolated CVM, scoring yields for each category of heart disease, with the aim of guiding genetic counseling and prenatal management. At the same time, we report 59 fetuses with isolated CVM with normal karyotype who underwent CMA. The incremental CMA diagnostic yield in fetuses with isolated CVM was 5.79% (CI 5.54–6.04), with conotruncal malformations showing the higher detection rate (15.93%). The yields for ventricular septal defects and aberrant right subclavian artery were the lowest (2.64% and 0.66%). Other CVM ranged from 4.42% to 6.67%. In the retrospective cohort, the diagnostic yield was consistent with literature data, with an overall CMA diagnostic yield of 3.38%. CMA in the prenatal setting was confirmed as a valuable tool for investigating the causes of fetal cardiovascular malformations.
Bulletin of Electrical Engineering and Informatics

Received Jan 26, 2022 Revised Mar 17, 2022 Accepted May 18, 2022 Due to the huge connectivity and ever-growing demands of diverse services and high data rate applications, more effective radio access techniques are required for the next generation wireless systems. Non-orthogonal multiple access (NOMA) is a promising candidate which has been recognized as an effective multiple access technique that notably improves the spectral efficiency (SE). In addition to SE, energy efficiency (EE) is also attracting too much interest nowadays due to the limited power of end users (EU) and internet of things (IoT) devices, and the strict environmental concerns related to global carbon dioxide (CO2) emission of communication devices. However, in fact there is a trade-off relationship between SE and EE. In this paper, the SE-EE trade-off relationship in NOMA-based systems is mathematically modeled and analyzed. On the other hand, in order to attain an elastic SE-EE trade-off relationship, the problem is formulated as an optimization problem with an objective of maximizing the EE under the constraint of satisfying a minimum SE demand. Simulation results confirmed the theoretical findings of this study and further asserted the validation of the proposed power allocation scheme which aims to achieve a more flexible trad-off relationship between SE and EE.
Explaining Return Anomalies with a Two-factor ICAPM Model

A two-factor Intertemporal Capital Asset Pricing Model (ICAPM) explains returns to risk associated with fundamentals with zero alpha. In contrast, the model identifies non-zero alphas for returns involving price movements rather than fundamentals, such as price momentum and price drifts. Further, these alphas are identified as mispricing under the ICAPM: In earning alpha, investors adopt factor exposures that differ from those a multiperiod investor rationally desires. Indeed, momentum crashes are explained as departure from optimal exposures.
ELK1-mediated YTHDF1 drives prostate cancer progression by facilitating the translation of Polo-like kinase 1 in an m6A dependent manner

Background: N6-methyladenosine (m6A) is one of the most prevalent mRNA modifications in mammals, and it regulates the fate of modified RNA transcripts. In the current study, we aimed to elucidate the role of YTH m6A RNA-binding protein 1 (YTHDF1), a “reader” of m6A modification, in prostate cancer tumorigenesis. Methods: We employed a multi-omics approach to detect the direct target of YTHDF1 upon manipulation of YTHDF1 expression in prostate cancer cells. Expression of YTHDF1 was also evaluated in human prostate tumors and either adjacent or paired normal tissues. Additionally, in vivo tumor growth and metastasis experimental assays were performed to evaluate the role of YTHDF1 in tumorigenesis. Finally, luciferase reporter assays and Chromatin immunoprecipitation (ChIP) were conducted to elucidate the transcriptional regulators of YTHDF1. Results: We demonstrated that polo-like kinase 1 (PLK1) is a direct target of YTHDF1. YTHDF1 facilitated the translation efficiency of PLK1 in an m6A-dependent manner by identifying the m6A-modified PLK1 mRNA and subsequently promoted the hyperactivation of the PI3K/AKT signaling pathway. Moreover, our results indicated that YTHDF1 was upregulated in prostate cancer tissue and that high YTHDF1 expression was associated with adverse prognosis in patients with prostate cancer. Furthermore, upregulation of YTHDF1 promoted prostate cancer tumorigenesis and metastasis in vitro and in vivo. Additionally, dysregulation of ETS transcription factor ELK1 activated the transcription of YTHDF1 by directly binding to its promoter region. Conclusions: Collectively, our findings suggest that the ELK1/YTHDF1/PLK1/PI3K/AKT axis is critical for prostate cancer progression and may serve as a potential therapeutic target for prostate cancer treatment.
The Role of Fault Rock Fabric in the Dynamics of Laboratory Faults

