Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Osteogenic Potential of Electrospun PCL incorporated by Dendrimerized Superparamagnetic Nanoparticles as a Bone Tissue Engineering Scaffold

Nanotechnology plays a promising role in biomedical applications, particularly tissue engineering. Recently, the application of magnetic scaffolds and pulsed electromagnetic field (PEMF) exposure has been considered in bone tissue regeneration. In this study, 3rd generation dendrimer-modified superparamagnetic iron oxide nanoparticles (G3-SPIONs) are synthesized comprehensively characterized. Magnetic polycaprolactone (PCL) nanofibers are prepared by incorporating G3-SPIONs within the electrospinning process ,and physicochemical characteristics ,as well as cytocompatibility and cell attachment are assessed. Eventually, the osteogenic differentiation ability of adipocyte-derived mesenchymal stem cells (ADMSCs) cultured on the magnetic scaffold with and without PEMF exposure was investigated by measurement of alkaline phosphatase (ALP) activity and calcium content. The expression of specific bone markers was studied using the Real-time PCR method. According to the results, G3-SPIONs with mean size and zeta potential of 17.95 ± 3.57 nm and 22.7 mV, respectively, show a high saturation magnetization (57.75 emu/g). Adding G3-SPIONs to PCL significantly decrease nanofibers size to 495±144 nm and improves cell attachment and growth. The ADMSCs cultured on the G3-SPION-PCL scaffold in the presence of osteogenic media (OM) and exposure to PEMF expressed the highest Osteocalcin and Runx2 and showed higher calcium content as well as ALP activity. It can be concluded that the synthesized G3-SPION incorporated PCL nanofibers serve as a promising magnetic scaffold for bone regeneration. Also, utilizing the magnetic scaffold in the presence of OM and PEMF provides a synergistic effect toward osteogenic differentiation of ADMSCs. Key Words: Superparamagnetic iron oxide nanoparticles, Dendrimer, Polycaprolactone, Pulsed electromagnetic field, Bone tissue engineering
Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning

ABSTRACT It is a well-established fact that the flotation performance is reflected in the structure of the froth surface. A machine vision is able to extract the froth features and present them to the plant operators or the process control system. In this communication, computer vision and machine learning techniques are integrated for recognition of process conditions of a coal column flotation circuit. An industrial flotation column is operated under various process conditions. The metallurgical parameters (combustible recovery and concentrate ash content) are measured and the froth visual (bubble size, froth velocity, and color) and textural (energy, entropy, contrast, homogeneity, and correlation) features are extracted by a machine vision system. The principle component analysis (PCA) is applied to reduce the input space. The relationship between the froth characteristics and the metallurgical parameters is modeled using different intelligent algorithms and a predictive model is built. The froth images are classified based on the froth features using the K-means data-clustering algorithm. The predictive and classification models are eventually integrated to diagnose the process conditions of the flotation column.
Predicting Sediment Discharge at Water Treatment Plant Under Different Land Use Scenarios Coupling Expert-Based GIS Model and Deep Neural Network

Abstract. Excessive sediment discharge at karstic springs and thus, water treatment plants, can be highly disruptive. It is essential for catchment stakeholders and drinking water supplier to reduce the impact of sediment on potable water supply, but their strategic choices must be based on simulations, integrating surface and groundwater transfers, and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical description for the modelling process, and they can be seen as a black-box due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new coupled modelling approach with an erosion-runoff GIS model (WaterSed) and a deep neural network. The model was used in the Radicatel catchment (106 km2 in Normandy, France) where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five designed storm projects under current land use and compared to three land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes were used as input data for the deep neural network model. The model structure was found by a classical trial and error procedure, and the model was trained on two significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly-backward chaining nested cross validation revealed that the model is able to generalize on new datasets. Simulations made for the three land use scenarios suggested that ploughing up 33 % of grasslands would not increase significantly sediment discharge at the water treatment plant (5 % in average). In the opposite, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the range of 10–44 and 24–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 to 80 %). The coupled modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plant under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.
Caching Behavior of Large Prey by Eurasian Lynx: Quantifying the Anti-Scavenging Benefits

