Miracle-dz/UniFAR-FTUs huggingface dataset:
Inducing Potential Mutants in Bread Wheat Using Different Doses of Certain Physical and Chemical Mutagens

Mutation is an effective strategy not only for creating novel variation into crop genome but also for direct releasing adapted and high-yielding genotypes. The current work explores inducing genetic variability in bread wheat using physical and chemical mutagens. Three wheat cultivars were treated by three mutagens; gamma irradiation (five doses; 250, 300, 350, 400 and 450 Gray); laser ray (three treatments; 1, 1.5, and 2 hour exposure) and EMS (three concentrations; 0.2, 0.3 and 0.4%). Besides, a combination of physical (laser) and chemical (EMS) mutagens using middle range of each treatment (1.5 hour laser and 0.3% EMS) was attempted to be applied. The treated seeds were sown in the first season and 4050 M1 plants were harvested. The harvested seeds were sown in the second season, and 78750 M2 plants were obtained. The selection was performed in second season (M2) based on morpho-physiological and yield traits; flag leaf area, flag leaf chlorophyll content, plant height, spike length, grain yield per plant and its components. Based on evaluated traits fourteen mutants were selected to be evaluated in the third generation (M3). The results indicated that the used mutagens had direct impact and significantly improved agronomic traits in derivative mutants compared to their parent cultivars. Moreover, the maximum increment in yield related traits were obtained by 0.4% EMS, 1 and 2 hour-laser, 350-Gy, 1.5 hour × 0.3% EMS and 250-Gy. The obtained results highlighted the importance of these doses of applied mutagens to induce useful genetic variability in bread wheat for improving grain yield and contributing traits.
APPROXIMATE POLYNOMIAL GCD

The computation of polynomial greatest common divisor (GCD) ranks among basic algebraic problems with many applications, for example, in image processing and control theory. The problem of the GCD computing of two exact polynomials is well deﬁned and can be solved symbolically, for example, by the oldest and commonly used Euclid’s algorithm. However, this is an ill-posed problem, particularly when some unknown noise is applied to the polynomial coeﬃcients. Hence, new methods for the GCD computation have been extensively studied in recent years. The aim is to overcome the ill-posed sensitivity of the GCD computation in the presence of noise. We show that this can be successively done through a TLS formulation of the solved problem, [1, 5, 7].
Impact of drug combination of clopidogrel and pantoprazole In the prognosis of patients with transient ischemic attack

The study aimed to investigate the impact of clopidogrel combined with proton pump inhibitors (PPI) pantoprazole treatment on the prognosis of patients with transient ischemic attack (TIA). A total of 478 cases of TIA patients treated with clopidogrel were randomly assigned half to clopidogrel combined with pantoprazole treatment and the control groups (clopidogrel treatment alone) from January 2012 to January 20l4. The platelet aggregation before and after treatment and cerebrovascular events incidence within 90 days were compared and analyzed. Multivariate analysis was used to estimate the incidence of cerebrovascular events within 90 days. The platelet aggregation rate before treatment was 73.2±6.1% in the treatment group, 74.1±8.8% in the control group. The platelet aggregation rate after treatment was 38.1±10.7% in the treatment group, 36.8±9.7% in the control group. The platelet aggregation before and after treatments between the two groups had not significant difference (P>0.05). The incidence of cerebrovascular events within 90 days (11.7% in the treatment group, 9.6% in the control group) between the two groups had not significant difference (P>0.05). Multivariate analysis showed that the incidence of cerebrovascular events within 90 day was associated with hypertension (P=0.008), diabetes (P=0.000), hyperlipidemia (P=0.002) and ABCD2 score >3 points (P=0.000). Clopidogrel combined with pantoprazole treatment had no significant effect on the prognosis of TIA patients.
Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Network

