Miracle-dz/UniFAR-FTUs huggingface dataset:
Krill Oil Ameliorates Mitochondrial Dysfunctions in Rats Treated with High-Fat Diet

In recent years, several studies focused their attention on the role of dietary fats in the pathogenesis of hepatic steatosis. It has been demonstrated that a high-fat diet is able to induce hyperglycemia, hyperinsulinemia, obesity, and nonalcoholic fatty liver disease. On the other hand, krill oil, a novel dietary supplement of n-3 PUFAs, has the ability to improve lipid and glucose metabolism, exerting possible protective effects against hepatic steatosis. In this study we have investigated the effects of krill oil on mitochondrial energetic metabolism in animals fed a high-fat diet. To this end, male Sprague-Dawley rats were divided into three groups and fed for 4 weeks with a standard diet (control group), a diet with 35% fat (HF group), or a high-fat diet supplemented with 2.5% krill oil (HF+KO group). The obtained results suggest that krill oil promotes the burning of fat excess introduced by the high-fat diet. This effect is obtained by stimulating mitochondrial metabolic pathways such as fatty acid oxidation, Krebs cycle, and respiratory chain complexes activity. Modulation of the expression of carrier proteins involved in mitochondrial uncoupling was also observed. Overall, krill oil counteracts the negative effects of a high-fat diet on mitochondrial energetic metabolism.
A Genetic Assay for Transcription Errors Reveals Multilayer Control of RNA Polymerase II Fidelity

We developed a highly sensitive assay to detect transcription errors in vivo. The assay is based on suppression of a missense mutation in the active site tyrosine in the Cre recombinase. Because Cre acts as tetramer, background from translation errors are negligible. Functional Cre resulting from rare transcription errors that restore the tyrosine codon can be detected by Cre-dependent rearrangement of reporter genes. Hence, transient transcription errors are captured as stable genetic changes. We used this Cre-based reporter to screen for mutations of Saccharomyces cerevisiae RPB1 (RPO21) that increase the level of misincorporation during transcription. The mutations are in three domains of Rpb1, the trigger loop, the bridge helix, and in sites involved in binding to TFIIS. Biochemical characterization demonstrates that these variants have elevated misincorporation, and/or ability to extend mispaired bases, or defects in TFIIS mediated editing.
The crying shame of robot nannies: An ethical appraisal

Childcare robots are being manufactured and developed with the long term aim of creating surrogate carers. While total childcare is not yet being promoted, there are indications that it is ‘on the cards’. We examine recent research and developments in childcare robots and speculate on progress over the coming years by extrapolating from other ongoing robotics work. Our main aim is to raise ethical questions about the part or full-time replacement of primary carers. The questions are about human rights, privacy, robot use of restraint, deception of children and accountability. But the most pressing ethical issues throughout the paper concern the consequences for the psychological and emotional wellbeing of children. We set these in the context of the child development literature on the pathology and causes of attachment disorders. We then consider the adequacy of current legislation and international ethical guidelines on the protection of children from the overuse of robot care.
A security framework for brokerless publish subscribe system using identity based signcryption

Publish subscribe communication paradigm has been recognized as the most promising communication paradigms for information dissemination in large scale distributed environment. It has been used in a variety of applications ranging from personalized information dissemination to a wide range of group communications. So publish subscribe system needs to fulfill the basic security demands of these information dissemination applications. Confidentiality, authentication, data integrity and non repudiation are the most important security services in security environment. Encryption and signature are the basic cryptographic tools for achieving these security services in communication system. This paper presents a novel approach to provide all the basic aspects of security such as confidentiality, authentication, integrity and Non-repudiation in broker-less content based publish subscribe system along with forward secrecy and unforgeability. The scheme use identity based signcryption technique and that can effectively reduces the computational cost and communication overhead.
Decision Support for Handling Gaps between ERP Functionalities and Organizational Needs: A Goal Reasoning Based Approach

In the process of selecting commercial off-the-shelf (COTS) ERP systems, it is inevitable to encounter gaps between ERP software and organizational needs. Gaps occur when COTS attributes do not exactly match organizational needs, and they make organizational users and software vendors in the dilemma: whether to adapt as-is business processes or to customize software packages. A decision support method for handling gaps is proposed in this paper. Firstly, organizational goal-process models are built as the base for decision making. Secondly, the idea of evidence theory is introduced into goal reasoning and a quantified algorithm is proposed to evaluate the impacts of different business processes on high level goals which are critical to make decision. At last, the risk is discussed to rank intervals which specify the expected utilization of business processes.
C/EBPβ Modulates the Early Events of Keratinocyte Differentiation Involving Growth Arrest and Keratin 1 and Keratin 10 Expression

ABSTRACT The epidermis is a stratified squamous epithelium composed primarily of keratinocytes that become postmitotic and undergo sequential changes in gene expression during terminal differentiation. The expression of the transcription factor CCAAT/enhancer binding protein β (C/EBPβ) within mouse epidermis and primary keratinocytes has recently been described; however, the function of C/EBPβ within the epidermal keratinocyte is unknown. We report here that transient transfection of mouse primary keratinocytes with a C/EBP-responsive promoter-reporter construct resulted in a sevenfold increase in luciferase activity when keratinocytes were switched to culture conditions that induce growth arrest and differentiation. Forced expression of C/EBPβ in BALB/MK2 keratinocytes inhibited growth, induced morphological changes consistent with a more differentiated phenotype, and upregulated two early markers of differentiation, keratin 1 (K1) and keratin 10 (K10) but had a minimal effect on the expression of late-stage markers, loricrin and involucrin. Analysis of the epidermis of C/EBPβ-deficient mice revealed a mild epidermal hyperplasia and decreased expression of K1 and K10 but not of involucrin and loricrin. C/EBPβ-deficient primary keratinocytes were partially resistant to calcium-induced growth arrest. Analysis of terminally differentiated spontaneously detached keratinocytes or those induced to differentiate by suspension culture revealed that C/EBPβ-deficient keratinocytes displayed striking decreases in K1 and K10, while expression of later-stage markers was only minimally altered. Our results demonstrate that C/EBPβ plays an important role in the early events of stratified squamous differentiation in keratinocytes involving growth arrest and K1 and K10 expression.
Automatically Gradient Threshold Estimation of Anisotropic Diffusion for Meyer's Watershed Algorithm Based Optimal Segmentation

