Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple isodicentric Y chromosomes in myeloid malignancies: a unique cytogenetic entity and potential therapeutic target

Abhishek A. Mangaonkar, Mrinal M. Patnaik , Gavin R. Oliver, Kathleen W. Rao, Kathleen Kaiser-Rogers, Jaime I. Davila, Numrah Fadra, Rebecca N. Wehrs, Michelle A. Elliott, Patricia T. Greipp, Kevin C. Halling and Daniel L. Van Dyke Division of Hematology, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
Bacterial Lipoproteins Constitute the TLR2-Stimulating Activity of Serum Amyloid A

Studies comparing endogenous and recombinant serum amyloid A (SAA) have generated conflicting data on the proinflammatory function of these proteins. In exploring this discrepancy, we found that in contrast to commercially sourced recombinant human SAA1 (hSAA1) proteins produced in Escherichia coli, hSAA1 produced from eukaryotic cells did not promote proinflammatory cytokine production from human or mouse cells, induce Th17 differentiation, or stimulate TLR2. Proteomic analysis of E. coli–derived hSAA1 revealed the presence of numerous bacterial proteins, with several being reported or probable lipoproteins. Treatment of hSAA1 with lipoprotein lipase or addition of a lipopeptide to eukaryotic cell–derived hSAA1 inhibited or induced the production of TNF-α from macrophages, respectively. Our results suggest that a function of SAA is in the binding of TLR2-stimulating bacterial proteins, including lipoproteins, and demand that future studies of SAA employ a recombinant protein derived from eukaryotic cells.
Upregulation of LGALS1 is associated with oral cancer metastasis

Background: Oral cancer metastasis is a devastating process that contributes to poor prognosis and high mortality, yet its detailed underlying mechanisms remain unclear. Here, we aimed to evaluate metastasis-specific markers in oral cancer and to provide comprehensive recognition concerning functional roles of the specific target in oral cancer metastasis. Methods: Lectin, galactoside-binding, soluble, 1 (LGALS1) was identified by secretomic analysis. LGALS1 expression of patient samples with oral cancer on the tissue microarray were examined by immunochemical (IHC) staining. Small interfering RNA (siRNA)-mediated knockdown of LGALS1 revealed the role of LGALS1 in oral cancer metastasis in vitro and in vivo. Results: LGALS1 was observed to be upregulated in highly invasive oral cancer cells, and elevated LGALS1 expression was correlated with cancer progression and lymph node metastasis in oral cancer tissue specimens. Functionally, silencing LGALS1 resulted in suppressed cell growth, wound healing, cell migration, and cell invasion in oral cancer cells in vitro. Knockdown of LGALS1 in highly invasive oral cancer cells dramatically inhibited lung metastasis in an in vivo mouse model. Mechanistic studies suggested p38 mitogen-activated protein kinase (MAPK) phosphorylation, upregulated MMP-9, and mesenchymal phenotypes of epithelial-mesenchymal transition (EMT) in highly invasive oral cancer cells, whereas siRNA against LGALS1 resulted in the inactivation of p38 MAPK pathway, downregulated MMP-9, and EMT inhibition. Conclusions: These findings demonstrate that elevated LGALS1 is strongly correlated with oral cancer progression and metastasis, and that it could potentially serve as a prognostic biomarker and an innovative target for oral cancer therapy.
RFree-ID: An Unobtrusive Human Identification System Irrespective of Walking Cofactors Using COTS RFID

Human identification is a prerequisite for many personalized services in smart spaces. This paper presents RFree-ID, the first unobtrusive RFID-based human identification system irrespective of walking cofactors ($e.g.$, appearance changes or inconsistent walking paths). The key insight is that the RFID reader and tags can serve as a radio gate, and the walking-induced RF signal fluctuations from tags are capable of perceiving different walking patterns when people cross this gate. More importantly, spatially separated tags can provide abundant temporal and spatial information for amplifying discrepancies among people and minifying the influence of walking cofactors. Therefore, after collecting phase fluctuations received from RFID tags, RFree-ID identifies people by using a sequence of signal processing techniques and a well-designed matching algorithm. The system is implemented on COTS RFID devices, and extensive experimental evaluation under various conditions validates the high reliability and robustness of our system.
Cooperating Tools for MWE Lexicon Management and Corpus Annotation

