Miracle-dz/UniFAR-FTUs huggingface dataset:
Thyroid Hormone Receptor Alpha Is Required for Thyroid Hormone-Dependent Neural Cell Proliferation During Tadpole Metamorphosis

Thyroid hormone (T3) plays several key roles in development of the nervous system in vertebrates, controlling diverse processes such as neurogenesis, cell migration, apoptosis, differentiation, and maturation. In anuran amphibians, the hormone exerts its actions on the tadpole brain during metamorphosis, a developmental period dependent on T3. Thyroid hormone regulates gene transcription by binding to two nuclear receptors, TRα and TRβ. Our previous findings using pharmacological and other approaches supported that TRα plays a pivotal role in mediating T3 actions on neural cell proliferation in Xenopus tadpole brain. Here we used Xenopus tropicalis (X. tropicalis) tadpoles with an inactivating mutation in the gene that encodes TRα to investigate roles for TRα in mitosis and gene regulation in tadpole brain. Gross morphological analysis showed that mutant tadpoles had proportionally smaller brains, corrected for body size, compared with wildtype, both during prometamorphosis and at the completion of metamorphosis. This was reflected in a large reduction in phosphorylated histone 3 (pH3; a mitosis marker) immunoreactive (ir) nuclei in prometamorphic tadpole brain, when T3-dependent cell proliferation is maximal. Treatment of wild type premetamorphic tadpoles with T3 for 48 h induced gross morphological changes in the brain, and strongly increased pH3-ir, but had no effect in mutant tadpoles. Thyroid hormone induction of the direct TR target genes thrb, klf9, and thibz was dysregulated in mutant tadpoles. Analysis of gene expression by RNA sequencing in the brain of premetamorphic tadpoles treated with or without T3 for 16 h showed that the TRα accounts for 95% of the gene regulation responses to T3.
Orchard Conditions and Fruiting Body Characteristics Drive the Microbiome of the Black Truffle Tuber aestivum

Truffle fungi are well known for their enticing aromas partially emitted by microbes colonizing truffle fruiting bodies. The identity and diversity of these microbes remain poorly investigated, because few studies have determined truffle-associated bacterial communities while considering only a small number of fruiting bodies. Hence, the factors driving the assembly of truffle microbiomes are yet to be elucidated. Here we investigated the bacterial community structure of more than 50 fruiting bodies of the black truffle Tuber aestivum in one French and one Swiss orchard using 16S rRNA gene amplicon high-throughput sequencing. Bacterial communities from truffles collected in both orchards shared their main dominant taxa: while 60% of fruiting bodies were dominated by α-Proteobacteria, in some cases the β-Proteobacteria or the Sphingobacteriia classes were the most abundant, suggesting that specific factors (i.e., truffle maturation and soil properties) shape differently truffle-associated microbiomes. We further attempted to assess the influence in truffle microbiome variation of factors related to collection season, truffle mating type, degree of maturation, and location within the truffle orchards. These factors had differential effects between the two truffle orchards, with season being the strongest predictor of community variation in the French orchard, and spatial location in the Swiss one. Surprisingly, genotype and fruiting body maturation did not have a significant effect on microbial community composition. In summary, our results show, regardless of the geographical location considered, the existence of heterogeneous bacterial communities within T. aestivum fruiting bodies that are dominated by three bacterial classes. They also indicate that factors shaping microbial communities within truffle fruiting bodies differ across local conditions.
Expert Systems Usage and Knowledge Acquisition: An Empirical Assessment of Analogical Reasoning in the Evaluation of Internal Controls

This article reports the results of a laboratory experiment that investigates the effectiveness of expert systems' explanatory capabilities in transferring knowledge to novice auditors. Three types of expert systems were developed and knowledge transfer was assessed through three outcome measures: accuracy, mean absolute error (MAE), and confidence. Findings indicated that expert system type impacted on the outcome measures. In addition, overconfidence was found to be significantly different for the expert system incorporating analogical reasoning techniques than for the other treatment conditions. The no-explanation group had the highest level of overconfidence, followed by the explanation group. The findings suggest that an expert system designed to incorporate analogical learning techniques along with the explanation can enhance knowledge transfer compared to a system that just provides explanations.
Automatic Classification of Rhetorical Roles for Sentences: Comparing Rule-Based Scripts with Machine Learning

