Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergence pattern and periodicity of Palmer amaranth (Amaranthus palmeri) populations from southcentral Great Plains

Abstract Evolution of multiple herbicide–resistant Palmer amaranth warrants the development of integrated strategies for its control in the southcentral Great Plains (SGP). To develop effective control strategies, a better understanding of the emergence biology of Palmer amaranth populations from the SGP region is needed. A common garden study was conducted in a no-till (NT) fallow field at the Kansas State University Agricultural Research Center near Hays, KS, during the 2018 and 2019 growing seasons, to determine the emergence pattern and periodicity of Palmer amaranth populations collected from the SGP region. Nine Palmer amaranth populations collected from five states were included: Colorado (CO1, CO2), Oklahoma (OK), Kansas (KS1, KS2), Texas (TX), and Nebraska (NE1, NE2, NE3). During the 2018 growing season, the CO1 and KS1 populations displayed more rapid emergence rates, with greater parameter b values (–5.4, and –5.3, respectively), whereas the TX and NE3 populations had the highest emergence rates (b = –12.2) in the 2019 growing season. The cumulative growing degree days (cGDD) required to achieve 10%, 50%, and 90% cumulative emergence ranged from 125 to 144, 190 to 254, and 285 to 445 in 2018; and 54 to 74, 88 to 160, and 105 to 420 in the 2019 growing season across all tested populations, respectively. The OK population exhibited the longest emergence duration (301 and 359 cGDD) in both growing seasons. All tested Palmer amaranth populations had a peak emergence period between May 11 and June 8 in 2018, and April 30 and June 1 in the 2019 growing season. Altogether, these results indicate the existence of differential emergence pattern and peak emergence periods of geographically distant Palmer amaranth populations from the SGP region. This information will help in developing prediction models for decision-making tools to manage Palmer amaranth in the region. Nomenclature: Palmer amaranth; Amaranthus palmeri S. Wats
Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Abstract In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.
Defect detection in pipes based on the measurement of surface displacements

This paper describes a novel guided wave technique for the detection of inhomogeneities in pipes, which is not relying on the excitation of one specific wavemode. The method works in the frequency domain and can be divided into three steps: (i) measurement of the surfacedisplacement of a pipe that is excited by an unknown combination of wavemodes at one discrete frequency, (ii) localization of the defect based on the surface-displacement, (iii) fitting the measured surface-displacement in the pipe-section in front of and behind the defect with a set of possible wavemodes. After this step, the contribution of every wavemode to the displacement-field and thus the wave-scattering of the incident modes at the defect is known. The technique is applied in a numerical model to analyze pipes with and without defect and to study the influence of the number of measurements used for the calculation of the scattering matrix and the presence of a random measurement error.
Clogging in Vertical Flow Constructed Wetlands: Causes for Clogging and Influence of Decontamination

Abstract With the continuous operation of constructed wetlands, substrate clogging is issue. In order to solve the problem, there is practical significance to understand the causes for clogging in constructed wetlands. Two pilot-scale vertical flow constructed wetlands were established, namely, CW-B and CW-C. By studying the relationship between the accumulation of different substances and the banked-up water area, it was found that the accumulation of non-filter substances and total solids was an important reason for the clogging of the substrate, and the accumulation degree of non-filter inorganic substances was more obvious than that of non-filter organic substances, and the blockage was mainly located in the 10-20 cm layer. In the vertical flow constructed wetland with river sand as the main substrate, water accumulation will occur when the content of total solid and non-filter substances exceeds 67.233 g and 101.228 g per cubic meter of substrate, respectively. Therefore, it is important to pay attention to the substrate particle size matching of 0-20 cm layer to reduce the clogging in the vertical flow constructed wetland. The clogging has little effect on chemical oxygen demand (COD) removal, but great effect on total phosphorus (TP) removal. Compared with the control wetland (CW-C), the biomass content in the CW-B with biochar increased by 334.26 nmol P/g, which can improve the removal efficiency of total nitrogen (TN) and total phosphorus (TP), but also increase the risk of clogging in the vertical flow constructed wetland. Future research should try to combine the anti-blocking research results of biochar constructed wetlands to improve the purification effect, which is of great significance to promote the sustainable development of constructed wetlands.
Evaluation of degradation in GaN high electron mobility transistors due to the inverse piezoelectric effect

