Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Microplastic on the Population Dynamics of a Marine Copepod: Insights from a Laboratory Experiment and a Mechanistic Model

Microplastic is ubiquitously and persistently present in the marine environment, but knowledge of its population‐level effects is limited. In the present study, to quantify the potential theoretical population effect of microplastic, a two‐step approach was followed. First, the impact of microplastic (polyethylene, 0.995 g cm−3, diameter 10–45 µm) on the filtration rate of the pelagic copepod Temora longicornis was investigated under laboratory conditions. It was found that the filtration rate decreased at increasing microplastic concentrations and followed a concentration–response relationship but that at microplastic concentrations <100 particles L−1 the filtration rate was not affected. From the concentration–response relationship between the microplastic concentrations and the individual filtration rate a median effect concentration of the individual filtration rate (48 h) of 1956 ± 311 particles L−1 was found. In a second step, the dynamics of a T. longicornis population were simulated for realistic environmental conditions, and the effects of microplastics on the population density equilibrium were assessed. The empirical filtration rate data were incorporated in an individual‐based model implementation of the dynamic energy budget theory to deduct potential theoretical population‐level effects. The yearly averaged concentration at which the population equilibrium density would decrease by 50% was 593 ± 376 particles L−1. The theoretical effect concentrations at the population level were 4‐fold lower than effect concentrations at the individual level. However, the theoretical effect concentrations at the population level remain 3–5 orders of magnitude higher than ambient microplastic concentrations. Because the present experiment was short‐term laboratory‐based and the results were only indirectly validated with field data, the in situ implications of microplastic pollution for the dynamics of zooplankton field populations remain to be further investigated. Environ Toxicol Chem 2022;41:1663–1674. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Single-cell RNA-seq reveals heterogeneity in hiPSC-derived muscle progenitors and E2F family as a key regulator of proliferation

This study identified and characterized four different populations of muscle progenitor cells derived from human induced pluripotent stem cells. Human pluripotent stem cell-derived muscle progenitor cells (hiPSC-MuPCs) resemble fetal-stage muscle progenitor cells and possess in vivo regeneration capacity. However, the heterogeneity of hiPSC-MuPCs is unknown, which could impact the regenerative potential of these cells. Here, we established an hiPSC-MuPC atlas by performing single-cell RNA sequencing of hiPSC-MuPC cultures. Bioinformatic analysis revealed four cell clusters for hiPSC-MuPCs: myocytes, committed, cycling, and noncycling progenitors. Using FGFR4 as a marker for noncycling progenitors and cycling cells and CD36 as a marker for committed and myocyte cells, we found that FGFR4+ cells possess a higher regenerative capacity than CD36+ cells. We also identified the family of E2F transcription factors are key regulators of hiPSC-MuPC proliferation. Our study provides insights on the purification of hiPSC-MuPCs with higher regenerative potential and increases the understanding of the transcriptional regulation of hiPSC-MuPCs.
Community centered public safety resilience under public emergencies: A case study of COVID‐19

During public emergencies, the level of public safety will be resilient and follow a process from decline to rise. Regarding the concept and influencing factors of public safety resilience, a three‐level public safety resilience framework that includes personal, community, and government levels was proposed in this study. It provided the overall metrics that used the resistance and recovery ability to describe the dynamic characteristics of public safety resilience as well as the resilience assessment indexes on three levels. In the context of the Coronavirus Disease 2019 (COVID‐19) pandemic, this study applied the proposed framework in a case study on public safety resilience at the Beihang community, Beijing, China through descriptive statistics, structural equation model, and principal component regression analysis of questionnaire data. The data analysis results showed that community resilience was the most important of the three levels of public safety resilience. In addition, community resilience could improve personal resilience, and government resilience had a positive effect on community and personal resilience. Compared with the resistance ability, the recovery ability was influenced more by the operation and improvement of the community. This study is conducive to understanding and improving public safety resilience on the personal, community, and government levels and can help relevant parties improve their ability to respond to the COVID‐19 pandemic. Furthermore, the methods used in this study can be extended to other studies on public emergencies.
AOP173 key event associated pathway predictor – online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis

Abstract Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.
Low energy availability surrogates associated with lower bone mineral density and bone stress injury site

