Miracle-dz/UniFAR-FTUs huggingface dataset:
Two Receptors, Two Isoforms, Two Cancers: Comprehensive Analysis of KIT and TrkA Expression in Neuroblastoma and Acute Myeloid Leukemia

Pediatric cancers represent a wide variety of different tumors, though they have unique features that distinguish them from adult cancers. Receptor tyrosine kinases KIT and TrkA functions in AML and NB, respectively, are well-characterized. Though expression of these receptors is found in both tumors, little is known about KIT function in NB and TrkA in AML. By combining gene enrichment analysis with multidimensional scaling we showed that pediatric AMLs with t(8;21) or inv16 and high KIT expression levels stand out from other AML subtypes as they share prominent transcriptomic features exclusively with KIT-overexpressing NBs. We showed that AML cell lines had a predominant expression of an alternative TrkAIII isoform, which reportedly has oncogenic features, while NB cell lines had dominating TrkAI-II isoforms. NB cells, on the other hand, had an abnormal ratio of KIT isoforms as opposed to AML cells. Both SCF and NGF exerted protective action against doxorubicin and cytarabine for t(8;21) AML and NB cells. We identified several gene sets both unique and common for pediatric AML and NB, and this expression is associated with KIT or TrkA levels. NMU, DUSP4, RET, SUSD5, NOS1, and GABRA5 genes are differentially expressed in NBs with high KIT expression and are associated with poor survival in NB. We identified HOXA10, BAG3, and MARCKS genes that are connected with TrkA expression and are marker genes of poor outcome in AML. We also report that SLC18A2, PLXNC1, and MRPL33 gene expression is associated with TrkA or KIT expression levels in both AML and NB, and these genes have a prognostic value for both cancers. Thus, we have provided a comprehensive characterization of TrkA and KIT expression along with the oncogenic signatures of these genes across two pediatric tumors.
Optimizing Neuromuscular Electrical Stimulation Pulse Width and Amplitude to Promote Central Activation in Individuals With Severe Spinal Cord Injury

Neuromuscular electrical stimulation (NMES) is one of the most effective treatments for counteracting the deleterious skeletal muscle adaptations that occur after spinal cord injury (SCI). Additionally, previous findings suggest that NMES can activate motor units via both peripheral and central mechanisms; however, this NMES-promoted central activation is not well understood. In this study, we aimed at investigating the effects of NMES on central activation in 10 individuals with motor complete SCI, focusing on understanding how to optimize NMES pulse width and amplitude for promoting central activation in this population. To this end, we used NMES to generate isometric contractions of the knee extensors and ankle plantarflexors while electromyographic (EMG) activity was recorded from the vastus lateralis and gastrocnemius medialis, respectively. We used EMG activity that persisted after the termination of NMES delivery (post-NMES) as a neurophysiological marker to assess central activation and explored differences in post-NMES EMG activity promoted by 500 and 1,000 μs pulse width NMES. Additionally, we explored the relationships between post-NMES EMG amplitude, torque output, and stimulation amplitude. Our results show that the higher pulse width (1,000 μs) demonstrated a greater effect on central activation as quantified by more frequent occurrences of post-NMES EMG activity (p = 0.002) and a 3.551 μV higher EMG amplitude (p = 0.003) when controlling for the torque output generated by 500 and 1,000 μs pulse width NMES. Importantly, we also found that the interplay among central activation, stimulation amplitude, and muscle torque output differs across SCI individuals, conceivably because of the individual-specific characteristics of the cord lesion and following plasticity of the spinal circuitry. These results suggest that NMES can be optimized to promote central activation, which may lead to novel opportunities for motor function recovery after SCI.
Profiles of Childhood Trauma in Women With Substance Use Disorders and Comorbid Posttraumatic Stress Disorders