Chess game position analysis is important in improving ones game. It requires entry of moves into a chess engine which is, cumbersome and error prone. We present ARChessAnalyzer, a complete pipeline from live image capture of a physical chess game, to board and piece recognition, to move analysis and finally to Augmented Reality (AR) overlay of the chess diagram position and move on the physical board. ARChessAnalyzer is like a scene analyzer - it uses an ensemble of traditional image and vision techniques to segment the scene (ie the chess game) and uses Convolution Neural Networks (CNNs) to predict the segmented pieces and combine it together to analyze the game. This paper advances the state of the art in the first of its kind end to end integration of robust detection and segmentation of the board, chess piece detection using the fine-tuned AlexNet CNN and chess engine analyzer in a handheld device app. The accuracy of the entire chess position prediction pipeline is 93.45\% and takes 3-4.5sec from live capture to AR overlay. We also validated our hypothesis that ARChessAnalyzer, is faster at analysis than manual entry for all board positions for valid outcomes. Our hope is that the instantaneous feedback this app provides will help chess learners worldwide at all levels improve their game.
Reading In-Between the Lines: An Analysis of Dissenter

Efforts by content creators and social networks to enforce legal and policy-based norms, e.g. blocking hate speech and users, has driven the rise of unrestricted communication platforms. One such recent effort is Dissenter, a browser and web application that provides a conversational overlay for any web page. These conversations hide in plain sight -- users of Dissenter can see and participate in this conversation, whereas visitors using other browsers are oblivious to their existence. Further, the website and content owners have no power over the conversation as it resides in an overlay outside their control. In this work, we obtain a history of Dissenter comments, users, and the websites being discussed, from the initial release of Dissenter in Feb. 2019 through Apr. 2020 (14 months). Our corpus consists of approximately 1.68M comments made by 101k users commenting on 588k distinct URLs. We first analyze macro characteristics of the network, including the user-base, comment distribution, and growth. We then use toxicity dictionaries, Perspective API, and a Natural Language Processing model to understand the nature of the comments and measure the propensity of particular websites and content to elicit hateful and offensive Dissenter comments. Using curated rankings of media bias, we examine the conditional probability of hateful comments given left and right-leaning content. Finally, we study Dissenter as a social network, and identify a core group of users with high comment toxicity.
Should a small robot have a small personal space? Investigating personal spatial zones and proxemic behavior in human-robot interaction

This paper presents the first study in a series of proxemics experiments concerned with the role of personal spatial zones in human-robot interaction. In the study 40 participants approached a NAO robot positioned approximately at participants' eye level and entered different social zones around the robot (personal and intimate space). When the robot perceived the approaching person entering its personal space, it started gazing at the participant, and upon the intrusion of its intimate space it leaned back. Our research questions were: (1) given the small size of the robot (58 cm tall), will people expect its social zones to shrink by its size? (2) Will the robot behaviors be interpreted as appropriate social behaviors? We found that the average approach distance of the participants was 48 cm, which represents the inner limit of the human-size personal zone (45-120 cm), but is outside of the personal zone scaled to robot size (16-42 cm). This suggests that most participants did not (fully) scale down the extent of these zones to the robot size. We also found that the leaning back behavior of the robot was correctly interpreted by most participants as the robot's reaction to the intrusion of its personal space; however, our implementation of the behavior was often perceived as "unfriendly". We will discuss this and other limitations of the study in detail. Additionally we found positive correlations between participants' personality traits, Godspeed Questionnaire subscales, and the average approach distance. The technical contribution of this work is the real-time perception of 25 keypoints on the human body using a single compact RGB-D camera and the use of these points for accurate interpersonal distance estimation and as gazing targets for the robot.
The development of a bovine interspecies model for the analysis of genomic imprinting in normal and nuclear transfer derived fetuses

The Development of a Bovine Interspecies Model for the Analysis of Genomic Imprinting in Normal and Nuclear Transfer Derived Fetuses. (August 2003) Scott Victor Dindot, B.S., Texas A&M University Co-Chairs of Advisory Committee: Dr. Jorge Piedrahita Dr. James Derr The advent of somatic cell nuclear transfer in cattle has provided the opportunity for researchers to generate genetically identical animals as well as animals that possess precise genetic modifications for agriculture and biomedical purposes. However, in spite of the revolutionary impact this technology presents, problems remain which hinder the production of healthy animals on a consistent basis. Research on cloned mice implicates improper reprogramming of epigenetic modifications and genomic imprinting for the low pregnancy rates and high incidence of abnormalities that are manifested in cloned animals; however, a systematic and comprehensive analysis of nuclear reprogramming in cloned cattle remains undone. The purpose of this research is to assess and characterize the patterns of genomic imprinting in normal and nuclear transfer derived bovine fetuses. To facilitate the identification of imprinted genes in the bovine, a Bos gaurus/Bos taurus interspecies model has been incorporated to maximize the genetic heterozygosity that exists between the alleles of putative imprinted genes for allelic discrimination and parental inheritance.
Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications.
Sonification approaches in sports in the past decade: a literature review