Exercise is an osteogenic stimulus that should increase bone mineral density (BMD) and protect against injury. However, some female athletes have lower BMD and increased bone stress injury (BSI) risk. Impaired bone health seen in athletes may be explained by low energy availability as described by concepts of Relative Energy Deficiency in Sport (RED‐S) and Female Athlete Triad (Triad).
Extended analysis of solid cancer incidence among the Nuclear Industry Workers in the UK: 1955–2011

Radiation worker studies provide direct estimates of cancer risk after protracted low-dose exposures to external X-ray and gamma-ray irradiations. The National Registry for Radiation Workers (NRRW) started in 1976 and has become the largest epidemiological program of research on nuclear workers in the UK. Here, we report on the relationship between solid cancer incidence and external radiation at the low-dose levels in 172,452 NRRW cohort members of whom (90%) were men. This study is based on 5.25 million person-years of follow-up from 1955 through the end of 2011. In the range of accumulated low doses two-thirds of workers have doses of less than 10 mSv. This study is an updated analysis of solid cancer incidence data with an additional 10 years of follow-up over the previous analysis of the NRRW cohort (NRRW-3). A total of 18,310 cases of solid cancers based on a 10-year lag were registered and of these 43% of the solid cancer cases occurred during the latest 10 years. Poisson regression was used to investigate the relationship between solid cancers risk and protracted chronic low-dose radiation exposure. This study demonstrated for solid cancers a rapid decrease of risk at high external doses that appeared to be driven by the workers who were monitored for potential exposure to internal emitters and who had also received relatively high external doses. Among cohort members only exposed to external radiation, a strong association was found between external dose and solid cancers (ERR/Sv = 0.52, 95% CI: 0.11; 0.96, based on 13,199 cases). A similar pattern is also seen for lung cancer. Excluding lung cancer from the grouping of all solid cancers resulted in evidence of a linear association with external radiation dose (ERR/Sv = 0.24, 95% CI: 0.01; 0.49, based on 15,035 cases), so suggesting some degree of confounding by smoking. Statistically significantly increasing trends with dose were seen for cancers of the colorectal, bladder and pleura cancer. Some of these results should be treated with caution because of the limited corroborating evidence from other published studies. Information on internal doses as well as non-radiation factors such smoking would be helpful to make more definitive inferences.
Hydrodynamics and vortex shedding characteristics of two tandem cylinders of different diameters in steady flow

Viscous flow past two tandem circular cylinders of different diameters is numerically investigated by using finite volume method. The diameter ratio between the two cylinders ranges from 0.1 to 1.6. The Reynolds number based on the diameter of cylinders is 800 for the large cylinder and 100 for the smallest cylinder. The spacing between two tandem cylinders ranges from 0.1 to 1.6 times the diameter of the large cylinder. Identified from the numerical results for all the cases of tandem configurations, spacing ratio* (S/d) = 1.5 is the critical point. The root mean square lift coefficient on both of the two cylinders abruptly increases at the critical point. The values of the fluctuating forces acting on the large cylinder are generally lower than those on a single cylinder. Three different wake modes are identified based on the vortex shedding characteristics behind two tandem cylinders, the one wake mode, the transitional mode and the reappearance mode. “Attachment” has been found in the transitional mode for the diameter ratio of d/D = 0.75 at the critical point. The numerical results are powerful basis for design of piggyback pipeline.
How Europe is approaching the smart grid

Public awareness of smart grid technology has expanded in Europe, especially as a result of the adoption by the European Union of the Third European Energy Liberalization Package. The main goal of this agreement is the installation of “intelligent metering systems” in 80 percent of households by 2020.1 New services by pioneering utilities such as Germany’s Yello Strom have also helped raise the profile of this new technology. Yet while almost all consumers in Italy now have smart meters, investment and development activity vary widely across the region and among smart grid segments. Enrico Giglioli, Cosma Panzacchi, and Leonardo Senni How Europe is approaching the smart grid
International Conference on Emerging Trends in Engineering , Science and Technology ( ICETEST-2015 ) Finite Element Analysis of Self-Centering Moment Resistant Frames with and without Steel Plate Shear Wall