Background: It is increasingly becoming accepted that substance use disorders, including substance abuse and substance dependence, are closely related to childhood trauma and posttraumatic stress disorders. Among women with substance use disorders, the majority report sexual, physical or emotional abuse, or neglect. However, it is poorly understood which types of childhood trauma co-occur in women with substance use disorders and how combinations of different types and severities of childhood trauma are related to clinical characteristics. This information is important to inform treatment of substance use disorders. Aim: The first aim of this research was to investigate profiles of childhood trauma in female patients with substance use disorders and posttraumatic stress disorders. The second aim was to examine relationships between these childhood trauma profiles and addiction characteristics or current clinical symptoms. Methods: We includeda 343 treatment-seeking women with substance use disorders and comorbid posttraumatic stress disorders according to DSM-IV. Five types of childhood trauma (sexual abuse, physical abuse, emotional abuse) were measured using the Childhood Trauma Questionnaire. Addiction characteristics were assessed by using the Addiction Severity Index-lite. Current severity of clinical symptoms was determined by the Symptom-Checklist-27. Latent profile analysis was conducted to distinguish profiles of childhood trauma. Analysis of variance was applied to examine the relationship between childhood trauma profiles and addiction characteristics or severity of clinical symptoms. Results: Nine out of ten women reported at least one type of childhood abuse or neglect. Four different childhood trauma profiles could be distinguished that characterized different types and severities of childhood trauma: ‘Low trauma’; ‘Moderate sexual abuse and emotional abuse’; ‘Severe sexual abuse and emotional abuse’; and ‘Severe levels of all types of trauma’. Profiles with more severe levels of childhood trauma showed an earlier age at initiation and escalation of substance use. Furthermore, childhood trauma profiles were related to current severity of depressive symptoms, dysthymic symptoms, sociophobic symptoms, and distrust. Conclusion: In women with substance use disorders and posttraumatic stress disorders, childhood trauma profiles can inform about addiction characteristics and severity of a wide range of clinical symptoms. This information is essential to understand current treatment needs and should be systematically assessed in women with substance use disorders and trauma exposure.
Editorial: Reactive Oxygen Species (ROS) Detection Methods in Biological System

Metabolic processes such as respiration and oxidative stress are associated with the formation of reactive oxygen species (ROS). Any kind of fluctuation in environmental stress factors either biotic or abiotic is known to be linked with an increased level of ROS among which superoxide radical anion (O2 •−); hydrogen peroxide (H2O2), hydroxyl radical (HO •), nitric oxide (NO), peroxynitrite (ONOO−), and singlet oxygen (O2) have been widely studied (Kohno et al., 2008; Tada et al., 2009; Ichiishi et al., 2015; Zhang et al., 2018; Pospíšil et al., 2019). The ROS bear huge impact influencing the different cellular functions due to its involvement in the regulation of several cellular processes within a living system. However, because of its high reactivity, it is at the same time, harmful to the cell and is well-known to be associated with several biological dysfunction and diseases. Due to short life time and high reactivity, the detection of ROS in the biological system has always been very challenging. Different detection techniques andmethodologies have been applied for quantification and localization of ROS and their reactive intermediates during the past decades both in in vitro and in vivo systems (Kohno et al., 2008; Tada et al., 2009; Ichiishi et al., 2015; Mattila et al., 2015). The detection is mainly based on:
Production of Defective Interfering Particles of Influenza A Virus in Parallel Continuous Cultures at Two Residence Times—Insights From qPCR Measurements and Viral Dynamics Modeling

Defective interfering particles (DIPs) are a natural byproduct of influenza A virus (IAV) replication. DIPs interfere with the propagation and spread of infectious standard virus (STV), reduce virus yields by competing for viral and cellular resources, and induce antiviral responses. These properties open exciting possibilities for the development of DIP-based antivirals. Exploring options for cell culture-based DIP production, we have established a fully continuous cultivation process, where one bioreactor is used to grow cells that are fed to two bioreactors operated in parallel for virus production. This system allows head-to-head comparisons of STV and DIP replication dynamics over extended time periods. Cultivations were performed at two residence times (RT, 22 and 36 h) using MDCK suspension cells grown in a fully defined medium. For infection, we used a virus seed generated by reverse genetics containing STVs and a known DIP carrying a deletion in segment 1 (delS1(1)). Four days post infection, DIPs achieved maximum concentrations of 7.0·109 virions/mL and 8.4·109 virions/mL for RTs of 22 and 36 h, respectively. Furthermore, oscillations in virus titers with two to three maxima were found for DIP accumulation at 36 and 22 h RT, respectively. To complement the study, a basic mathematical model using simple kinetics and a reasonable number of parameters to describe DIP-propagation in continuous cultures was established. Upon fitting the model individually to each of the two data sets, oscillations in the viral dynamics and the cell population dynamics were described well. Modeling suggests that both STV inactivation and virus degradation have to be taken into account to achieve good agreement of simulations and experimental data for longer RTs. Together, the high DIP titers obtained, and the successful simulation of the experimental data showed that the combination of continuous bioreactors and mathematical models can enable studies regarding DIP dynamics over extended time periods and allow large scale manufacturing of DIP-based antivirals.
Improved Wheat Growth and Yield by Delayed Leaf Senescence Using Developmentally Regulated Expression of a Cytokinin Biosynthesis Gene