Automatically mining patterns of reasoning from evidenceintensive legal decisions can make legal services more efficient, and it can increase the public’s access to justice, through a range of use cases (including semantic viewers, semantic search, decision summarizers, argument recommenders, and reasoning monitors). Important to these use cases is the task of automatically classifying those sentences that state whether the conditions of applicable legal rules have been satisfied or not in a particular legal case. However, insufficient quantities of gold-standard semantic data, and the high cost of generating such data, threaten to undermine the development of such automatic classifiers. This paper tests two hypotheses: whether distinctive phrasing enables the development of automatic classifiers on the basis of a small sample of labeled decisions, with adequate results for some important use cases, and whether semantic attribution theory provides a general methodology for developing such classifiers. The paper reports promising results from using a qualitative methodology to analyze a small sample of classified sentences (N = 530) to develop rulebased scripts that can classify sentences that state findings of fact (“Finding Sentences”). We compare those results with the performance of standard machine learning (ML) algorithms trained and tested on a larger dataset (about 5,800 labeled sentences), which is still relatively small by ML standards. This methodology and these test results suggest that some access-to-justice use cases can be adequately addressed at much lower cost than previously believed. The datasets, the protocols used to define sentence types, the scripts and ML codes will be publicly available. ACM Reference format: Vern R. Walker, Krishnan Pillaipakkamnatt, Alexandra M. Davidson, Marysa Linares and Domenick J. Pesce. 2019. Automatic Classification of Rhetorical Roles for Sentences: Comparing Rule-Based Scripts with Machine Learning. In Proceedings of the Third Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2019), Montreal, QC, Canada, 10 pages.
Fitting the Style: The Semantic Space for Emotions on Stylized Faces

It has been widely proven among several research disciplines that the information conveyed through the visual channel can be critical for communication. Altering either the motion or the appearance of our interlocutor can change the opinion we form about them or lead us to drastically different conclusions on what is being communicated. In this paper, we explore the influence of altering the visual appearance of a virtual 3D face model through several different stylization techniques on the perception of dynamic conversational facial expressions. We propose the use of a semantic-differential task to recover the underlying semantic/cognitive space for the expressions and their corresponding shifts due to the application of the different styles. Given the appropriate amount of stimuli, we consider this technique well fitted to provide a mapping between meaning of emotions and spectrum of rendering parameters. Our results show that the different techniques are capable to alter the perception of the expressions making them, for example, more intense or ephemeral in a rather consistent manner.
Interlingua: Linking Textbooks Across Different Languages

Increasing numbers of students enrol in formal and informal courses taught in a foreign language. Studying a course from an unfamiliar university/program is difficult enough, but the difficulties multiply when the transition to new course requirements is exacerbated by the necessity to learn course material in a foreign language. This paper describes Interlingua a platform where students can study textbooks in a foreign language supported by on-demand access to relevant reading material in their mother tongue. Interlingua automatically recognises important terminology within textbooks content, extracts structural models of textbooks and links sections and subsections across textbooks in different languages covering the same academic subject. The interface and architecture of Interlingua as well as the technologies underlying the platform are described.
Garment Preconditioning and Regional Simulation Omission for Trying-On Virtual Ensembles

This paper proposes a new method that can be used when virtual ensembles need to be put on to a 3D avatar. We assume that input to the virtual fitting system is a number of garments draped on a target avatar. Then, the problem is to account for the interference between the garments and produce the scene in which the constituent garments are worn in an ensemble. For that, we propose a technique to stratify the garments such that they do not intersect in any region, thus the conventional simulators can start from a workable initial configuration. Then, we introduce the regional simulation omission, which can generate try-on of ensembles in a significantly reduced amount of time. Experiments show that, in the robustness, processing speed, and flexibility, the proposed method is strikingly better than the conventional approach.
Using Programmed Instruction to Help Students Engage with eTextbook Content

