Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptome Analysis of Ovarian and Uterine Clear Cell Malignancies

Purpose Ovarian and uterine clear cell carcinomas (CCCs) are rare but associated with poor prognosis. This study explored RNA transcription patterns characteristic of these tumors. Experimental Design RNA sequencing (RNA-seq) of 11 ovarian CCCs and five uterine CCCs was performed and compared to publicly available data from high grade serous ovarian cancers (HGSOCs). Ingenuity Pathway Analyses were performed. CIBERSORT analyses estimated relative fractions of 22 immune cell types in each RNA-seq sample. Sequencing data was correlated with PD-L1 immunohistochemical expression. Results RNA-seq revealed 1,613 downregulated and 1,212 upregulated genes (corrected p < 0.05, |FC |≥10) in ovarian CCC versus HGSOC. Two subgroups were identified in the ovarian CCC, characterized by ethnicity and expression differences in ARID1A. There were 3,252 differentially expressed genes between PD-L1+/− ovarian CCCs, revealing immune response, cell death, and DNA repair networks, negatively correlated with PD-L1 expression, whereas cellular proliferation networks positively correlated with expression. In clear cell ovarian versus clear cell uterine cancer, 1,607 genes were significantly upregulated, and 109 genes were significantly downregulated (corrected p < 0.05, |FC|≥10). Comparative pathway analysis of late and early stage ovarian CCCs revealed unique metabolic and PTEN pathways, whereas uterine CCCs had unique Wnt/Ca+, estrogen receptor, and CCR5 signaling. CIBERSORT analysis revealed that activated mast cells and regulatory T cell populations were relatively enriched in uterine CCCs. The PD-L1+ ovarian CCCs had enriched resting NK cells and memory B cell populations, while PD-L1− had enriched CD8 T-cells, monocytes, eosinophils, and activated dendritic cells. Conclusions Unique transcriptional expression profiles distinguish clear cell uterine and ovarian cancers from each other and from other more common histologic subtypes. These insights may aid in devising novel therapeutics.
Targeting PAI-1 in Cardiovascular Disease: Structural Insights Into PAI-1 Functionality and Inhibition

Plasminogen activator inhibitor-1 (PAI-1), a member of the serine protease inhibitor (serpin) superfamily with antiprotease activity, is the main physiological inhibitor of tissue-type (tPA) and urokinase-type (uPA) plasminogen activators (PAs). Apart from being crucially involved in fibrinolysis and wound healing, PAI-1 plays a pivotal role in various acute and chronic pathophysiological processes, including cardiovascular disease, tissue fibrosis, cancer, and age-related diseases. In the prospect of treating the broad range of PAI-1-related pathologies, many efforts have been devoted to developing PAI-1 inhibitors. The use of these inhibitors, including low molecular weight molecules, peptides, antibodies, and antibody fragments, in various animal disease models has provided ample evidence of their beneficial effect in vivo and moved forward some of these inhibitors in clinical trials. However, none of these inhibitors is currently approved for therapeutic use in humans, mainly due to selectivity and toxicity issues. Furthermore, the conformational plasticity of PAI-1, which is unique among serpins, poses a real challenge in the identification and development of PAI-1 inhibitors. This review will provide an overview of the structural insights into PAI-1 functionality and modulation thereof and will highlight diverse approaches to inhibit PAI-1 activity.
Cyp2C19*2 Polymorphism Related to Clopidogrel Resistance in Patients With Coronary Heart Disease, Especially in the Asian Population: A Systematic Review and Meta-Analysis