Large solitary felids often kill large prey items that can provide multiple meals. However, being able to utilize these multiple meals requires that they can cache the meat in a manner that delays its discovery by vertebrate and invertebrate scavengers. Covering the kill with vegetation and snow is a commonly observed strategy among felids. This study investigates the utility of this strategy using observational data from Eurasian lynx (Lynx lynx)-killed roe deer (Capreolus capreolus) carcasses, and a set of two experiments focused on vertebrate and invertebrate scavengers, respectively. Lynx-killed roe deer that were covered by snow or vegetation were less likely to have been visited by scavengers. Experimentally-deployed video-monitored roe deer carcasses had significantly longer time prior to discovery by avian scavengers when covered with vegetation. Carcass parts placed in cages that excluded vertebrate scavengers had delayed invertebrate activity when covered with vegetation. All three datasets indicated that covering a kill was a successful caching/anti-scavenger strategy. These results can help explain why lynx functional responses reach plateaus at relatively low kill rates. The success of this anti-scavenging behavior therefore has clear effects on the dynamics of a predator–prey system.
Evaluating the relationship between fecal egg count, FAMACHA score, and weight in dewormed and non-dewormed Katahdin rams during a parasite challenge

Abstract The objective of this study was to evaluate and to estimate the relationship between fecal egg counts (FECs) and FAMACHA score and the body weight of growing Katahdin rams during a parasite challenge. One of the largest factors negatively influencing reproduction and economics in the sheep industry is gastrointestinal nematode (GIN) parasites. Due to anthelmintic resistance of these parasites, animals are selected for parasite resistance using FEC and FAMACHA scores. Data were used from the Virginia Tech Southwest Agricultural Research and Extension Center Ram Test in Glade Spring, VA, from the year 2012 to 2018 in which animals were tested in 14-d intervals for 70 d. Mixed models for repeated weight measurements were made from backward stepwise selection to evaluate the relationships between weight and GIN FEC. A total of 576 animals within 23 contemporary groups derived from test year and pasture group were analyzed. Ram, contemporary group, and consignor were considered random effects, and fixed effects were birth type, test day, age, age squared, starting weight, FEC, and FAMACHA score. Pairwise contrasts were used in the statistical analysis of parameters and their interactions. Weight and age were found to have a quadratic relationship. Increased FEC was associated with weight loss at a rate of 0.00030 kg/FEC (P < 0.0001). Animals dewormed at any point during the trial weighed less than those that were not and increased with test day to a maximum difference of 4.66 kg (P < 0.001). FAMACHA score was found to be significant (P < 0.05), but a direct relationship with weight was not conclusive. Overall, rams with severe enough parasite load to require deworming had lesser weights, which could impact the profitability of sheep production and reinforced the need to select animals that had greater innate parasite resistance.
Patched Green's function method applied to acoustic wave propagation in disordered media: an interdisciplinary approach

Modern visualization can be formulated as inversion problems that aim to obtain structural information about a complex medium through wave excitations. However, without numerically efficient forward calculations, even state-of-the-art inversion procedures are too computationally intensive to implement. We adapt a method previously used to treat transport in electronic waveguides to describe acoustic wave motion in complex media with high gains in computational time. The method consists of describing the system as if it was made of disconnected parts that are patched together. By expressing the system in this manner, wave-propagation calculations that otherwise would involve a very large matrix can be done with considerably smaller matrices instead. In particular, by treating one of such patches as a target whose parameters are changeable, we are able to implement target-oriented optimization in which the model parameters can be continuously refined until the ideal result is reproduced. The so-called Patched Green's function (PGF) approach is mathematically exact and involves no approximations, thus improving the computational cost without compromising accuracy. Given the generality of our method, it can be applied to a wide variety of inversion problems. Here we apply it to the case of seismic modeling where acoustic waves are used to map the earth subsurface in order to identify and explore mineral resources. The technique is tested with realistic seismic models and compared to standard calculation methods. The reduction in computational complexity is remarkable and paves the way to treating larger systems with increasing accuracy levels.
Deep Learning in Image Analysis for COVID-19 Diagnosis: a Survey

COVID-19 achieved the highest concentration of confirmed cases in the Americas with a significant impact in Latin America and the Caribbean region, where access to water and sanitation is restricted. In this scenario, we surveyed deep learning techniques applied to extract information from images to detect pneumonia caused by SARS-COV-2, directly assisting health professionals through an automatic case screening. We identify the main public and private image datasets and deep network architectures. Thereby, we identified challenges and research directions. Thus, our goal is to provide a theoretical basis to contribute to the development of computational systems to aid the diagnosis of COVID-19.
Protective Effects of Maternal Education against Low Birth Weight Deliveries: Blacks' Diminished Returns.

