Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalization of ventricular cardiac conduction system models to reproduce patient electrocardiogram

Cardiac electrical activation is a fundamental mechanism that controls cardiac function. It is key for a computational model to be able to reproduce the patient ECG and the underlying sequence of activation of the ventricles. In this study, we propose to personalize the activation sequence by estimating a compatible cardiac conduction system (CCS) for a given patient from his electroanatom-ical map. We perform the study on five subjects in sinus rhythm from which we estimated the CCS and performed forward simulations to obtain the virtual ECG. Errors at the endocardium were in average 5.72ms, and correlation between clinical and simulated ECG ranged between 0.75 and 0.90. We concluded that the methodology is feasible to personalize the sequence of activation with the advantage of providing an underlying structure for the CCS.
Phytopathological Threats Associated with Quinoa (Chenopodium quinoa Willd.) Cultivation and Seed Production in an Area of Central Italy

In 2017, in a new Chenopodium quinoa cultivation area (Central Italy), emergence failures of the Titicaca, Rio Bamba, and Real varieties, whose seeds were obtained the previous year (2016) in the same location, were observed. Moreover, leaf disease symptoms on the Regalona variety, whose seeds came from Chile, were detected. Visual and microscopic analyses showed the presence of browning/necrotic symptoms on the seeds of the three varieties whose emergence in the field had failed. In addition, their in vitro germination rates were strongly compromised. Fusarium spp. was isolated with high incidence from Titicaca, Rio Bamba, and Real seeds. Among the detected Fusarium species, in the phylogenetic analysis, the dominant one clustered in the sub-clade Equiseti of the Fusarium incarnatum-equiseti (FIESC) species complex. Instead, the pathogen associated with Regalona leaf symptoms was identified, by morphological and molecular features, as Peronospora variabilis, the causal agents of downy mildew. This is the first report of both P. variabilis and F. equiseti on C. quinoa in Italy. Species-specific primers also detected P. variabilis in Regalona seeds. These results underline the importance of pathogen monitoring in new quinoa distribution areas, as well as of healthy seed production and import for successful cultivation.
Investigation of Job Satisfaction Dimensions of Health Care Knowledge Workers: Factor Analysis-Multivariate Approach

The investigation of the job satisfaction dimensions is crucial before further analysis such as testing the research hypotheses. The widely used approach includes Exploratory Factor Analysis with principal components analysis, unweighted least squares, generalized least squares, maximum likelihood, principal axis factoring, alpha factoring, and image factoring. The study focuses on investigation of the underlying dimensions of the job satisfaction of the knowledge workers in healthcare using multiple methods. The target sample of the research is the health care personnel including nurses, administrators, and doctors as the knowledge workers. Toward this end, a theory-based classical approaches are utilized. Healthcare employee’s job satisfaction is an important in the successful performance of institutions. A rich literature of academic studies has showed that personnel’s satisfaction is critical for the successful performance of organizations. The dimensions healthcare workers’ satisfaction as the knowledge workers were extracted by employing: (i) Exploratory Factor Analysis method, (ii) Confirmatory Factor Analysis. The sample of this study were collected via a survey from health care personnel as knowledge workers. There are 249 observations collected from healthcare workers. A set of 34 items are utilized to investigate the job satisfaction of the healthcare knowledge workers. The results of the factor analysis indicated that six underlying crucial factors are extracted: attitude of management, organizational support, job security, reward and pay, working conditions, attitude of colleagues.
Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach

Globally, heart disease has been the leading cause of death for more than two decades. There is a need to develop intelligent architectures to handle a variety of real life clinical scenarios when a 12-lead ECG is not a viable option. We propose a method using wide and deep CNN architectures to classify cardiac abnormalities from 12, 6, 4, 3, and 2 leads ECGs. These five networks were created for the PhysioNet/CinC Challenge 2021, by the Biomedic2ai team. ECG signals were down-sampled to 100Hz and partitioned with 5-second windows using a sliding 4-second overlap. A one-dimensional deep CNN (1D-dCNN) module was used to preserve sequentially related features embedded in the signals. A feature extraction module was added to the 1D-dCNN, creating a ‘wide and deep modular network’. This framework allows the addition or removal of modules to optimize classification models. We achieved test scores of 0.36, 0.30, 0.31, 0.29, and 0.34 (ranked 23rd, 26th, 26th, 27th, and 22nd out of 39 officially ranked teams) for 12,6,4,3, and 2 leads, respectively, on the hidden test set provided by the challenge. Our model demonstrates potential with the wide modular network. The framework also provides the flexibility to integrate clinical knowledge in the future modules to improve the overall classification performance.
Functional Differences in the Blooming Phytoplankton <named-content content-type='genus-species'>Heterosigma akashiwo</named-content> and <named-content content-type='genus-species'>Prorocentrum donghaiense</named-content> Revealed by Comparative Metaproteomics