The material taught in a Formal languages course is mathematical in nature and requires students to practice proofs and algorithms to understand the content. Traditional Formal Languages textbooks are heavy on prose, and homework typically consists of solving many paper exercises. Students need to read a significant amount of text and do practice problems by hand to achieve understanding. Electronic textbooks have many useful methods to display the content to students. However, unless carefully designed, students abuse these methods to earn grades without studying the content carefully. Inspired by the principles of the Programmed Instruction (PI) teaching method, we seek to develop a new Formal Languages eTextbook capable of conveying Formal Languages concepts more intuitively. The PI approach has students read a little, ideally a sentence or a paragraph, and then answer a question or complete an exercise related to that information. Based on the question response, students are permitted to continue on to other frames of information, or must retry to solve the exercise. Our goal is to present all algorithms using algorithm visualizations and to produce auto-graded exercises to let students demonstrate understanding. To evaluate the pedagogical effectiveness of our new eTextbook, we plan to conduct time and performance evaluations across three offerings of the course CS4114 Formal Languages and Automata at Virginia Tech. The time spent by students looking at instructional content with text and visualizations will be compared with time spent using PI frames to determine levels of student engagement.
FP Tree Algorithm and Approaches in BigData

ABSTRACT: Data mining is process of extracting a large amount of data that need to be analysed patterns have to be extracted from that to share knowledge. In this new era with rumble of data together structured, semi-structured and unstructured, in the field of machine learning, educational data mining, web mining and text mining, environmental research areas, it has become difficult to process, manage and analyzed patterns using traditional databases and relational databases So, a appropriate architecture should be understood to gain and share knowledge about the Big Data. This paper presents an analysis of various algorithms from for handling such big data set. These algorithms define various structures and methods implemented to handle Big Data, also in the paper are listed various data mining tools that were developed for analyzing them.
Optic Disc and Cup Segmentation Based on Enhanced SegNet

Due to imbalanced distributed and restricted medical resources, reliable analysis for medical images is hard to come by, and it is impractical to only rely on human beings to do all the analysis, which is time-consuming and not economic. Application of computer vision techniques in such fields emerges as the situation requires. In this paper, we use deep learning segmentation algorithm to segment the optic disc and the cup from each other and from the rest of the ophthalmoscopy photographs. For a better performance, we change the loss function and crop as a way of data augmentation. The segmentation results can be used to calculate the cup-to-disc ratio (CDR), which is further used to diagnose glaucoma. Challenges such as over-fitting, biased dataset, and poor generalization of the model exist in front of us. We illustrate our model and associated methods dealing with these challenges.
Explainability of Formal Models of Argumentation Applied to Legal Domain

This paper deals with the problems of explainability of argumentation models applied to legal domains. The systems based on the theory of Argumentation Frameworks may be in principle fruitfully applied as explanation tools for AI systems enhanced with Machine Learning mechanisms. However, Argumentation Frameworks as a quickly evolving and diversified field – should be tested with regard to their own explainability. In this paper we provide a set of criteria and we outline a testing procedure towards this goal.
Domain Model and Modeling Workbench for Modeling PDCA-Management Systems

Plan-Do-Check-Act (PDCA)-management systems [1] are cybernetic management systems that underly all management systems standards from the International Organization for Standardization (ISO, https://www.iso.org). Furthermore they play an essential role in the Resource-Event-Agent (REA)business management ontology [2] and in the OntoREA© Accounting and Finance Model [3]. The peculiarity of PDCA-management systems lies in the fact that they contain structural and behavioural components so that they are difficult to model by using the UML language. In this article this modeling problem is solved by the development of a PDCA-domain model (Ecore), i.e. a domain specific language (DSL) and a corresponding PDCA-modeling workbench (EMF) that allows the user to construct context-specific PDCA-applications.
VETUS - Visual Exploration of Time Use Data to Support Environmental Assessment of Lifestyles