In recent years, the relationship between Cyp2C19*2 gene polymorphism and clopidogrel resistance reflected by platelet function assay has been studied extensively, but there is no clear conclusion yet. In order to evaluate the relationship between Cyp2C19*2 gene polymorphism and clopidogrel resistance more accurately, meta-analysis was conducted in this study. The I2 value taking 50% as the limit, the heterogeneity is judged as high or low, and then a random effect model or a fixed effect model is selected for statistical analysis. PubMed, EMBASE, Web of Science, CNKI, and China Wanfang database were searched, and the related literatures from the establishment of the database to May 2020 were collected and analyzed by STATA 15.0 software. A total of 3,073 patients were involved in 12 studies, including 1,174 patients with clopidogrel resistance and 1,899 patients with non-clopidogrel resistance. The results of this study showed that allele model (A vs. G): OR = 2.42 (95%CI: 1.97–2.98); dominant model (AA+GA vs. GG): OR = 2.74 (95%CI: 2.09–3.59); recessive model (AA vs. GA+GG): OR = 4.07 (95%CI: 3.06–5.41); homozygous model (AA vs. GG): OR = 5.70 (95%CI: 4.22–7.71); heterozygote model (GA vs. GG): OR = 2.32 (95%CI: 1.76–3.07), the differences were statistically significant. Also, the analysis of the Ethnicity subgroup indicated that the Asian allele model and the other four gene models were statistically significant. In conclusion, Cyp2C19*2 gene polymorphism is strongly associated with clopidogrel resistance. Allele A, genotype GA, AA, and GG + GA can increase clopidogrel resistance, especially in the Asian population.
Epitaxial growth of silicon on Si(001) by scanning tunneling microscopy

The epitaxial growth of silicon on Si(001)‐(2×1) substrates at temperatures between 580 and 850 K is studied using scanning tunneling microscopy (STM). The growth is strongly anisotropic, forming long narrow structures only a few dimers wide but more than 100 A long. Models are proposed for the two types of antiphase boundaries that are observed on the epitaxially grown surfaces.
Dysregulation of BMP9/BMPR2/SMAD signalling pathway contributes to pulmonary fibrosis and pulmonary hypertension induced by bleomycin in rats

Pulmonary hypertension related to pulmonary fibrosis is classed as WHO Group III, one of the most common groups which lacks effective treatment options. In this study, we aimed to uncover the underlying mechanisms, particularly the involvement of the BMP9/BMPR2/SMAD signalling pathway, in this subtype of pulmonary hypertension.
First Results of the TurkLang-7 Project: Creating Russian-Turkic Parallel Corpora and MT Systems

The idea of the “TurkLang-7” project is to create datasets and neural machine translation systems for a set of Russian-Turkic low-resource language pairs. It is planned to achieve this goal through a hybrid approach to the creation of a multilingual parallel corpus between Russian and Turkic languages, studying the applicability and effectiveness of neural network learning methods (transfer learning, multi-task learning, back-translation, dual learning) in the context of the selected language pairs, as well as the development of specialized methods for the unification of parallel data in different languages, based on the agglutinative nature of the selected Turkic languages (structural and functional model of the Turkic morpheme). In this paper, we describe the main stages of work on this project and the results of the first year: we developed a semi-automatic process for creating parallel corpora, collected data from several sources on 7 Turkic languages, and conducted the first experiments to create machine translation systems.
Not Just for Humans: Explanation for Agent-to-Agent Communication

Once precisely defined so as to include just the explanation’s act, the notion of explanation should be regarded as a central notion in the engineering of intelligent system—not just as an add-on to make them understandable to humans. Based on symbolic AI techniques to match intuitive and rational cognition, explanation should be exploited as a fundamental tool for inter-agent communication among heterogeneous agents in open multi-agent systems. More generally, explanation-ready agents should work as the basic components in the engineering of intelligent systems integrating both symbolic and sub-/non-symbolic AI techniques.
Exploring Composite Dataset Biases for Heart Sound Classification