Background Racial and economic disparities in low birth weight (LBW) deliveries is among the most well-established differences between Blacks and Whites. As LBW is an established risk factor for chronic diseases such as asthma and diabetes, it is particularly important to understand drivers of racial and economic disparities in LBW deliveries in urban areas. Aims Built on the Minorities' Diminished Returns framework, which argues that educational attainment generates fewer positive health outcomes for Black than White Americans, we conducted this study with three aims: 1) to test the association between mothers' educational attainment and LBW of babies born in urban areas, 2) to compare Blacks and Whites for the effect of mothers' educational attainment on LBW, and 3) to test whether LBW is predictive of future chronic diseases 15 years later. Methods Data came from the Fragile Families and Child Well-being / included a random sample of births in cities larger than 200,000 population. For the aims 1 and 2, we analyzed data of 2,922 births to Black (n = 2,146) or White (n = 776) mothers. For aim 3, we analyzed data of a subsample of 1,604 Black or White newborns who were followed to age 15. The presence or absence of chronic diseases was determined at age 15. Logistic regression was used for data analysis. Results Maternal educational attainment was inversely associated with LBW overall. We, however, found a significant interaction between maternal educational attainment and race, suggesting that the inverse association between maternal education and LBW is weaker for Black than White babies. At the same time, LBW increased the odds of chronic disease 15 years later. Conclusions Diminished returns of maternal educational attainment contribute to racial disparities in LBW, which in turn contributes to future racial inequalities in chronic diseases in urban settings. That is, smaller protective effects of maternal education on reducing LBW for Black than White children contribute to the high prevalence of chronic diseases among adolescents in urban settings. Health disparities are not just due to racial differences in socioeconomic status but also diminishing returns of socioeconomic status indicators such as education for racial and ethnic minorities. Research should study contextual factors that reduce Blacks' ability to translate their human capital to health outcomes in urban settings.
Efficient Hardware Design for the AV1 CDEF Filter Targeting 4K UHD Videos

Developed by the AOMedia industry consortium, the AOM Video 1 (AV1) is an open-source and royalty-free video encoder released in June 2018. The Constrained Directional Enhancement Filter (CDEF) is one of the three AV1 in-loop filters and it is the focus of this work. The CDEF has the goal to reduce ringing artifacts generated with the encoding process, acting as a directional deringing filter. This paper presents a hardware design for the AV1 CDEF targeting real-time processing of 4K Ultra High Definition (UHD) videos. The architecture was synthesized to ASIC using the 40nm TSMC library, requiring 185 kgates and with a power dissipation of 43 mW when running at 93 MHz, reaching the frame rate of 60 frames per second (fps). To the best of the author's knowledge, there is no other work in the literature with dedicated hardware design for the AV1 CDEF.
Fostering international students’ sense of belonging and perceived social support through a semester-long experiential activity

We tested whether a semester-long classroom experiential activity designed to foster international and domestic student social interaction increases international students’ sense of belonging and perceived social support. Two studies were conducted testing the effectiveness of this experiential activity which was based on the theoretical underpinnings of how social attachment is formed. The first study was a controlled experiment with freshmen university students. When compared to the control group, the students participating in the experiential activity had an increased sense of belonging and perceived social support by the end of the semester, especially when they had lower levels of openness at the onset of starting college. The second study was a longitudinal study conducted with graduate students. The results found that students had an increase in their sense of belonging and perceived social support. These studies suggest that semester-long classroom experiential activities designed to foster international and domestic student social interaction can foster international students’ sense of belonging and social support.
Does prophylactic ureteral catheter placement offer any advantage for laparoscopic gynecological surgery? A urologist’ perspective from a systematic review and meta-analysis

Our aim is to assess the efficacy of prophylactic ureteral catheter placement on patients undergoing laparoscopic gynecological surgery. We searched the electronic database including PubMed, Cochrane Library, Embase, Web of Science, WANFANG and CNKI in January 2020 to identify possible studies without languages limitations. A manual search was also conducted. The trials that compared catheterized group (CG) to non-catheterized group (NCG) were included. This meta-analysis was accomplished by RevMan5 (version 5.3). Initial search yield 997 studies and 5 randomized control trials were included in the final meta-analysis. Pooling data of five studies showed that patients in the CG had a lower risk of ureteral injury than those in NCG (RR: 0.44, 95% CI: 0.20–0.97, P=0.04) without significant between-study heterogeneity (P=0.23, I2=29%). The pooled data analysis showed a statistically significant difference in favor of prophylactic ureter catheter placement (MD: −40.51, 95% CI: −58.65 to −22.36, P<0.0001). Random-effects model meta-analysis found that patients in the CG experienced higher EBL compared to NCG (SMD: −5.78, 95% CI: −10.51 to −1.04, P=0.02). There was no statistically significant difference between CG group and NCG group (P=0.23) with regard to LOS. Current evidence indicates that prophylactic ureteral catheter placement has the advantages of reducing ureteral injury, shortening the operative time, and reducing the amount of bleeding. It might serve as a routine preoperative preparation choice for laparoscopic gynecological surgery, especially with pelvic adhesion. Further large volume, multicenter well-designed trials are warranted before making the final clinical guidelines.
Structural health monitoring on a steel-concrete composite continuous bridge during construction and vehicle load tests