The time-use (or activity) patterns individuals perform on a typical day – their individual lifestyles – fundamentally shape our society and the environment we live in. Not only are lifestyles evolving over time, driven by societal and technological change, they also significantly contribute to the achievement of Sustainable Development Goal 12 “responsible consumption and production”, namely through the resource use and emissions associated with goods and services consumed to perform activities. We created an interactive, browser-based tool to visualize and intuitively explore statistical time-use data. The visualization helps to gain an overview about the available data, identify and compare common time-use patterns and draw up hypotheses about the relationship between changes in lifestyles and their social and environmental consequences. We use the tool to compare time-use data from different regions, time periods as well as socio-economic and demographic backgrounds and estimate the associated energy consumption. From a time-use perspective, any technological change which triggers changes in time allocation can only be environmentally sustainable if the environmental impact of the total of the activities performed after the change is lower than before.
Scaffolded, Scrutable Open Learner Model (SOLM) as a Foundation for Personalised e-Textbooks

While e-textbooks have become increasingly widespread, they have made little use of personalisation. This paper presents an architecture for making a learner’s own lifelong learner model as the foundation for a highly flexible form of personalisation for learners using e-textbooks. The architecture has the following core components: an arbitrary e-texbook platform that minimally provides log data about the learner; a personal user model ; an open learner model (OLM) interface that provides personalised scaffolding to support reflection on learning progress and support for the learner to scrutinise their model. We present a worked example of the approach, explaining design decisions in terms of a substantial body of previous work on open learner models and support for meta-cognitive processes of self-monitoring, self-reflection and planning. Our key contribution is a new approach to personalised e-textbooks based on the learner’s own long term learner model.
Smooth muscle-specific deletion of MnSOD exacerbates diabetes-induced bladder dysfunction in mice.

Bladder dysfunction in diabetes progresses gradually over time. However, the mechanisms of the development are not clear. We test the hypothesis that oxidative stress plays a key role in the development of diabetic bladder dysfunction, using an inducible smooth muscle (SM)-specific Sod2 gene knockout (SM-Sod2 KO) mouse model. Eight-week-old male Sod2lox/lox, SM-CreERT2(ki)Cre/+ mice and wild-type mice were assigned to diabetic or control groups. 4-hydroxytamoxifen was injected into Sod2lox/lox, SM-CreERT2(ki)Cre/+ mice to activate CreERT2-mediated deletion of Sod2. Diabetes was induced by injection of streptozotocin while control mice were injected with vehicle. Nine weeks later, bladder function was evaluated and bladders were harvested for immunoblotting analysis. Wild-type diabetic mice presented compensated bladder function, along with increased nitrotyrosine and manganese superoxide dismutase in detrusor muscle. Induction of diabetes in SM-Sod2 knockout mice caused deteriorated bladder function and even greater increases in nitrotyrosine compared with wild-type diabetic mice. The expression levels of Bax and cleaved caspase-3 were increased, but Bcl-2 was decreased in detrusor muscle of both diabetic groups, with more pronounced effects in SM-Sod2 knockout diabetic mice. Our findings demonstrate that exaggerated oxidative stress can accelerate the development of bladder dysfunction in diabetic mice, and the enhanced activation of apoptotic pathways in the bladder may be involved in the process.
Monophosphoryl lipid A induces protection against LPS in medullary thick ascending limb through induction of Tollip and negative regulation of IRAK-1.