The raise of novel mobile sensor and actuator technologies in the past decade enabled sonification of sport related data in real time for the athlete. We have systematically analysed scientific literature with respect to sports domains, feedback goals, user types, used sensor technology, and how the captured sensor data was sonified. We describe these aspects of 34 selected papers. Our analysis enables researchers to grasp prevalent approaches and to identify blind spots for new ways how sonification can not only aid athletes to improve their performance, but also to enhance the experience of various sports practitioners through sonification.
Shaping Our Common Digital Future

& THE GLOBAL SPREAD of a vicious disease in our interconnected world is threatening the health and livelihoods of millions of people. Beyond the immediate effects of the disease on individuals, families, and communities, we can anticipate the long-term impact on whole societies and economies. Our lives are changing not only because of the coronavirus pandemic, but also because of climate change and environmental damage. These are the defining crises of our time, and they are shining a harsh spotlight on the intractable socioeconomic inequalities long plaguing the world’s people. We cannot meet these challenges only on a local or national scale. Global crises require a global response.
An Upper-Level Biology Course Designed to Develop Science Communication in Stem Majors by Examining the Biotechnology Industry

Abstract Many undergraduate students pursuing life science majors are not aware of job options outside of medicine and academic research, because many departments stress these as the only primary career pathways. In addition, biology students often do not have many opportunities to develop their science communication and presentation skills due to the rigorous course requirements inherent in these fields that would make them more competitive for careers in biotechnology. We developed a course using diverse pedagogies designed to introduce students to new careers in biotechnology, to help them understand the role of ethics in the drug development pipeline, and to incorporate more communication assignments, such as student presentations and journal-club-style paper discussions to more effectively prepare them for many STEM-based career possibilities. By the end of the course, students had broader knowledge of previously unknown science careers, had improved their scientific communication skills, and reported a greater understanding of course material as a result of the science communication assignments.
First report on the occurrence of invasive black-chin tilapia Sarotherodon melanotheron (Ruppell, 1852) in Manila Bay and of Mayan cichlid Cichlasoma urophthalmus (Gunther, 1892) in the Philippines.

We report the presence of blackchin tilapia, Sarotherodon melanotheron, in the coastal waters of Manila Bay and in the river tributary of Hagonoy, Bulacan, suggesting its possible range expansion. The occurrence of the native Central American Mayan cichlid, Cichlosoma urophthalmus, is also putatively confirmed for the first time in Hagonoy, Bulacan. While morphological features, morphometric characters, and meristic counts of the current populations match those reported elsewhere for these species, further genetic work is required to confirm their identification and possibly provide information on their source. Negative impacts of the existence of the two species due to competitive interactions have already been anecdotally reported, however, no empirical scientific evidence is available to provide verification. Therefore, studies on the potential ecological impacts should be done, especially because the two species were already reported as notoriously invasive.
Transcriptome response comparison between vector and non-vector aphids after feeding on virus-infected wheat plants

Background Plant viruses maintain intricate interactions with their vector and non-vector insects and can impact the fitness of insects. However, the details of their molecular and cellular mechanisms have not been studied well. We compared the transcriptome-level responses in vector and non-vector aphids ( Schizaphis graminum and Rhopalosiphum padi , respectively) after feeding on wheat plants with viral infections ( Barley Yellow Dwarf Virus (BYDV) and Wheat dwarf virus (WDV), respectively). We conducted differentially expressed gene (DEG) annotation analyses and observed DEGs related to immune pathway, growth, development, and reproduction. And we conducted cloning and bioinformatic analyses of the key DEG involved in immune. Results For all differentially expressed gene analyses, the numbers of DEGs related to immune, growth, development, reproduction and cuticle were higher in vector aphids than in non-vector aphids. STAT5B (signal transducer and activator of transcription 5B), which is involved in the JAK-STAT pathway, was upregulated in R. padi exposed to WDV. The cloning and bioinformatic results indicated that the RpSTAT5B sequence contains a 2082 bp ORF encoding 693 amino acids. The protein molecular weight is 79.1 kD and pI is 8.13. Analysis indicated that RpSTAT5B is a non-transmembrane protein and a non-secreted protein. Homology and evolutionary analysis indicated that RpSTAT5B was closely related to R. maidis. Conclusions Unigene expression analysis showed that the total number of differentially expressed genes (DEGs) in the vector aphids was higher than that in the non-vector aphids. Functional enrichment analysis showed that the DEGs related to immunity, growth and reproduction in vector aphids were higher than those in non-vector aphids, and the differentially expressed genes related to immune were up-regulated. This study provides a basis for the evaluation of the response mechanisms of vector/non-vector insects to plant viruses.
On inexact alternating direction implicit iteration for continuous Sylvester equations