EVALUATION OF DEGRADATION IN GaN HIGH ELECTRON MOBILITY TRANSISTORS DUE TO THE INVERSE PIEZOELECTRIC EFFECT Deepthi Nagulapally Old Dominion University, 2014 Director: Dr. Ravindra P. Joshi It has recently been postulated that high voltage stress can result in the degradation of nanoscale structures that are made up of piezoelectric materials. The inverse piezoelectric effect (IPE) is believed to be the likely reason for this degradation mechanism. Basically, the IPE leads to the creation o f high internal stresses driven by the presence o f an electric field. Consequently, devices based on piezoelectric materials are postulated to undergo defect formation induced by the large mechanical stress arising from the inverse piezoelectric effect in the presence o f an applied bias. GaN based devices are mostly observed to show this degradation mechanism, in particular AlGaN/GaN HEMTs. The key feature o f this mechanism is the sudden increase in leakage currents due to defect induced energy levels. The leakage currents can contribute to local heating or electromigration, and further enhance defect creation leading to an irreversible device degradation cycle. Given this possibility, and the need to mitigate such deleterious effects, it becomes important to understand and model this degradation mechanism in nanoscale devices. The aim of this dissertation research is to focus on the particular aspect o f the inverse piezoelectric effect, understand its role in potential device degradation o f GaNbased High Electron Mobility Transistors (HEMTs), and evaluate the possibilities of minimizing the inverse piezoelectric effect by optimizing the GaN-HEMT geometry and design parameters. The possible modifications o f the parameter space include changing device dimensions, varying the A1 composition, and employing high-k insulating cap layers. The effect of such changes on various device aspects such as the carrier density, strain, internal electric fields and the related stored energy etc. were carefully and systematically evaluated in this dissertation research. Details on the salient results, potential summarizing conclusions, and scope for future work are also presented.
Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process

The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome—the metagenome—are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host‐centric methods. Furthermore, the discoveries of intracellular and intra‐metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non‐human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs
Drug‐Loaded Mucoadhesive Patch with Active Delivery and Controlled Releasing Ability

Herein, a mucoadhesive patch for gastrointestinal tracts with active delivery, hyperthermia, and controlled drug release function using a magnetically actuated capsule is proposed to overcome the drug delivery and efficiency challenges of wireless structures. The proposed patch has excellent adhesion to the gastrointestinal tract and contains anticancer drug doxorubicin and magnetic nanoparticles. This enables hyperthermia and the controlled release of the loaded drug under an alternating magnetic field (AMF). In addition, it can be delivered to multiple intestinal target lesions using a magnetically actuated capsule. Characteristic analyses of the proposed patch are performed, such as morphology, adhesion force measurement with intestine, temperature change under an AMF, and drug release. The feasibility of the patch delivery into the gastrointestinal tract is verified through locomotion performance tests and ex vivo patch delivery experiments using a magnetically actuated capsule. Finally, through an in vitro therapeutic effect test, the death of tumor cells using the proposed patch is confirmed. As a result, the possibility that the multiple mucoadhesive patches can be delivered to target lesions in a digestive tract through a magnetically actuated capsule and can treat the lesions through hyperthermia and active drug release using an AMF stimulation is verified.
Empirical Model for Mobile Learning and Their Factors. Case Study: Universities Located at Metropolitan Zone of Guadalajara, México

The information and communication technologies (ICT) are producing new and innovative forms of teaching-learning process, so our research question is: Which is the Empirical Model for Mobile Learning and their Factors, in Universities located at Metropolitan Zone of Guadalajara, Mexico? This research is aimed to respond it, based on documentary study to select the variables with specialists in m-Learning using Analytic Hierarchy Process (AHP). The final Factors, discovered were 3: Technology (TECH); Contents, Teaching-Learning Management & Styles (CTLMS); Professor S with 13 Dimensions and 60 Variables. The study was applied on: 20 professors and 800 students both participating in social sciences courses, from 7 Universities located at Metropolitan Zone of Guadalajara, Mexico (UMZG) during the period 2013-2014 (24 months). The data of the questionnaires, were analyzed by structural equations modeling (SEM), using EQS 6.1 software. The final results suggest that there are 9/60 variables showing most influence to improve the interaction with mL model at UZMG.
The Link Between Parent–Child Relationship and Learning Engagement Among Adolescents: The Chain Mediating Roles of Learning Motivation and Academic Self-Efficacy