In the last few years, the automatic classiﬁcation of heart sounds has been widely studied as a screening method for heart disease. Some of these studies have achieved high accuracies in heart abnormality prediction. However, for such models to assist clinicians in the detection of heart abnormalities, it is of critical importance that they are gener-alisable, working on unseen real-world data. Despite the importance of generalisability, the presence of bias in the leading heart sound datasets used in these studies has remained unexplored. In this paper, we explore the presence of potential bias in heart sound datasets. Using a small set of spectral features for heart sound representation, we demonstrate experimentally that it is possible to detect sub-datasets of PhysioNet, the leading dataset of the ﬁeld, with 98% accuracy. We also show that sensors which have been used to capture recordings of each dataset are likely the main cause of the bias in these datasets. Lack of awareness of this bias works against generalised models for heart sound diagnostics. Our ﬁndings call for further research on the bias issue in heart sound datasets and its impact on the generalisability of heart abnormality prediction models.
Artificial Intelligence Research for Fighting Political Polarisation: A Research Agenda

Political polarization is a growing phenomenon in numerous countries around the world. In many cases, this polarization is leading to a divergence between the positions of elected decision makers, creating a loss of middle-ground and compromise, which is in turn causing the democratic process to grind to a halt. The causes of this polarization are both contentious and numerous, with many sociologists, political scientists, economists, and historians weighing in on the matter. Although computer science cannot comment on the causes, we may be able to develop group decision making algorithms and tools to help ameliorate the situation. In this paper we outline some general research proposals on the possibility of combining machine learning techniques with results from computational social choice to mitigate the polarization. Speciﬁcally, we focus on the design of multi-winner voting rules that are both eﬃciently computable and have provable guarantees on desirable properties such as proportionality and representation. We hope that research advances in these areas can have concrete impact on ﬁghting political polarization.
Simulation-based Assessment of Grocery Shopping in Urban Areas

This study proposes a simulation approach for modelling, assessing, and quantifying travel distances caused by stationary grocery shopping activities as well as e-grocery deliveries on the last mile. Utilizing an integrated emission calculation model, the simulated travel values are converted into relevant emission output factors to assess the individual impact of e-grocery deliveries compared to individual shopping trips by private consumers for different scenarios. While e-grocery does not yield an emission saving potential for low penetration rates less than 20 %, up to 41.5 % of the total emission outputs can be economized when home deliveries are employed for a moderate share of the urban population.
LogVis: Graph-Assisted Visual Analysis of Event Logs from Industrial Equipment

Visual reasoning on a graph is often a challenging task mainly due to the vast number of nodes and edges displayed. It becomes particularly challenging on log graph data, where thousands of events may be logged within minutes. In this study, we focus on three common log analysis tasks, namely Event Overview , Root-Cause Analysis and Pattern Analysis , and propose visualization approaches to overcome challenges particularly associated with these tasks. The proposed approaches are demonstrated on sample use-cases on industrial equipment logs.
Less is More when Applying Transfer Learning to Multi-Spectral Data

Transfer Learning is widely recognized as providing incred-ible beneﬁts to many image processing tasks. But not all problems in the computer vision ﬁeld are driven by traditional Red, Green, and Blue (RGB) imagery as tend to be assumed by most large pre-trained models for Transfer Learning. Satellite based remote sensing applications for example typically use multispectral bands of light. While transferring RGB features to this non-RGB domain has been shown to generally give higher accuracy than training from scratch, the question remains whether a more suitable ﬁne tuned method can be found. Given this challenge, this paper presents a study in multispectral image analysis using multiple methods to achieve feature transfer. Speciﬁcally, we train and compare two pre-trained models based on the Resnet50 architecture and apply them to a multi-spectral image processing task. The key diﬀerence between the two models is that one was pre-trained in a conventional way against RGB data, while the other was trained against a single band greyscale variant of the same data. Our results demonstrate an improved performance on the greyscale pre-trained model relative to the more traditional RGB model.
Map relative localization based on road lane matching with Iterative Closest Point algorithm

Accurate and reliable localization is necessary for vehicle autonomous driving. Existing localization systems based on the GNSS cannot always provide lane-level accuracy. This paper proposes the method that improves vehicle localization by using road lanes recognized from a camera and a digital map. Iterative Closest Point (ICP) matching is performed for generated point clouds to minimize lateral error. The neural network is used for lane detection, detections are post-processed and fitted to the polynomial. Changes that allowed improving ICP matching are described. Finally, we perform an experiment with GPS RTK signal as ground truth and demonstrate that the proposed method has a position error of less than 0.5 m for vehicle localization.
A Large Scale Study On the Effectiveness of Manual and Automatic Unit Test Generation