LPS inhibits HCO3- absorption in the medullary thick ascending limb (MTAL) through a TLR4-MyD88-ERK pathway that is upregulated by sepsis. Pretreatment with the nontoxic immunomodulator monophosphoryl lipid A (MPLA) prevents inhibition by LPS through activation of a TLR4-TRIF-PI3K pathway that prevents LPS-induced ERK activation. Here, we identify molecular mechanisms that underlie the protective inhibitory interaction between the MPLA-PI3K and LPS-ERK pathways. Treatment of mouse MTALs with LPS in vitro increased phosphorylation of interleukin-1 receptor-associated kinase (IRAK)-1, a critical mediator of LPS signaling downstream of TLR4-MyD88. Activation of ERK by LPS was eliminated by a selective IRAK-1 inhibitor, establishing IRAK-1 as the upstream mediator of ERK activation. Pretreatment of MTALs with MPLA in vitro prevented LPS-induced IRAK-1 activation; this effect was dependent on PI3K. Treatment of MTALs with MPLA increased expression of Tollip, an inducible protein that negatively regulates LPS signaling by inhibiting IRAK-1. The MPLA-induced increase in Tollip protein level was prevented by PI3K inhibitors. In coimmunoprecipitation experiments, MPLA increased the amount of Tollip stably bound to IRAK-1, an interaction that inhibits IRAK-1 activation. These results support a mechanism whereby MPLA increases Tollip expression in the MTAL through a PI3K-dependent pathway. Tollip, in turn, inhibits LPS-induced TLR4 signaling by suppressing activation of IRAK-1, thereby preventing activation of ERK that inhibits HCO3- absorption. These studies show that MPLA induces reprogramming of MTAL cells that protects against LPS stimulation and identify IRAK-1 and Tollip as new therapeutic targets to prevent renal tubule dysfunction in response to infectious and inflammatory stimuli.
Design, Synthesis and Biological Evaluation of 7-Chloro-9H-pyrimido[4,5-b]indole-based Glycogen Synthase Kinase-3β Inhibitors

Glycogen synthase kinase-3β (GSK-3β) represents a relevant drug target for the treatment of neurodegenerative pathologies including Alzheimer’s disease. We herein report on the optimization of a novel class of GSK-3β inhibitors based on the tofacitinib-derived screen hit 3-((3R,4R)-3-((7-chloro-9H-pyrimido[4,5-b]indol-4-yl)(methyl)amino)-4-methylpiperidin-1-yl)-3-oxopropanenitrile (1). We synthesized a series of 19 novel 7-chloro-9H-pyrimido[4,5-b]indole-based derivatives and studied their structure–activity relationships with focus on the cyanoacetyl piperidine moiety. We unveiled the crucial role of the nitrile group and its importance for the activity of this compound series. A successful rigidization approach afforded 3-(3aRS,7aSR)-(1-(7-chloro-9H-pyrimido[4,5-b]indol-4-yl)octahydro-6H-pyrrolo[2,3-c]pyridin-6-yl)-propanenitrile (24), which displayed an IC50 value of 130 nM on GSK-3β and was further characterized by its metabolic stability. Finally, we disclosed the putative binding modes of the most potent inhibitors within the ATP binding site of GSK-3β by 1 µs molecular dynamics simulations.
Molecular Mechanisms of Microglial Motility: Changes in Ageing and Alzheimer’s Disease

Microglia are the tissue-resident immune cells of the central nervous system, where they constitute the first line of defense against any pathogens or injury. Microglia are highly motile cells and in order to carry out their function, they constantly undergo changes in their morphology to adapt to their environment. The microglial motility and morphological versatility are the result of a complex molecular machinery, mainly composed of mechanisms of organization of the actin cytoskeleton, coupled with a “sensory” system of membrane receptors that allow the cells to perceive changes in their microenvironment and modulate their responses. Evidence points to microglia as accountable for some of the changes observed in the brain during ageing, and microglia have a role in the development of neurodegenerative diseases, such as Alzheimer’s disease. The present review describes in detail the main mechanisms driving microglial motility in physiological conditions, namely, the cytoskeletal actin dynamics, with emphasis in proteins highly expressed in microglia, and the role of chemotactic membrane proteins, such as the fractalkine and purinergic receptors. The review further delves into the changes occurring to the involved proteins and pathways specifically during ageing and in Alzheimer’s disease, analyzing how these changes might participate in the development of this disease.