Previous studies have indicated that parent–child relationship as well as learning motivation and academic self-efficacy is an influential factor of learning engagement, but the impact mechanism of the relevant factors needs to be explored. The purpose of the current study is to investigate how parent–child relationship is related to learning engagement via the mediating roles of learning motivation and academic self-efficacy. Structural equation modeling (SEM) was adopted for the analysis of 280 participants from China. Results showed that parent–child relationship was directly correlated with adolescents’ learning engagement. Results also showed that parent–child relationship indirectly predicted adolescents’ learning engagement via learning motivation and academic self-efficacy respectively and sequentially. More importantly, it was revealed that the direct effect was much lower than the total indirect effects which include the indirect effect of learning motivation, the indirect effect of academic self-efficacy, and the indirect effect of learning motivation and academic self-efficacy. Among the three indirect effects, the indirect effect of learning motivation and academic self-efficacy was the greatest. Major findings were discussed with implications and limitations in the study.
Gamification for Machine Learning in Surgical Patient Engagement

Patients and their surgeons face a complex and evolving set of choices in the process of shared decision making. The plan of care must be tailored to individual patient risk factors and values, though objective estimates of risk can be elusive, and these risk factors are often modifiable and can alter the plan of care. Machine learning can perform real-time predictions of outcomes, though these technologies are limited by usability and interpretability. Gamification, or the use of game elements in non-game contexts, may be able to incorporate machine learning technology to help patients optimize their pre-operative risks, reduce in-hospital complications, and hasten recovery. This article proposes a theoretical mobile application to help guide decision making and provide evidence-based, tangible goals for patients and surgeons with the goal of achieving the best possible operative outcome that aligns with patient values.
Anti-Tumoral Effect and Action Mechanism of Exosomes Derived From Toxoplasma gondii-Infected Dendritic Cells in Mice Colorectal Cancer

Toxoplasma gondii is an obligate intracellular protozoan with anti-tumor activity against a variety of cancers. However, the therapeutic effect of T. gondii on colorectal cancer is unclear, and using direct Toxoplasma infection in immunotherapy involves safety concerns. This study investigated the anti-tumoral effect and mechanism of exosomes derived from dendritic cells (DCs) infected with T. gondii (Me49-DC-Exo). We used differential ultracentrifugation to isolate exosomes from uninfected DCs (DC-Exo) and T. gondii Me49-infected DCs (Me49-DC-Exo). The isolated exosomes were identified by transmission electron microscopy, nanoparticle tracking analysis, and western blotting. Me49-DC-Exo significantly inhibited the tumor growth and reduced the proportion of M2 macrophages in the blood of tumor-bearing mice. In vitro, Me49-DC-Exo suppressed macrophage (RAW264.7) polarization to M2 phenotype. miRNA sequencing revealed that multiple miRNAs in Me49-DC-Exo were differentially expressed compared with DC-Exo, among which miR-182-5p, miR-155-5p, miR-125b-2-3p, and miR-155-3p were up-regulated, while miR-9-5p was significantly down-regulated. Transfecting mimics or inhibitors of these differential miRNAs into RAW264.7 cells showed that miR-155-5p promoted M1 macrophage polarization while inhibiting M2 macrophage polarization. Bioinformatics prediction and dual-luciferase reporter assay confirmed the suppressor of cytokine signaling 1 (SOCS1) as a direct target of miR-155-5p. Silencing SOCS1 gene expression in RAW264.7 cells increased CD86 + CD206 − M1 macrophage proportion, and inducible nitric oxide synthase and tumor necrosis factor-α mRNA levels. However, arginase-1 and transglutaminase 2 expression levels decreased. These results suggest that the exosomes inhibit macrophage polarization to M2 phenotype and regulate SOCS1 expression by delivering functional miR-155-5p. These findings provide new ideas for colorectal cancer immunotherapy.
Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration

This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.
Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials

Genomic prediction has been established in breeding programs to predict the genotypic values of selection candidates without phenotypic data. First results in wheat showed that genomic predictions can also prove useful to select among material for which phenotypic data are available. In such a scenario, the selection candidates are evaluated with low intensity in the field. Genome-wide effects are estimated from the field data and are then used to predict the genotypic values of the selection candidates. The objectives of our simulation study were to investigate the correlations r(y, g) between genomic predictions y and genotypic values g and to compare these with the correlations r(p, g) between phenotypic values p and genotypic values g. We used data from a yield trial of 250 barley lines to estimate variance components and genome-wide effects. These parameters were used as basis for simulations. The simulations included multiple crossing schemes, population sizes, and varying sizes of the components of the masking variance. The genotypic values g of the selection candidates were obtained by genetic simulations, the phenotypic values p by simulating evaluation in the field, and the genomic predictions y by RR-BLUP effect estimation from the phenotypic values. The correlations r(y, g) were greater than the correlations r(p, g) for all investigated scenarios. We conclude that using genomic predictions for selection among candidates tested with low intensity in the field can proof useful for increasing the efficiency of barley breeding programs.
Prevalence of Transcriptionally Active HPV Infection in Tumor-Free Oropharyngeal Tissue of OPSCC-Patients

Objectives The natural history of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) is still largely unknown. Since reports of second primary tumors (SPTs) in patients with HPV-related OPSCCs are increasing, a multifocal HPV infection, hinting a «virus-induced field effect», has been hypothesized. This study aimed to investigate the HPV-prevalence in normal appearing oropharyngeal tissue in patients with OPSCCs. Materials and Methods 49 OPSCC patients undergoing panendoscopy were prospectively enrolled. Tumor specimens and biopsies of normal appearing oropharyngeal tissue adjacent to and distant from the index OPSCC underwent histopathological examination, p16INK4A immunohistochemical staining, HPV DNA and mRNA-detection. Patient characteristics and follow-up data on SPTs were obtained. Results 26 of 49 (53%) OPSCC were positive for HPV DNA and p16INK4A. HPV mRNA was detected in 23 of 26 (88%) of these tumor samples. HPV DNA was detected in 36% adjacent mucosa and in 17% distant mucosa samples and only in patients with an HPV-related index OPSCC. HPV mRNA could not be detected in tumor-free distant and adjacent mucosa samples. No evidence of association between HPV detection in normal appearing mucosa and development of second primary tumors was found. Conclusions HPV was detectable but not transcriptionally active in adjacent/distant tumor-free oropharyngeal tissue. This suggests that a multifocal HPV infection, hinting a «virus-induced fielcd cancerization», may not be pertaining to HPV-related OPSCC.
A Systematic Review Comparing Animal and Human Scarring Models

Introduction A reproducible, standardised model for cutaneous scar tissue to assess therapeutics is crucial to the progress of the field. A systematic review was performed to critically evaluate scarring models in both animal and human research. Method All studies in which cutaneous scars are modelling in animals or humans were included. Models that were focused on the wound healing process or those in humans with scars from an existing injury were excluded. Ovid Medline® was searched on 25 February 2019 to perform two near identical searches; one aimed at animals and the other aimed at humans. Two reviewers independently screened the titles and abstracts for study selection. Full texts of potentially suitable studies were then obtained for analysis. Results The animal kingdom search yielded 818 results, of which 71 were included in the review. Animals utilised included rabbits, mice, pigs, dogs and primates. Methods used for creating scar tissue included sharp excision, dermatome injury, thermal injury and injection of fibrotic substances. The search for scar assessment in humans yielded 287 results, of which 9 met the inclusion criteria. In all human studies, sharp incision was used to create scar tissue. Some studies focused on patients before or after elective surgery, including bilateral breast reduction, knee replacement or midline sternotomy. Discussion The rabbit ear scar model was the most popular tool for scar research, although pigs produce scar tissue which most closely resembles that of humans. Immunodeficient mouse models allow for in vivo engraftment and study of human scar tissue, however, there are limitations relating to the systemic response to these xenografts. Factors that determine the use of animals include cost of housing requirements, genetic traceability, and ethical concerns. In humans, surgical patients are often studied for scarring responses and outcomes, but reproducibility and patient factors that impact healing can limit interpretation. Human tissue use in vitro may serve as a good basis to rapidly screen and assess treatments prior to clinical use, with the advantage of reduced cost and setup requirements.