In this paper, we study the alternating direction implicit (ADI) iteration for solving the continuous Sylvester equation AX + XB = C, where the coefficient matrices A and B are assumed to be positive semi‐definite matrices (not necessarily Hermitian), and at least one of them to be positive definite. We first analyze the convergence of the ADI iteration for solving such a class of Sylvester equations, then derive an upper bound for the contraction factor of this ADI iteration. To reduce its computational complexity, we further propose an inexact variant of the ADI iteration, which employs some Krylov subspace methods as its inner iteration processes at each step of the outer ADI iteration. The convergence is also analyzed in detail. The numerical experiments are given to illustrate the effectiveness of both ADI and inexact ADI iterations.
A Heaviside Function Approximation for Neural Network Binary Classification

Neural network binary classifiers are often evaluated on metrics like accuracy and $F_1$-Score, which are based on confusion matrix values (True Positives, False Positives, False Negatives, and True Negatives). However, these classifiers are commonly trained with a different loss, e.g. log loss. While it is preferable to perform training on the same loss as the evaluation metric, this is difficult in the case of confusion matrix based metrics because set membership is a step function without a derivative useful for backpropagation. To address this challenge, we propose an approximation of the step function that adheres to the properties necessary for effective training of binary networks using confusion matrix based metrics. This approach allows for end-to-end training of binary deep neural classifiers via batch gradient descent. We demonstrate the flexibility of this approach in several applications with varying levels of class imbalance. We also demonstrate how the approximation allows balancing between precision and recall in the appropriate ratio for the task at hand.
Purinergic regulation of microglial mobility and viability during ischemic conditions in developing mouse brain tissues

Approved: _______________________________________________________ Thesis Supervisor _______________________________________________________ Title and Department ________________________________________________________ Date PURINERGIC REGULATION OF MICROGLIAL MOBILITY AND VIABILITY DURING ISCHEMIC CONDITIONS IN DEVELOPING MOUSE BRAIN TISSUES by Ukpong Bassey Eyo A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Biology in the Graduate College of The University of Iowa
Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction in China

Background Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction (AMI) after initial AMI remain poorly understood. Data on recurrent AMI in China is unknown. Methods Using the China Patient-centred Evaluative Assessment of Cardiac Events (PEACE)-Prospective AMI Study, we studied 3387 patients admitted to 53 hospitals for AMI and discharged alive. The association of recurrent AMI with 1-year mortality was evaluated using time-dependent Cox regression. Recurrent AMI events were classified as early (1–30 days), late (31–180 days), and very late (181–365 days). Their impacts on 1-year mortality were estimated by Kaplan-Meier methodology and compared by the log-rank test. Multivariable modelling was used to identify factors associated with recurrent AMI. Results The mean (SD) age was 60.7 (11.9) years and 783 (23.1%) were women. The observed 1-year recurrent AMI rate was 2.5% (95% CI 2.00 to 3.07) with 35.7% events occurring within the first 30 days. Recurrent AMI was associated with 1-year mortality with an adjusted HR of 25.42 (95% CI 15.27 to 42.34). Early recurrent AMI was associated with the highest 1-year mortality rate of 53.3% (log-rank p<0.001). Predictors of recurrent AMI included age 75–84, in-hospital percutaneous coronary intervention, heart rate >90 min/beats at initial admission, renal dysfunction, and not being prescribed any of guideline-based medications at discharge. Conclusions One-third of recurrent AMI events occurred early. Recurrent AMI is strongly associated with 1-year mortality, particularly if early. Heightened surveillance during this early period and improving prescription of recommended discharge medications may reduce recurrent AMI in China.