We present tools for lexicon and corpus management that offer cooperating functionality in corpus annotation. The former, named Cradle, stores a set of words and expressions where multi-word expressions are defined with their own part-of-speech information and internal syntactic structures. The latter, named ChaKi, manages text corpora with part-of-speech (POS) and syntactic dependency structure annotations. Those two tools cooperate so that the words and multi-word expressions stored in Cradle are directly referred to by ChaKi in conducting corpus annotation, and the words and expressions annotated in ChaKi can be output as a list of lexical entities that are to be stored in Cradle.
As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Summary Objectives: To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query. Results: In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies. Conclusions: Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.
Abnormal expression of HMGB-3 is significantly associated with malignant transformation of hepatocytes

AIM To explore the relationship between dynamic expression of high mobility group box-3 (HMGB3) and malignant transformation of hepatocytes. METHODS Expression of HMGB family proteins were observed in rat hepatocarcinogenesis models induced with 2-acetylaminofluorene. Alterations of HMGB3 were analyzed at the mRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and at the protein level by immunohistochemistry or Western blotting. HMGB3 in human liver cancer tissues were evaluated using bioinformatics databases from GEO, TCGA, and Oncomine. A specific HMGB3-shRNA was used to knock down HMGB3 expression in order to investigate its effects on proliferation and cell cycle in vitro and in vivo. RESULTS Elevated HMGB3 levels were first reported in hepatocarcinogenesis, with increasing expression from normal liver to cancer. Bioinformatic databases showed that HMGB3 expression in hepatocellular carcinoma tissues was significantly higher than that in normal liver tissues. Higher HMGB3 expression was discovered in liver cancer cells compared with LO2 cells in vitro. According to gene set enrichment analysis, HMGB3 mRNA levels were correlated with cell cycle and DNA replication pathways. Knocking down HMGB3 by specific shRNA significantly inhibited proliferation of HepG2 cells by cell cycle arrest and downregulating DNA replication related genes (cyclin B1, FEN1, and PCNA) at the mRNA and protein level. Furthermore, silencing HMGB3 significantly inhibited xenograft tumor growth (measured by Ki67) in vivo. CONCLUSION HMGB3 is involved in malignant transformation of hepatocytes and could be a useful biomarker for diagnosis and a potential target for therapy of liver cancer.
Cell circuits between B cell progenitors and IL-7+ mesenchymal progenitor cells control B cell development

B cell progenitors require paracrine signals such as interleukin-7 (IL-7) provided by bone marrow stromal cells for proliferation and survival. Yet, how B cells regulate access to these signals in vivo remains unclear. Here we show that proB and IL-7+ cells form a cell circuit wired by IL-7R signaling, which controls CXCR4 and focal adhesion kinase (FAK) expression and restricts proB cell movement due to increased adhesion to IL-7+CXCL12Hi cells. PreBCR signaling breaks this circuit by switching the preB cell behavior into a fast-moving and lower-adhesion state via increased CXCR4 and reduced FAK/&agr;4&bgr;1 expression. This behavioral change reduces preB cell exposure to IL-7, thereby attenuating IL-7R signaling in vivo. Remarkably, IL-7 production is downregulated by signals provided by preB cells with unrepaired double-stranded DNA breaks and by preB acute lymphoblastic leukemic cells. Combined, these studies revealed that distinct cell circuits control the quality and homeostasis of B cell progenitors.
Islanding Detection of Hybrid Distributed Generation Under Reduced Non-Detection Zone

Future distribution systems are faced with more challenges on islanding detection due to the increasing penetration level of inverter-based distributed generators (DGs). Different DG technologies, inverter control as well as other advanced inverter functions, such as fault ride through are challenging the capability of islanding detection schemes. On the other hand, for multiple feeders network, topological change of feeders and islanding at adjacent feeder increase the vulnerability of islanding detection devices. Furthermore, available islanding detection schemes are suffering from notable non-detection zones (NDZs) under reduced power mismatches. Therefore, to mitigate these issues, this paper proposes an effective methodology for building decision trees-based intelligent relay (IR). This methodology utilizes the NDZ boundaries of existing standard relays and applies a comprehensive training/testing strategy, which effectively reduces the NDZ while maintaining a superior dependability and security performance. To validate the applicability of the proposed methodology, the hardware-in-the-loop simulations are realized by programming the generated IR logics into a real commercial relay.
Decision-Making for Electricity Retailers: A Brief Survey