Phytoplankton blooms are natural phenomena in the ocean, which are the results of rapid cell growth of some phytoplankton species in a unique environment. However, little is known about the molecular events occurring during the bloom. Here, we compared metaproteomes of two phytoplankton Heterosigma akashiwo and Prorocentrum donghaiense in the coastal East China Sea. H. akashiwo and P. donghaiense accounted for 7.82% and 4.74% of the phytoplankton community protein abundances in the nonbloom sample, whereas they contributed to 60.13% and 78.09%, respectively, in their individual blooming samples. Compared with P. donghaiense, H. akashiwo possessed a significantly higher abundance of light-harvesting complex proteins, carbonic anhydrasem and RuBisCO. The blooming H. akashiwo cells expressed more proteins related to external nutrient acquisition, such as bicarbonate transporter SLC4, ammonium transporter, nitrite transporter, and alkaline phosphatase, while the blooming P. donghaiense cells highly expressed proteins related to extraand intracellular organic nutrient utilization, such as amino acid transporter, 5=-nucleotidase, acid phosphatase, and tripeptidyl-peptidase. The strong capabilities of light harvesting, as well as acquisition and assimilation of inorganic carbon, nitrogen, and phosphorus, facilitated the formation of the H. akashiwo bloom under the high turbidity and inorganic nutrient-sufficient condition, whereas the competitive advantages in organic nutrient acquisition and reallocation guaranteed the occurrence of the P. donghaiense bloom under the inorganic nutrient-insufficient condition. This study highlights the power of metaproteomics for revealing the underlying molecular behaviors of different coexisting phytoplankton species and advances our knowledge on the formation of phytoplankton blooms. IMPORTANCE A deep understanding of the mechanisms driving bloom formation is a prerequisite for effective bloom management. Metaproteomics was applied in this study to reveal the adaptive and responsive strategies of two coexisting phytoplankton species, H. akashiwo and P. donghaiense, during their bloom periods. Metabolic features and niche divergence in light harvesting, as well as carbon, nitrogen, and phosphorus acquisition and assimilation likely promoted the bloom occurrence under different environments. The molecular behaviors of coexisting bloom-causing species will give clues for bloom monitoring and management in the oceans.
MATH-SHU 236 t-Stochastic Neighboring Embedding

PCA is a linear technique, which is to find a linear subspace which captures the maximal directions of covariance. In other words, this method will keep the dissimilar points (with high distance) far apart in the low dimensional representation. For high dimensional data, the intrinsic dimension is usually lower than the ambient dimension d, i.e., they may lie near a low dim object such as hypersurface or manifold. For example, a curve in R has its intrinsic dimension equal to 1. We hope that the low dimensional representations of similar points are still close to one another. Therefore, sometimes we prefer nonlinear dimension reduction techniques such as Laplacian eigenmaps and diffusion maps.
Unimapper: An Online Interactive Analyzer/Visualizer of Optical Mapping Experimental Data