Delaying leaf senescence in plants, especially under water stress conditions, can help to maintain the remobilization of stored nutrients in source–sink relationships, thus leading to improved crop yields. Leaf senescence can be delayed by plant hormones such as cytokinin. Here, the Isopentenyl transferase (IPT) gene, encoding a cytokinin biosynthesis enzyme, driven by a modified AtMYB32xs promoter was transformed into wheat. Transgenic wheat plants exhibited delayed leaf senescence, retaining chlorophyll for longer under controlled environment conditions. Selected independent transgenic events and their corresponding nulls were grown under field conditions for two consecutive years under well-watered and water stress treatments using automated rainout shelters. Three independent transgenic events had improved canopy green cover, lower canopy temperatures, and higher leaf water potential than their respective non-transgenic nulls, with no abnormality in morphology and phenology. Increased grain yield was observed in transgenic events under both water treatments, with the yield increase more pronounced under water stress (26–42%). These results have shown that delayed leaf senescence using the chimeric transgene AtMYB32xs-p::IPT can be a useful strategy to achieve grain yield gains in wheat and potentially other crops for sustainable food production.
Abiotic Changes Driving Microphytoplankton Functional Diversity in Admiralty Bay, King George Island (Antarctica)

Environmental gradients can provide habitat-specific scenarios for community functional diversity that determine the composition of populations on both spatial and temporal scales. The western shelf of the Antarctic Peninsula has experiencing increasing air temperatures while the climate is transitioning to a warm-humid sub-Antarctic-type of climate. As a consequence, abiotic changes are leading to alterations in the trophic web. Microphytoplankton functional diversity was analyzed across environmental gradients of sea surface temperature, salinity, meltwater percentage and nutrient availability in Admiralty Bay, South Shetland Islands, Western Antarctic Peninsula. Samples were collected during the austral summer from 2009 to 2011 and from 2013 to 2015, at Admiralty Bay for which functional diversity indices were calculated based on species traits. The amount of meltwater (MW) present in Admiralty Bay groups microphytoplankton into communities according to physiological and ecological tolerances, thus leading to a greater functional diversity. When meltwater dominated the bay (> 2.25% MW scenarios iii - 2013-14 and iv - 2014-15), diatoms and dinoflagellates were codominant. An increase in the dinoflagellate fraction of microphytoplankton can be considered a trigger for changes in the structure of the Antarctic food web. Our results suggest using Admiralty Bay as a model for studies on changes in microphytoplankton community composition and functional diversity.
Enhancing PLP-Binding Capacity of Class-III ω-Transaminase by Single Residue Substitution

Transaminases are pyridoxal-5′-phosphate (PLP) binding enzymes, broadly studied for their potential industrial application. Their affinity for PLP has been related to their performance and operational stability and while significant differences in PLP requirements have been reported, the environment of the PLP-binding pocket is highly conserved. In this study, thorough analysis of the residue interaction network of three homologous transaminases Halomonas elongata (HeTA), Chromobacterium violaceum (CvTA), and Pseudomonas fluorescens (PfTA) revealed a single residue difference in their PLP binding pocket: an asparagine at position 120 in HeTA. N120 is suitably positioned to interact with an aspartic acid known to protonate the PLP pyridinium nitrogen, while the equivalent position is occupied by a valine in the other two enzymes. Three different mutants were constructed (HeTA-N120V, CvTA-V124N, and PfTA-V129N) and functionally analyzed. Notably, in HeTA and CvTA, the asparagine variants, consistently exhibited a higher thermal stability and a significant decrease in the dissociation constant (Kd) for PLP, confirming the important role of N120 in PLP binding. Moreover, the reaction intermediate pyridoxamine-5′-phosphate (PMP) was released more slowly into the bulk, indicating that the mutation also enhances their PMP binding capacity. The crystal structure of PfTA, elucidated in this work, revealed a tetrameric arrangement with the PLP binding sites near the subunit interface. In this case, the V129N mutation had a negligible effect on PLP-binding, but it reduced its temperature stability possibly destabilizing the quaternary structure.
Effective Frequent Motif Discovery for Long Time Series Classification: A Study using Phonocardiogram

Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved A mechanism for extracting frequent motifs from long time series is proposed, directed at classifying phono-cardiograms. The approach features two preprocessing techniques: silent gap removal and a novel candidate frequent motif discovery mechanism founded on the clustering of time series subsequences. These techniques were combined into one process for extracting discriminative frequent motifs from single time series and then to combine these to identify a global set of discriminative frequent motifs. The proposed approach compares favourably with these existing approaches in terms of accuracy and has a significantly improved runtime.
Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed. CCS CONCEPTS • Computing methodologies → Natural language processing.
Proactive Phishing Sites Detection

Phishing is one of the social engineering techniques to steal users’ sensitive information by disguising a fake Web site as a trustworthy one. Previous research proposed phishing mitigation techniques, such as blacklist, heuristics, visual similarity, and machine learning. However, these kinds of methods have limitation on the detection of a zero-hour phishing site, a phishing site that no one has noticed yet. This paper presents a new approach to the detection of zero-hour phishing sites: proactive detection. If those malicious sites are detected as early as possible, shutdown by the specialized agencies and mitigation of user damages are expected. We also present a method and system of efficient phishing site detection based on the proactive approach. The method is composed of two major parts: suspicious domain names generation and judgment. The former predicts likely phishing Web sites from the given legitimate brand domain name. The latter scores and judges suspects by calculating various indexes. That is, zero-hour phishing sites can be detected by hypothesis and test cycles. As a result of the preliminary experiment, we detected several zero-hour phishing sites disguising as major brands, including eBay, Google, and Amazon. CCS CONCEPTS • Security and privacy $\rightarrow$ Phishing; Social network security and privacy.
Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network

Recent advancements in computer-assisted learning systems have caused an increase in the research of knowledge tracing, wherein student performance on coursework exercises is predicted over time. From the viewpoint of data structure, the coursework can be potentially structured as a graph. Incorporating this graph-structured nature into the knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of the graph neural network (GNN), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information.CCS CONCEPTS • Computing methodologies → Neural networks; • Information systems → Data mining; • Applied computing → Learning management systems.
User Profiling : Application to Cybersecurity

In recent years, we witnessed a rapid growth of social media networking and micro-blogging sites such as Twitter. In these sites, users provide a variety of data such as their personal data, interests, and opinions. However, this data shared is not always true. Often, social media users hide behind a fake profile and may use it to spread rumors or threaten others. To address that, different methods and techniques were proposed for user profiling. In this article, we use machine learning for user profiling in order to predict the age and gender of a user’s profile and we assess whether it is a dangerous profile using the users’ tweets and features. Our approach uses several stylistic features such as characters based, words based and syntax based. Moreover, the topics of interest of a user are included in the profiling task. We obtained the best accuracy levels with SVM and these were respectively 73.49% for age, 83.7% for gender, and 88.7% for the dangerous profile detection.
Topological Approach for Finding Nearest Neighbor Sequence in Time Series

The aim of this work is to obtain a good quality approximation of the nearest neighbor distance (nnd) profile among sequences of a time series. The knowledge of the nearest neighbor distance of all the sequences provides useful information regarding, for example, anomalies and clusters of a time series, however the complexity of this task grows quadratically with the number of sequences, thus limiting its possible application. We propose here an approximate method which allows one to obtain good quality nnd profiles faster (1-2 orders of magnitude) than the brute force approach and which exploits the interdependence of three different topologies of a time series, one induced by the SAX clustering procedure, one induced by the position in time of each sequence and one by the Euclidean distance. The quality of the approximation has been evaluated with real life time series, where more than 98% of the nnd values obtained with our approach are exact and the average relative error for the approximated ones is usually below 10%.
The Problem of Finding the Best Strategy for Progress Computation in Adaptive Web Surveys

Web surveys are typical web information systems. As part of the interface of a survey, progress indicators inform the participants about their state of completion. Different studies suggest that the progress indicator in web surveys has an impact on the dropout and answer behaviour of the participants. Therefore, researchers should invest some time in finding the right indicator for their surveys. But the calculation of the progress is sometimes more difficult than expected, especially, in surveys with a lot of branches. Current related work explains the progress computation in such cases based on different prediction strategies. However, the performance of these strategies varies for different surveys. In this position paper, we explain how to compare those strategies. The chosen Root Mean Square Error measure allows to select the best strategy. But experiments with two large real-world surveys show that there is no single best strategy for all of them. It highly depends on the structure of the surveys and sometimes even the best known strategy produces bad predictions. Dedicated research should find solutions for these cases.