Recently, an increasingly large amount of effort has been devoted to implementing tools to generate unit test suites automatically. Previous studies have investigated the effectiveness of these tools by comparing automatically generated test suites (ATSs) to manually written test suites (MTSs). Most of these studies report that ATSs can achieve higher code coverage, or even mutation coverage, than MTSs, particularly when suites are generated from defective code. However, these studies usually consider a limited amount of classes or subject programs, while the adoption of such tools in the industry is still low. This work aims to compare the effectiveness of ATSs and MTSs when applied as regression test suites. We conduct an empirical study, using ten programs (1368 classes), written in Java, that already have MTSs and apply two sophisticated tools that automatically generate test cases: Randoop and EvoSuite. To evaluate the test suites' effectiveness, we use line and mutation coverage. Our results indicate that MTSs are, in general, more effective than ATSs regarding the investigated metrics. Moreover, the number of generated test cases may not indicate test suites' effectiveness. Furthermore, there are situations when ATSs are more effective, and even when ATSs and MTSs can be complementary.
A Qualitative Analysis of the State of the Art in Process Extraction from Text

Within a company, processes are typically documented in form of unstructured textual information. To exploit all the techniques of Business Process Management and Process Mining, process models need to be represented in a formal (or semi-formal) representation, the process model diagram. However, manually obtaining an initial process model out of a process description document is a time consuming and cost intensive operation. Some initial solutions to address the challenge of process extraction from text have been proposed in the literature. But, the analysis of state of the art contributions reveals that this line of research has not reached its maturity yet and that process extraction from text can be considered an unresolved problem still in an early stage of development. Indeed, these contributions mainly adopt ad-hoc solutions based on rules, word-lists, and heuristics. In this paper, we adopt the instrument of qualitative analysis on state-of-the-art approaches and tools to shed light on current limitations of the process extraction from text area. In addition to an analysis of the main reference papers we test reference tools on samples of text extracted from real documents describing Standard Operating Procedures that exhibit a greater complexity than the publicly available procedural descriptions so far used as reference text by the process extraction from text community. The analysis reveals the inability for those approaches to perform well in real scenarios. The discussion of the results illustrates open points, fundamental challenges to solve, and gaps to fill. It also suggests new ideas on how to tackle some of the identified limitations which we intend to pursue in the future.
Forward Linearised Tree Pattern Matching Using Tree Pattern Border Array

We define a tree pattern border array as a property of linearised trees analogous to border arrays from the string domain. We use it to define a new forward tree pattern matching algorithm for ordered trees, which finds all occurrences of a single given linearised tree pattern in a linearised input tree. As with the classical Morris-Pratt algorithm, the tree pattern border array is used to determine shift lengths in the searching phase of the tree pattern matching algorithm. We compare the new algorithm with the best performing previously existing algorithms based on backward linearised tree pattern matching algorithms, (non-)linearised tree pattern matching algorithms using finite tree automata or stringpath matchers. We show that the presented algorithm outperforms these for single tree pattern matching.
A Dashboard to Support Decision-Making Processes in Learning Ecosystems: A Metamodel Integration

There are software solutions to solve most of the problems related to information management in any company or institutions, but still, there is a problem for transforming information into knowledge. Technological ecosystems emerge as a solution to combine existing tools and human resources to solve different problems of knowledge management. In particular, when the ecosystem is focused on learning processes associated with knowledge are named learning ecosystems. The learning ecosystem metamodel defined in previous works solves several problems related to the definition and implementation of these solutions. However, there are still challenges associated with improving the analysis and visualization of information as a way to discover knowledge and support decision making processes. On the other hand, there is a metamodel proposal to define customized dashboards for supporting decision-making processes. This proposal aims to integrate both metamodels as a way to improve the definition of learning ecosystems.
iRadviz: an inversion radviz for class visualization of multivariate data visualization