Time series of spatially-extended two-dimensional recordings are the cornerstone of basic and clinical cardiac electrophysiology. The data source may be either multipolar catheters, multi-electrode arrays, optical mapping with the help of voltage and calcium-sensitive fluorescent dyes, or the output of simulation studies. The resulting data cubes (usually two spatial and one temporal dimension) are shared either as movie files or, after additional processing, various graphs and tables. However, such data products can only convey a limited view of the data. It will be beneficial if the data consumers can interactively process the data, explore different processing options and change its visualization. This paper presents the Unified Electrophysiology Mapping Framework (Unimapper) to facilitate the exchange of electrophysiology data. Its pedigree includes a Java-based optical mapping application. The core of Unimapper is a website and a collection of JavaScript utility functions for data import and visualization (including multi-channel visualization for simultaneous voltage/calcium mapping), basic image processing (e.g., smoothing), basic signal processing (e.g., signal detrending), and advanced processing (e.g., phase calculation using the Hilbert transform). Additionally, Unimapper can optionally use graphics processing units (GPUs) for computationally intensive operations. The Unimapper ecosystem also includes utility libraries for commonly used scientific programming languages (MATLAB, Python, and Julia) that allow the data producers to convert images and recorded signals into a standard format readable by Unimapper. Unimapper can act as a nexus to share electrophysiology data - whether recorded experimentally, clinically or generated by simulation - and enhance communication and collaboration among researchers.
Igor: Crash Deduplication Through Root-Cause Clustering

Fuzzing has emerged as the most effective bug-finding technique. The output of a fuzzer is a set of proof-of-concept (PoC) test cases for all observed "unique'' crashes. It costs developers substantial efforts to analyze each crashing test case. This, mostly manual, process has lead to the number of reported crashes out-pacing the number of bug fixes. Automatic crash deduplication techniques, which mostly rely on coverage profiles and stack hashes, are supposed to alleviate these pressures. However, these techniques both inflate actual bug counts and falsely conflate unrelated bugs. This hinders, rather than helps, developers, and calls for more accurate techniques. The highly-stochastic nature of fuzzing means that PoCs commonly exercise many program behaviors that are orthogonal to the crash's underlying root cause. This diversity in program behaviors manifests as a diversity in crashes, contributing to bug-count inflation and conflation. Based on this insight, we develop Igor, an automated dual-phase crash deduplication technique. By minimizing each PoC's execution trace, we obtain pruned test cases that exercise the critical behavior necessary for triggering a bug. Then, we use a graph similarity comparison to cluster crashes based on the control-flow graph of the minimized execution traces, with each cluster mapping back to a single, unique root cause. We evaluate Igor against 39 bugs resulting from 254,000 PoCs, distributed over 10 programs. Our results show that Igor accurately groups these crashes into 48 uniquely identifiable clusters, while other state-of-the-art methods yield bug counts at least one order of magnitude larger.
School graduation finals are dead - long live the students e-portfolio

The concept of e-portfolios is to reflect an avenue into the past, present, and future of a person; displaying his or her lifetime education and devotion to further development, represents a valuable tool in aiding students’ not only in their academic growth, but mainly in their transition into higher education levels or even new careers. In the last few years, especially after the COVID-19 pandemic, the use of e-portfolios has shown an unexpected rise on university level, in its use to meet new developed needs. This work gives a brief definition of e-portfolios, how faculty members and students gain from them, and how to select the correct e-portfolio platform to support traditional school graduation final exams and university enrolment systems
Hypogonadotropic Hypogonadism in a Female Patient with Thalassemia Major

Thalassemia refers to a group of inherited diseases characterized by decreased or absent synthesis of normal globin chains. The direct consequence is an imbalance of the alpha and beta globin chain synthesis that results in anemia from ineffective erythropoiesis and hemolysis. The term thalassemia major refers to the severe form that is often associated with life-long transfusion dependent anemia. Hypogonadism is the most frequently reported endocrine complication, affecting 70-80% of thalassemia major patients. Hypogonadism is likely to be caused by hypertransfusion therapy resulting in iron deposits in the gonads, pituitary gland or both. However, hypogonadotropic hypogonadism resulting from iron deposition in the pituitary gonadotrope is more commonly found. Gonadal iron deposition in ovaries or testes occurs less frequently, as the majority of amenorrheic women can still ovulate after hormonal treatment. Despite recent advances in iron chelation therapy, excess iron overload in pituitary gonadotropic cells remains one of the major problems in thalassemic patients. Hypogonadism, mostly hypogonadotropic hypogonadism, is usually detected during puberty. Early diagnosis and treatment are crucial for normal pubertal development and to reduce the complications of hypogonadism. The risks and benefits of hormonal replacement therapy, especially regarding the thromboembolic event, remain a challenge for providers caring for thalassemic patients. We hereby present a case of 15-year-old unmarried girl with thalassemia major presenting with primary amenorrhea and poorly developed secondary sexual characteristics. A thorough history, clinical examination, laboratory and radiological investigations were conducted. These tests confirmed the diagnosis of hypogonadotropic hypogonadism. Patient was started on hormone replacement therapy. She is on regular follow-up and compliant with her treatment.
Mobile Perfusion Assessment Tool as Alternative to LDI