Fault stability is inherently linked to the frictional and healing properties of fault rocks and associated fabrics. Their complex interaction controls how the stored elastic energy is dissipated, that is, through creep or seismic motion. In this work, we focus on the relevance of fault fabrics in controlling the reactivation and slip behavior of dolomite‐anhydrite analog faults. We designed a set of laboratory experiments where we first develop fault rocks characterized by different grain size reduction and localization at normal stresses of σN = 15, 35, 60, and 100 MPa and second, we reload and reactivate these fault rocks at the frictional stability transition, achieved at σN = 35 MPa by reducing the machine stiffness. If normal stress is lowered this way, reactivation occurs with relatively large stress drops and large peak‐slip velocities. Subsequent unstable behavior produces slow stick‐slip events with low stress drop and with either asymmetric or Gaussian slip velocity function depending on the inherited fault fabric. If normal stress is raised, deformation is accommodated within angular cataclasites promoting stable slip. The integration of microstructural data (showing brittle reworking of preexisting textures) with mechanical data (documenting restrengthening and dilation upon reactivation) suggests that frictional and chemically assisted healing, which is common in natural faults during the interseismic phase, can be a relevant process in developing large instabilities. We also conclude that fault rock heterogeneity (fault fabric) modulates the slip velocity function and thus the dynamics of repeating stick‐slip cycles.
Fast and accurate sea-land segmentation of SAR imagery based on multi-source data registration

Abstract Targeted on accurate and efficient ship detection of Synthetic Aperture Radar imagery, sea-land segmentation is key for the complex port scenes. Despite the importance of this task, current methods are still severely influenced by several limitations, such as high level of image speckle noise, complicated backscattering characteristics. In order to overcome these issues, we propose a sea-land segmentation method of Synthetic Aperture Radar imagery based on multi-source data registration. To be specific, high-resolution optical remote sensing images and high-precision coastline vector data are integrated with Synthetic Aperture Radar images for accurate sea-land segmentation. First, the Pyramid Level-by-level Guided-Histogram of the Orientated Phase Congruency method is developed for the registration of Synthetic Aperture Radar and optical images, making two types of images into the same spatial reference. Following this, the high-precision coastline vector data collected from optical images is transferred to the Synthetic Aperture Radar image, realizing accurate sea-land segmentation. Finally, aiming at ship detection tasks, two quantitative evaluation indicators for sea-land segmentation are proposed: (1) the Absolute Segmentation Accuracy; and (2) the Ship Mis-segmentation Index. The accuracy and efficiency of the proposed method are tested and verified on Gaofen-3 SAR images of Yokosuka Port, Japan. Experimental results show that the proposed method achieves an Absolute Segmentation Accuracy of 3.15 pixels, a Ship Mis-segmentation Index of 9.23% and processing time of 82.92 s on average, which reveals that the proposed method outperforms current popular approaches in terms of accuracy and efficiency.
Robust Optimization for Bidirectional Dispatch Coordination of Large-Scale V2G

This paper proposes a robust optimization (RO) model for bidirectional dispatch coordination of large-scale plug-in electric vehicles (PEVs) in a power grid in which the PEVs are aggregated to manage. The PEV aggregators are considered as a type of dispatchable demand response and energy storage resource with stochastic behaviors, and can supply load or provide ancillary services such as regulation reserve to the grid. The proposed RO model is then reformulated as a mixed-integer quadratic programming model, which can be solved efficiently. Computer simulations are performed for a power grid with ten generators and three PEV aggregators to validate the economic benefit of the RO model for bidirectional dispatch coordination of the PEVs and the robustness of the RO model to the uncertainty of the PEVs' stochastic mobility behaviors.
Artificial Neural Networks, Non Linear Auto Regression Networks (NARX) and Causal Loop Diagram Approaches for Modelling Bridge Infrastructure Conditions

56 Introduction . 57 Background . 58 Methodology . 61 Results and Discussions . 77 Summary and conclusions . 81 CHAPTER FIVE . 85 5. A MODELING APPROACH FOR EVALUATING THE EFFECTS OF DESIGN VARIABLES ON BRIDGE CONDITION RATINGS . 85 Abstract . 85 Introduction . 86 Table of
The Cultural Value Validity of Digital Media Art Based on Deep Learning Network Model

The development of Internet digital media technology has enabled more works of different artistic styles to be discovered, learned, and appreciated by art lovers. Artists generate new creative ideas from different artistic painting styles, resulting in many artistic creation styles that are mixed with different artistic creation techniques. However, in the face of an increasing number of digital media art works, the identification of artistic and cultural value is mainly done manually by professionals, which costs a lot of human and financial resources. Therefore, it is of great practical significance to study how to efficiently and accurately classify various types of artistic images to help users select images that meet their needs. In order to solve this problem, this paper proposes a deep learning neural network model based on a dual-core compression activation module. The convolution kernels of different sizes in one module are used to extract the overall features and local details of the image, and another module is used to achieve the main goal. The enhancement of features and the suppression of irrelevant features realize the evaluation of artistic and cultural value. The experimental results show that, compared with mainstream neural network models and traditional classification algorithms, the proposed algorithm has higher classification accuracy and higher recognition and classification efficiency.
Supercritical CO2-Induced Evolution of Alkali-Activated Slag Cements