Multivariate data visualization is an interesting research field with many applications in ubiquitous fields of sciences. Radial visualization (Radviz) is one of the most common information visualization techniques for visualizing multivariate data. Unfortunately, Radviz display different information about structures of multivariate data on the different the order of the data dimensions and all points with different scale maps into the same point in the visual space. In this paper, we propose a method that improve the Radviz layout for class visualization of multivariate data. The basic idea of our method is finding a good corner viewing of a hypercube. Our method provides an improvement visualizing class structures of multivariate data sets on the Radviz. We present our method with two kinds of quality measurement. We prove the efficiency of our method for several data sets.
Systematic Literature Mapping on Eye Tracking and Data Mining

The eye tracking technology has been applied in several fields, such as medicine, engineering and computation. In this last, one of the main uses of the eye tracking technology is for user testing, producing a large volume of data that must be analyzed effectively and efficiently. Of course, there is the idea of combining the concepts of mining the data from eye tracking devices. In order to address this issue, the goal of this work is to carry out a systematic survey of the literature on eye tracking and data mining. We selected some of the major digital libraries to search for articles related to these issues, and through formulated criteria we reached a final set of 16 articles to be analyzed. The analysis of such studies allowed us to reach to some conclusions, e.g., which techniques are the most used in data mining from eye tracking, which are the main forms of visualization of this data.
Covid-19 CT Image Recognition Based on Multi-stage Transfer Learning

So far, deep learning method of artificial intelligence (AI) has achieved great success in the field of image processing. We aim to utilize deep learning method to automatically distinguish subjects' lung CT images to assist in the diagnosis of Covid-1 9. W e innovatively build a multi-stage transfer learning (TL) model: fine-tuning from hand-written numbers recognition to normal pneumonia X-ray images, and then to Covid-19 lung CT. Compared to the conventional single-stage TL model, multi-stage TL model achieves better results in accuracy, specificity and sensitivity. It illuminates that machine learning is similar to human learning, for the split of difficult targets into progressive simple ones helps to complete the final goal.
Arsenic Removal Using Iron Oxide Loaded Alginate Beads

The application of biopolymers (alginate), as sorbent supports, for the removal of arsenic from contaminated water has been investigated in the present study. Calcium alginate beads were placed in a column to form a fixed bed and treated (doped/coated) with hydrous ferric oxides. Three different types of modified alginate beads were examined for the removal of arsenic; the most efficient type was found to be doped with alginate and subsequently coated with iron oxides, whereas the other two types were calcium alginate beads doped or coated with iron oxides. The total amount of iron loaded on this material was found to be 3.9 mg of Fe/g of wet alginate bead. Approximately 230 bed volumes of a 50 μg/L As(V) solution were treated before the breakthrough point was reached, whereas the removal of As(III) was not as efficient, reaching the breakthrough point after the treatment of only 45 bed volumes. The results were modeled using the bed depth service time and empty bed residence time models.
Determination of the Length, Diameter, Molecular Mass, Density and Surfactant Adsorption of SWCNTs in Dilute Dispersion by Intrinsic Viscosity, Sedimentation, and Diffusion Measurements

A combination of intrinsic viscosity, sedimentation, and diffusion measurements is performed to characterize the structures of single-walled carbon nanotubes (SWCNTs) in dilute dispersion. It allows quantifying the bulk averaged length (L), diameter (d), molecular mass (M), and density (ρ) as well as the surfactant adsorption for SWCNTs. With this approach applied to the SWCNTs processed by sonication, a novel Mark–Houwink–Sakurada equation, [η]c = 1.17M0. 33 is recovered. The less than 0.5 exponent is a result of the power-law relationship d ∝ L0.62 caused by the concurrently occurred cutting and exfoliation of SWCNT bundles in the sonication process. An in-depth analysis of the experimentally acquired d and ρ enables a quantification of sodium dodecylbenzenesulfonate (SDBS) adsorption on SWCNTs to reveal the curvature effect on the surface coverage of surfactant adsorption.