The Northridge earthquake on January 1994 damaged a large number of buildings all through Los Angeles. The most recurrent pattern of structural damage was the brittle failures at beam column junction in steel moment-resisting frames (MRF’s)[6] This event brought out the need for further improving the Moment Resistant Frames and its connections. A new system of Moment Resisting Frames (MRF) commonly known as Self-Centering Moment Resisting Frames (SC-MRF) was developed. These designs are capable of resisting the seismic loads without sustaining much damage. The paper studies the behaviour of SelfCentering Moment Resisting Frames (SC-MRF) with and without steel plate shear walls subjected to cyclic loading. A detailed three dimensional finite element modeling using plate elements available in ANSYS FEA code is done. Geometric and material nonlinear stress analysis of the frame and a detailed comparison of the frame with and without steel plate shear walls is done. It can be observed that the sway of the SC-MRF frame is much higher than that of a conventional frame, but by introducing a thin steel plate shear wall this deformation can be brought down to a much lower value. © 2016 The Authors.Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of ICETEST – 2015.
A New Predictive Machine-learning Approach for Detecting Creditworthiness of Borrowers

We present in this paper a new approach for predicting creditworthiness of borrowers that we call “Method of splitting the learning set into two region”. The aim of this approach consists on the construction of two regions from a learning set, the first called "Solvency Region" that contains the feature vectors of the elements that have paid their financial obligations on time and the second one called "Non-Solvency Region", which contains the feature vectors of the elements that have defaulted in paying their debts. Therefore, to predict creditworthiness borrowers, it is sufficient to identify which of the two regions includes his feature vectors; if it doesn’t correspond to any region, the credit decision-making requires further analysis. To develop and test our predictive proposed approach, a large set of real and recent credit data obtained from the UCI repository is used, we trained also on a real credit database from a Moroccan bank and the creditworthiness of borrowers are analyzed using two performance measurement indicators such as Classification accuracy and the AUC of the ROC curve as a robustness measurement criteria. The proposed model was compared to three traditional machine-learning algorithms: LR, RBF-NN and the MLP-NN. The experimental results show the improved performance of our proposed predictive method for predicting creditworthiness of borrowers.
Digitized Doppler Signal Processor

At the present time there is a requirement for developing an airborne approach for processing radar doppler video data into digital PCM format compatible with current IRIG standards. Techniques for digitizing the doppler video presently exist, but have limitations due to the fact that the data is processed and represented in the time domain. These limitations can be mainly attributed to the high bit rates required for quantizing the dynamic nature of the doppler signal. Therefore, an alternate approach was selected by which the video doppler data is converted and represented in the frequency domain. The time to frequency domain conversion is accomplished with a digital Part Fourier Transform (FFT) implemented in conjunction with a quadrature translator. This method will provide a means by which the doppler signal can be represented as a quasi-static spectrum. The advantage in this application is that only the spectral data which contains relevant engineering information will be processed. The resultant system will thereby minimize the transmission bit rate and maximize the dynamic range for the purpose of signal analysis. The paper will describe the implementation and work performed on the digitized doppler signal processor along with the potential application in PCM systems requiring spectral signal analysis.
ROSE: Robust Caches for Amazon Product Search

Product search engines like Amazon Search often use caches to improve the customer user experience; caches can improve both the system’s latency as well as search quality. However, as search traffic increases over time, the cache’s ever-growing size can diminish the overall system performance. Furthermore, typos, misspellings, and redundancy widely witnessed in real-world product search queries can cause unnecessary cache misses, reducing the cache’s utility. In this paper, we introduce ROSE, a RObuSt cachE, a system that is tolerant to misspellings and typos while retaining the look-up cost of traditional caches. The core component of ROSE is a randomized hashing schema that makes ROSE able to index and retrieve an arbitrarily large set of queries with constant memory and constant time. ROSE is also robust to any query intent, typos, and grammatical errors with theoretical guarantees. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of ROSE. ROSE is deployed in the Amazon Search Engine and produced a significant improvement over the existing solutions across several key business metrics.
Unsupervised Superpixel-Driven Parcel Segmentation of Remote Sensing Images Using Graph Convolutional Network