The phase changes in alkali-activated slag samples when exposed to supercritical carbonation were evaluated. Ground granulated blast furnace slag was activated with five different activators. The NaOH, Na2SiO3, CaO, Na2SO4, and MgO were used as activators. C-S-H is identified as the main reaction product in all samples along with other minor reaction products. The X-ray diffractograms showed the complete decalcification of C-S-H and the formation of CaCO3 polymorphs such as calcite, aragonite, and vaterite. The thermal decomposition of carbonated samples indicates a broader range of CO2 decomposition. Formation of highly cross-linked aluminosilicate gel and a reduction in unreacted slag content upon carbonation is observed through 29Si and 27Al NMR spectroscopy. The observations indicate complete decalcification of C-S-H with formation of highly cross-linked aluminosilicates upon sCO2 carbonation. A 20–30% CO2 consumption per reacted slag under supercritical conditions is observed.
School Surrounding Region Traffic Commuting Analysis Based on Simulation

Student commuting is an important part of urban travel demand and private car commuting plays an important role in urban traffic, especially in areas near schools. Since parents, especially the parents of elementary and junior high school students, prefer to drive rather than take public transport, there will be a negative effect on traffic management. To address the challenge, a simulation model is established based on schools’ surrounding regions to analyze traffic status. Specifically, the model focuses on urban construction and transportation near the entrance of schools and neighborhoods. In addition, four variable parameters consisting of the directional hourly volume, the parking demand of delivery vehicles, the distance between the school and intersection, and the average parking time for pick-up vehicles are set as influence factors, while traffic efficiency, energy consumption, and pollutant emissions are considered as the evaluation criteria of our model. Extensive simulated experiments show that comparing different scenarios, the traffic state of schools’ surrounding areas can achieve much better performance when the distance between entrances and intersections is 400 m under the 1000 pcu/h condition. This research can provide a scientific basis for school regional traffic management and organization optimization.
Cooperative Adaptive Cruise Control: Human Factors Analysis

FOREWORD Traffic is an increasing concern in many urban areas, and traffic congestion is growing at a faster rate than can be alleviated solely by additional road construction. This report examines a technology called Cooperative Adaptive Cruise Control (CACC) that aims to increase traffic throughput by safely permitting shorter following distances between vehicles. This report establishes a framework that the can be used to evaluate the human-factors, safety, and implementation issues associated with CACC. This document discusses CACC benefits and identifies various ways in which the CACC concept could be realized as well as human-factors-related issues of implementation. Several research areas are suggested to address these issues. Human-factors, operations, safety, and transportation researchers can use this report as a starting point to further define and execute critical research studies. These studies will, in turn, help facilitate the safe implementation of this mobility-enhancing technology in the years to come. The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers' names appear in this report only because they are considered essential to the objective of the document. Quality Assurance Statement The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. 16. Abstract Traffic congestion is growing at a faster rate than can be alleviated solely by additional road construction. Various Intelligent Transportation Systems technologies aim to increase and improve transportation via non-traditional means. Cooperative Adaptive Cruise Control (CACC) is one such technology, intended to increase traffic throughput by safely permitting shorter following distances between vehicles. Both vehicle-to-vehicle and vehicle-to-infrastructure communications help such endeavors at the micro-and macro-levels of traffic management. This report identifies the various ways in which the CACC concept could be realized and the human-factors-related implementation issues. Several research areas are suggested to address these human-factors issues. 17. in inches 25.4 millimeters mm ft feet 0.305 meters m yd yards 0.914 meters m mi miles 1.61 kilometers km
Forecasting Gold Prices Time Series by Using Joint Analysis and Separately Analysis

In recent years, there are many studies rely on forecasting with artificial neural networks. In this study, artificial neural networks are discussed considerably in demand over the past decade in the world finance literature. In the study, forecasting for the highest and the lowest gold prices with feed forward artificial neural networks are comprehensively studied. Linear or curvilinear functions are used in activation functions of artificial neural networks. Model1 and Model2 are used. Model1 has linear activation function in output layer and Model2 has lojistic activation function in output layer. Initially, we used two separate feed-forward artificial neural networks for analyzing the lowest and the highest gold prices values. Afterwards, lagged variables of time series are jo? ntly given to artificial neural networks as input. We jointly forecast the lowest and the highest gold prices. Artificial neural networks gave better results for certain architectures. The forecasting results are discussed according to Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Direction Accuracy (DA) criterion. Jointly analysis gave better results.
Documentation of atrial fibrillation among non-conveyed ambulance patients: a new primary prevention opportunity?