Blood perfusion can be used as an indicator to assess the healing of a chronic wound. The current gold standard, LDI, is expensive and relatively inaccessible. In this paper we present an integrated software-and-hardware solution that is able to classify areas of the patient’s hand as having low or high perfusion, using a standard phone video and infrared image as inputs. Our solution is able to produce accurate classifications for a two-class system distinguishing between high and medium perfusion from low perfusion however it provides poor classifications when attempting to classify a three-class system of low, medium, and high perfusion levels. We conclude that such a software solution might replace LDI in the foreseeable future but requires further testing and tuning for now.
Variation in Linguistic Complexity and its Cognitive Underpinning

Linguistic complexity – manifested in terms of hierarchical recursive structures generated by grammar – is often discussed from the perspective of cross-linguistic comparison (cf. Everett, 2005; Nevins, Pesetsky, & Rodrigues, 2009 on Pirahã). In this paper, we focus instead on the variation in complexity within a single language, English, and on the lower bound of complexity, specifically (cf. Futrell et al., 2016). We report results of two studies, a corpus study (Study 1) and a production experiment (Study 2), that investigate syntactic complexity of expressions that arise in the context of human-computer interaction and compare them to the standard language. The results of both studies show that the expressions generated in the context of human-computer interaction exhibit lesser structural complexity and often violate the norm of the language (cf. margaret mead culture famous research). Our results suggest that such expressions are generated by a qualitatively different type of formal grammar, Linear Grammar (Jackendoff & Wittenberg, 2017), rather than by recursive grammar (Roeper, 1999).
Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way.
Gender Differences in Student Perception of The Role of Learner-Content Interactions On Their Engagement In Online Courses

Online education continues to rise. With the outbreak of the global pandemic, various institutions have opted to put their courses online so that faculty members, students and staff members could be safe (Hodges et al, 2020; Zhou et al, 2020). With the high number of online learners, instructors and course designers may need to establish ways of engaging their students in an online course. Numerous practices have been suggested for engaging learners in an online learning environment. These strategies proposed center around how learner-content interactions, learner-technology interactions and learner-learner interactions can be improved to engage online students (Chakraborty & Nafukho, 2014). However, very few studies have investigated if there are gender differences in student views of the role of learner-content interactions on their engagement in an online course. This study focuses on learner-content interactions and how it plays a role in learner engagement. The learners rated how the following aspects of course content impacts their engagement in an online learning environment; personal interests in the subject matter, narrated lecture videos provided as a class resource, interactive assignments presented in the class, critical thinking assignments presented in the class and problem-solving assignments presented in the class. Researchers examined if male and female perceptions of the role of learner-content interactions on their engagement in an online learning environment was different.
A Fast Algorithm for Facilitating Heartbeat Annotation in Long-Term ECG Signals

Machine-learning models for automatic interpretation of ECG signals require properly labeled datasets. However, manual annotation is arduous and time-consuming, particularly in long-term recordings. We propose a fast semiautomatic algorithm for facilitating long-term ECG analysis and annotation, free from fatigue-caused errors or exorbitantly long computations. Heartbeats are compressed to short strings using Symbolic Aggregation approXimation (SAX). Every unique string becomes a pre-cluster of all the beats represented by this string, dramatically reducing the amount of memory and computations required. Next, hierarchical clustering of signal-averaged beats of every pre-cluster is performed. Clusters can then be conveniently analyzed and annotated by the researcher. Our algorithm shows a precision and Fowlkes-Mallows index of 0.91 for both metrics on the Physionet's MIT-BIH database, and runs, on average, in less than one hour on a modern PC for three-day-long ECGs. The proposed algorithm is accurate and efficient enough to facilitate practical long-term ECG signal beat analysis and annotation.