Accurate parcel segmentation of remote sensing images plays an important role in ensuring various downstream tasks. Traditionally, parcel segmentation is based on supervised learning using precise parcel-level ground truth information, which is difficult to obtain. In this paper, we propose an end-to-end unsupervised Graph Convolutional Network (GCN)-based framework for superpixel-driven parcel segmentation of remote sensing images. The key component is a novel graph-based superpixel aggregation model, which effectively learns superpixels’ latent affinities and better aggregates similar ones in spatial and spectral spaces. We construct a multi-temporal multi-location testing dataset using Sentinel-2 images and the ground truth annotations in four different regions. Extensive experiments are conducted to demonstrate the efficacy and robustness of our proposed model. The best performance is achieved by our model compared with the competing methods.
Mining Homophilic Groups of Users using Edge Attributed Node Embedding from Enterprise Social Networks

We develop a method to identify groups of similarly behaving users with similar work contexts from their activity on enterprise social media. This would allow organizations to discover redundancies and increase efficiency. To better capture the network structure and communication characteristics, we model user communications with directed attributed edges in a graph. Communication parameters including engagement frequency, emotion words, and post lengths act as edge weights of the multiedge. Upon the resultant adjacency tensor, we develop a node embedding algorithm using higher order singular value tensor decomposition and convolutional autoencoder. We develop a peer group identification algorithm using the cluster labels obtained from the node embedding and show its results on Enron emails and StackExchange Workplace community. We observe that people of the same roles in enterprise social media are clustered together by our method. We provide a comparison with existing node embedding algorithms as a reference indicating that attributed social networks and our formulations are an efficient and scalable way to identify peer groups in an enterprise social network that aids in professional social matching.
Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network

: Human Activity Recognition (HAR) has become an active ﬁeld of research in the computer vision community. Recognizing the basic activities of human beings with the help of computers and mobile sensors can be beneﬁcial for numerous real-life applications. The main objective of this paper is to recognize six basic human activities, viz., jogging, sitting, standing, walking and whether a person is going upstairs or downstairs. This paper focuses on predicting the activities using a deep learning technique called Convolutional Neural Network (CNN) and the accelerometer present in smartphones. Furthermore, the methodology proposed in this paper focuses on grouping the data in the form of nodes and dividing the nodes into three major layers of the CNN after which the outcome is predicted in the output layer. This work also supports the evaluation of testing and training of the two-dimensional CNN model. Finally, it was observed that the model was able to give a good prediction of the activities with an average accuracy of 89.67%. Considering that the dataset used in this research work was built with the aid of smartphones, coming up with an efﬁcient model for such datasets and some futuristic ideas pose open challenges in the research community.
Using STPA to inform developmental product testing

Developmental product testing currently evaluates system safety the same way it evaluates system performance: it attempts to isolate individual components’ behaviors to evaluate their reliability. However, today’s systems are often irreducible because of their complexity, leaving current practices ineffective at identifying safety deficiencies. Evolving to a modern systems-based hazard analysis is important for product development. Products stand to benefit during the testing stage, before initial fielding. In test, designs meet operation for the first time, and use practices and organizational influences both contribute to the safety of the system. By evaluating safety as an emergent property, hazards that emerge because of the testing process itself can be mitigated, and hazards that exist because of the inherent system design and use philosophy can be identified and traced throughout development and fielding. System-Theoretic Process Analysis (STPA), developed by Nancy Leveson at the Massachusetts Institute of Technology, is a modern hazard analysis technique that identifies unsafe scenarios in a system in order to generate requirements to eliminate or control those scenarios. It improves on traditional reductionist approaches that treat accident causation only as a linear chain of events or probabilistic occurrence of simultaneous component failures (including human error). While systems-based and complete, STPA could benefit from additional guidance, particularly in the identification of human contributions to accidents. The present research begins by extending STPA to include more guidance for the controller analysis, including refinements to the process model, fundamental human-engineering considerations, and socio-organizational influences. Next, Leveson’s organizational control structure example is updated to include a test stage that serves as an intermediary between design and field use. Model inclusion criteria are updated, and Explicit-Influence Maps are introduced as a tool to understand the organization and aid in hazard analysis. Finally, this research investigates the U.S. Air Force developmental testing enterprise and applies STPA to a product test. Results are compared to that of the test-safety planning and reporting techniques traditionally in use, and utility is assessed with a research survey administered to developmental test professionals. Thesis Supervisor: Nancy G. Leveson Title: Professor of Aeronautics and Astronautics and Engineering Systems