With the continuous development of smart grid and further restructuring of power industry, modern power systems have been transformed to complex cyber-physical systems characterized with high renewable energy penetrations, distributed facilities, advanced metering, and communication technologies, as well as ever-increasing customer awareness. These new development trends pose significant challenges for electricity retailers and call for innovative decision-making methods. To help researchers and engineers have a better overview of the state-of-the-art on electricity retail decision-making schemes, this paper aims to survey the latest progress on this subject. Some critical and open issues in this field are also discussed.
Importance of User's Profile Attributes in Identity Matching Across Multiple Online Social Networking Sites

This paper is an attempt to explore the importance of user's profile attributes that can make a significant impact on identity resolution techniques across multiple online social networks. In this study, we proposed a method that is helpful to find out the most significant profile attributes in identity matching schemes. For example, a unique identity number (like a mobile number) can be significant attributes in identity matching, but most of the user's profiles don't disclose this kind of information because of privacy issues. So there is an opportunity for other profile attributes to be significant because of easily accessible on social networks from identity matching point of view. This method will be helpful in increasing the accuracy of the identity matching frameworks.
Robust Coordinated Optimization of Active and Reactive Power in Active Distribution Systems

Active power dispatch and reactive power optimization problems are usually handled separately in active distribution systems, aiming at minimizing the total generation cost or transmission losses. However, the separate optimization cannot achieve a global optimum scheme in distribution system operations. Moreover, the significant relationship between the active power and reactive power may pose great challenges to distribution system operations due to the uncertain nature of load demands and intermittent renewable energy resources. In this paper, using the branch flow model-based relaxed optimal power flow, we formulate a robust coordinated optimization problem for active and reactive powers as a mixed integer second-order cone (SOC) programming problem. Furthermore, in order to address the uncertainties, a two-stage robust optimization model is proposed to coordinate the on load tap changer ratios, reactive power compensators, and charge–discharge power of energy storage system to find a robust optimal solution. Then the column-and-constraint generation algorithm is applied to solve the proposed robust two-stage optimization model. In the relaxed optimal power flow, a stricter cut is added to speed up the computation process of the SOC relaxation in order to guarantee the exactness for representative cases, such as those with high penetration of distributed energy resources. Numerical results based on the 33-bus and 69-bus systems verify the effectiveness of the proposed method.
A New Clustering Method Using an Augmentation to the Self Organizing Maps

A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods.
Face Detection for Pulse Rate Measurement

Monitoring vital signals, including pulse rate is a crucial process to detect cardiovascular disorders and body liveness. Some of the clinically used techniques might cause irritation or discomfort for patients. This paper presents a contactless, non-invasive camera-based method of extracting pulse rate from a video stream without attaching electrodes to the skin. The introduced method is a colour-based approach that can estimate heart rate through amplifying subtle changes in skin colour which caused by blood circulation. When evaluated on 7 subjects, this approach reported similar heart rates comparing to a reference oximeter system, however, this method is sensitive to motion artefact and light noise.
Evaluating the Robustness of Global Appearance Descriptors in a Visual Localization Task, under Changing Lighting Conditions

Map building and localization are two important abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents some possibilities to solve these problems robustly using global appearance descriptors. In this way, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance description for each whole image. Using these descriptors, the robot is able to estimate its position in a previously built map, which is also composed of a set of global appearance descriptors. In this work, the global appearance descriptors performance at the localization task inside a known environment has been studied. In the assignment a map is already built and the goal is to evaluate the descriptors’ robustness to perform localization tasks when the environment visual appearance changes substantially. To achieve this objective, a comparative evaluation of several global appearance descriptors is carried out.
Liquids Permittivity Measurement Using Two-Wire Transmission Line Sensor

A novel method for the measurement of complex permittivity of liquid dielectric materials in a wide frequency range is proposed. The technique utilizes an open-ended two-wire transmission line sensor that is submerged in a liquid-under-test to determine its complex permittivity from the measured differential reflection coefficient. The proposed approach has been theoretically analyzed to derive the appropriate sensor’s model and the required calibration procedure as well as experimentally validated by measurements of three liquids having frequency-dependent complex permittivity. Moreover, measurement accuracy analysis has been provided, including the method‘s uncertainty estimation and error sources’ discussion. Finally, the measured permittivity of each liquid has been compared against tabularized data. Good agreement has been obtained proving the validity and applicability of the proposed method.
Data Cleaning-A Thorough Analysis and Survey on Unstructured Data