Introduction: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a significant risk factor for stroke. Prescription of oral anticoagulant (OAC) medication reduces the risk of AF-related stroke by 64% – yet over 400,000 people in England have undiagnosed (and therefore untreated) AF. Emergency medical services (EMS) encounter a wide range of patients, some of whom may not engage with other healthcare services. AF may be detected by EMS in connection with the cause of the call, or as an incidental finding. While EMS are not traditionally utilised for public health screening, they may offer an opportunity to identify patients with undiagnosed or untreated AF and refer onward. This study aimed to explore what proportion of patients seen by EMS who were not transported to hospital had AF and to estimate how many would potentially benefit from OAC. Methods: A retrospective service evaluation was conducted using routinely collected data from a large UK regional ambulance service. The sample included adults attended by EMS on the 15th of each month in 2019, who were not transported to hospital and where an electrocardiogram was recorded. Of those with AF, we calculated the proportion in whom this was possibly new and report whether OAC was prescribed. Results: There were 859 patients who met the inclusion criteria, of whom 91 (11%) had AF documented. Of the 91 patients with AF, 23 (25%) had no documented history of AF or OAC prescription, so were potentially new diagnoses of AF, who would benefit from consideration of OAC therapy. Conclusion: The EMS assessment offers an opportunity for AF to be identified in patients who were not transported to hospital. EMS may have a role in primary prevention of harm, including stroke, by identifying and referring patients with AF for consideration of OAC.
Rules of Engagement: Flexplace and Ideal Workers

Employees increasingly claim they do not have enough time to manage the demands of both work and family/life. Workplace flexibility policies have been offered as a key solution to managing these conflicting demands. Yet, employers remain resistant to develop, implement, and endorse these policies. We suggest one avenue to further our understanding is a more holistic look at the connection between availability and use of flexibility, and the workplace context. We specifically examine flexplace. The data derive from 25 in-depth interviews of employees in professional and supervisory positions in the U.S. automotive industry. By examining the variation of flexplace policy availability, we unpack the logics of employee use/non-use of flexplace. We argue that different assumptions of the ideal worker norm undergird flexplace availability, which in turn create different rules of engagement and use. This study offers an analytical model to extend our theorizing on the availability/use gap. Examination of ideal worker norms allows insight into how employees struggle to decipher signals on permissible flexplace use. The findings capture dynamic and interrelated relationships to uncover the constraints of policies and the power of the workplace context.
Silicon Photonic Modulators for Low-power Applications

In this book, silicon photonic integrated circuits are combined with electro-optic organic materials for realizing energy-efficient modulators with unprecedented performance. These silicon-organic hybrid Mach-Zehnder modulators feature a compact size, sub-Volt drive voltages, and they support data rates up to 84 Gbit/s. In addition, a wet chemical waveguide fabrication scheme and an efficient fiber-chip coupling scheme are presented.
A Bocage Landscape Restricts the Gene Flow of Pest Vole Populations

The population dynamics of most animal species inhabiting agro-ecosystems may be determined by landscape characteristics, with agricultural intensification and the reduction of natural habitats influencing dispersal and hence limiting gene flow. Increasing landscape complexity would thus benefit many endangered species by providing different ecological niches, but it could also lead to undesired effects in species that can act as crop pests and disease reservoirs. We tested the hypothesis that a highly variegated landscape influences patterns of genetic structure in agricultural pest voles. Ten populations of fossorial water vole, Arvicola scherman, located in a bocage landscape in Atlantic NW Spain were studied using DNA microsatellite markers and a graph-based model. The results showed a strong isolation-by-distance pattern with a significant genetic correlation at smaller geographic scales, while genetic differentiation at larger geographic scales indicated a hierarchical pattern of up to eight genetic clusters. A metapopulation-type structure was observed, immersed in a landscape with a low proportion of suitable habitats. Matrix scale rather than matrix heterogeneity per se may have an important effect upon gene flow, acting as a demographic sink. The identification of sub-populations, considered to be independent management units, allows the establishment of feasible population control efforts in this area. These insights support the use of agro-ecological tools aimed at recreating enclosed field systems when planning integrated managements for controlling patch-dependent species such as grassland voles.
Multiple purpose hardware for sensors at 100 GHz

This paper describes a 100-GHz multiple purpose high resolution radar, designed to fulfill the requirements of different applications: ISAR imaging of scaled model targets for automatic recognition tasks, micro-Doppler signature analysis of human gait and 3D scanning imaging for stand-off detection. Experimental results are included to show the radar system performance. (5 pages)