Abstract This study presents the structural health monitoring system of an I-shaped steel-concrete composite girder bridge during construction and vehicle load tests. Strains and deflections were monitored. A finite element model was built and calibrated. Vehicle load tests were carried out for various load conditions. Measured results show that the strains in the concrete slab and steel beams changed rapidly in the first 15 days and stabilized. The composite girder was in an elastic working state under the vehicle load tests. This research provides guidance for the construction control and safety evaluation of similar I-shaped steel-concrete composite bridges in the future.
Monitoring and Evaluation of Sandstone Decay Adopting Non-Destructive Techniques: On-Site Application on Building Stones

This paper focuses on the characterization approach to evaluate the decay state of Pietra Serena of historic buildings in Florence (Italy). Pietra Serena is a Florentine sandstone largely used in the city especially during the Renaissance; it is a symbol of cultural heritage of Florence and constitutes a large part of the city center, which was named a World Heritage Site by UNESCO in 1982. Unfortunately, many environmental factors negatively affect the stone, increasing damage and the danger of falling material. Any detachment of stone fragments, in addition to constitute a loss in cultural heritage, can be dangerous for citizens and the many tourists that visit the city. The use of non-destructive techniques (NDTs) as ultrasonic and Schmidt hammer tests can quantitatively define some mechanical properties and help to monitor the decay degree of building stone. In this study, the NDTs were combined with mineralogical, petrographical, chemical and physical analyses to investigate the stone materials, in order to correlate their features with the characteristics of the different artefacts in Pietra Serena. Correlations between the NDTs results and the compositional characteristics of the on-site stone were carried out; such discussion allows to identify zones of weakness and dangerous unstable elements.
The Identification and Characterization of Stem/Progenitor Cells in the Ovarian Surface Epithelium

When a follicle ovulates, the ovarian surface epithelium (OSE), which covers the surface of the ovary, is ruptured and then rapidly regenerates. Because of this cycle of wound and repair, we determined if the OSE contains a stem/progenitor cell population. We identified a population of OSE cells with progenitor cell characteristics that express the stem cell marker Stem Cell Antigen-1 (SCA-1). We determined the size of the SCA-1 expressing (SCA-1+) progenitor cell population is regulated by at least two ovulationassociated factors present in the follicular fluid: Transforming Growth Factor beta 1 (TGFB1) and Leukaemia-Inhibitory Factor. Ovulation triggers an epithelial-tomesenchymal transition (EMT) in the OSE surrounding the ovulatory wound. Because TGFB1 is a known inducer of the EMT, and because the EMT plays a role in expanding stem cell populations in other epithelia, we determined if TGFB1 expands the SCA-1+ OSE progenitor cell population by triggering an EMT. Treatment with TGFB1 and overexpression of the EMT master gene, SNAIL, caused the OSE cells to undergo an EMT, but unlike TGFB1 treatment, SNAIL overexpression did not expand the SCA-1+ OSE population. Interestingly, EMT induced by SNAIL overexpression increased sphere formation by OSE cells, which indicated that it increased specific stem cell capabilities of the OSE. Because BRCA1 controls the size of the progenitor cell population in mammary epithelium, we determined if the OSE progenitor cell population is regulated by BRCA1. OSE with inactivated Brca1 were more stem-like and expressed higher mRNA levels of the known stem cell markers CD44, CD117, CD133 and Sca-1 compared to OSE with functional Brca1. Inactivation of Brca1 also increased the number of OSE cells with
Diagnosing Autism Using T1-W MRI With Multi-Kernel Learning and Hypergraph Neural Network

The field of network neuroscience provided unprecedented insights into how brain connectivity gets altered by autism spectrum disorder (ASD) on functional, structural, and morphological levels. However, a few studies have looked to design a framework that captures the complex network structure of the brain and disentangles the heterogeneity of ASD. In this paper, we leverage multi-kernel unsupervised learning in the construction of multiview hypergraph neural networks (HGNN), each capturing a particular view of the brain connectome, to eventually distinguish between ASD and normal control (NC) subjects. Additionally, we tested and measured how our proposed framework compares to other variants based on previous baseline methods. Our classification results outperformed comparison methods and agreed with the literature in the sense that the right hemisphere connectivity was more discriminative in ASD diagnosis than the left hemisphere.