This is the age of data. Each and every field, ranging from academics to research, from marketing to city administration, etc. are using data to progress towards an innovative future. Though this data is used to understand trends, create insights and make better decisions, but with this also comes various challenges. The biggest challenge is the dirtiness in the data which pose the biggest hindrance to data analysis. This paper tries to explain the importance of data cleaning by working on a pollution dataset. The main focus of the paper is twofold: One is analysis and visualization of unstructured data on Air pollution. Second is the survey on methods to clean unwanted, dirty and ambiguous data.
Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers

Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresses this problem and achieves competitive performance in specific cases. We show that this algorithm can fail when the geometries of known and unknown classes differ. To overcome this problem, we propose two new algorithms relying on approximations from extreme value theory. We show the effectiveness of our classifiers in simulations and on the LETTER and MNIST data sets.
Infertile Couples Prefer Twins: Analysis of Their Reasons and Clinical Characteristics Related to This Preference

Background: The number of multiple pregnancies has been significantly increased in the last decades due to assisted reproduction techniques development. Compared to singleton, twins and multiple pregnancies are associated to more complications and risks for both mother and children. The objective of this study was to examine the proportion of patients preferring a multiple birth over a singleton after an IVF/ICSI attempt, their reasons and the influence of socio-demographic and clinical parameters on their preference. Methods: A prospective study was conducted in two different Spanish centers in 2014; a public university hospital and a private clinic, with different populations and embryo transfer policies. In order to evaluate patients and partners attitudes towards twins and singletons, an anonymous 10-question survey was conducted and 399 were invited to participate. Results: 58.2% of participants preferred having twins to having one child at a time and 4.8% preferred triplets. Primary reasons for preferring twins were “avoiding a new IVF/ICSI attempt” (61.6%), “I like the idea of having twins” (27.3%), “avoiding the waiting list” (5.8%), and “in my opinion with the latest technology, the rate and severity of complications in multiple pregnancies are low” (5.2%). The multivariate analysis showed that the only significant parameter related to a preference for multiplets was the transfer of women’s own fresh embryos (OR=3.31). Conclusion: Twin pregnancy risks are not perceived as important by the majority of IVF/ICSI couples, and many of them specifically prefer twins. In our opinion, much more information is needed highlighting the multiple pregnancy risks and that information should come from medical sources besides general media.
PsyHeal: An Approach to Remote Mental Health Monitoring System

Numerous research initiatives to develop and provide cloud-based healthcare solutions have been undertaken by academicians and industry alike. It holds major significance especially when dealing with mental health issues. Inputs taken from a patient undergoing treatment, when collected and analyzed over a time period can help the doctor in making appropriate deductions and inferences. The aim of this paper is to demonstrate the potential of using third party APIs' in creating a remote health assessment system pertaining to mental illness. In this paper, a proof-of-concept cloud based psychological assessment/ monitoring service named PsyHeal is proposed. It is a web app which interfaces with a handful of third party API's for its implementation. We first discuss the primary system design and limitations of the same. We then elaborate on some enhancements which have been added to the primary system in order to overcome the identified flaws and thus explain the final design of this web app. Future scope of the application is discussed in order to build upon the aforementioned application.
Efficient Implementation of Gaussian and Laplacian Kernels for Feature Extraction from IP Fisheye Cameras

The Gaussian kernel, its partial derivatives and the Laplacian kernel, applied at different image scales, play a very important role in image processing and in feature extraction from images. Although they have been extensively studied in the case of images acquired by projective cameras, this is not the case for cameras with fisheye lenses. This type of cameras is becoming very popular, since it exhibits a Field of View of 180 degrees. The model of fisheye image formation differs substantially from the simple projective transformation, causing straight lines to be imaged as curves. Thus the traditional kernels used for processing images acquired by projective cameras, are not optimal for fisheye images. This work uses the calibration of the acquiring fisheye camera to define a geodesic metric for distance between pixels in fisheye images and subsequently redefines the Gaussian kernel, its partial derivatives, as well as the Laplacian kernel. Finally, algorithms for applying in the spatial domain these kernels, as well as the Harris corner detector, are proposed, using efficient computational implementations. Comparative results are shown, in terms of correctness of image processing, efficiency of application for multi scale processing, as well as salient point extraction. Thus we conclude that the proposed algorithms allow the efficient application of standard processing and analysis techniques of fisheye images, in the spatial domain, once the calibration of the specific camera is available.