Medical image segmentation is a fundamental task in the medical imaging field. Optimal segmentation is required for the accurate judgment or appropriate clinical diagnosis. In this paper, we proposed automatically gradient threshold estimator of anisotropic diffusion for Meyer's Watershed algorithm based optimal segmentation. The Meyer's Watershed algorithm is the most significant for a large number of regions separations but the over segmentation is the major drawback of the Meyer's Watershed algorithm. We are able to remove over segmentation after using anisotropic diffusion as a preprocessing step of segmentation in the Meyer's Watershed algorithm. We used a fixed window size for dynamically gradient threshold estimation. The gradient threshold is the most important parameter of the anisotropic diffusion for image smoothing. The proposed method is able to segment medical image accurately because of obtaining the enhancement image. The introducing method demonstrates better performance without loss of any clinical information while preserving edges. Our investigated method is more efficient and effective in order to segment the region of interests in the medical images indeed.
Molecular basis of selective antagonism of the P2X1 receptor for ATP by NF449 and suramin: contribution of basic amino acids in the cysteine-rich loop

BACKGROUND AND PURPOSE The cysteine‐rich head region, which is adjacent to the proposed ATP‐binding pocket in the extracellular ligand‐binding loop of P2X receptors for ATP, is absent in the antagonist‐insensitive Dictyostelium receptors. In this study we have determined the contribution of the head region to the antagonist action of NF449 and suramin at the human P2X1 receptor.
An Interactive Tool for Outdoor Computer Controlled Cultivation of Microalgae in a Tubular Photobioreactor System

This paper describes an interactive virtual laboratory for experimenting with an outdoor tubular photobioreactor (henceforth PBR for short). This virtual laboratory it makes possible to: (a) accurately reproduce the structure of a real plant (the PBR designed and built by the Department of Chemical Engineering of the University of Almería, Spain); (b) simulate a generic tubular PBR by changing the PBR geometry; (c) simulate the effects of changing different operating parameters such as the conditions of the culture (pH, biomass concentration, dissolved O2, inyected CO2, etc.); (d) simulate the PBR in its environmental context; it is possible to change the geographic location of the system or the solar irradiation profile; (e) apply different control strategies to adjust different variables such as the CO2 injection, culture circulation rate or culture temperature in order to maximize the biomass production; (f) simulate the harvesting. In this way, users can learn in an intuitive way how productivity is affected by any change in the design. It facilitates the learning of how to manipulate essential variables for microalgae growth to design an optimal PBR. The simulator has been developed with Easy Java Simulations, a freeware open-source tool developed in Java, specifically designed for the creation of interactive dynamic simulations.
Comparison of feature extraction and feature selection approaches to decide whether a face image belongs to a male or a female

In this study, a gender recognition system which only uses face images was proposed. Since the dimension of the face images were huge and different from each other; the number of features should be decreased. In order to decrease the dimension of the images Principal Component Analysis (PCA) and a hybrid aprproach combined by PCA+SFS (Sequential Forward Selection) has been presented and their performances were compared with each other. Via PCA and PCA+SFS hybrid method, the dimension of the dataset was reduced and the proposed system was trained and tested by Support Vector Machine (SVM). The classification results of two dimension reduction approaches according to the extracted features were evaulated via SVM (Support Vector Machines) and the classification results were compared.
Predicting muscular dystrophy with sequence based features for point mutations

Hefty amounts of biological data are accumulated for research with the advancement of sequencing technologies. Genetic diseases are caused by the deformity in the inherited genes. Identifying trait diseases through DNA analysis is a prime task in diagnosing an ailment. Identification of disease based on mutations in the gene sequences is an essential and challenging task in the medical diagnosis of genetic disorders such as Muscular dystrophy. Muscular dystrophy is a rare disease that alters the structure and nature of the muscles that deteriorate the musculoskeletal system and hinder locomotion. There are nine major kinds of muscular dystrophy and it is vital to identify the type of muscular dystrophy for proper diagnosis and treatment. Hence a new model is proposed for predicting the disease accurately with the gene sequences, which are mutated by adopting an approach like positional cloning on the reference cDNA sequence. This paper addresses the problem by considering mutated gene sequences of fifty five genes that causes five types of muscular dystrophy and developing an efficient pattern recognition model using supervised pattern classification technique. The resultant the trained model shows the prediction accuracy of 100% by estimating using 10-fold cross validation.
Localization Using Multisensor Fusion of IMM Fixed Lag Smoother in a Cricket Sensor Network

We propose an approach for estimating the location of target moving over a time delayed sensor field via a distributed interacting multiple model fixed-lag smoother in conjunction with multiple pseudo measurements obtained using a trilateration positioning algorithm. Proposed pseudo measurements that indirectly measure the target position are defined using the results of trilateration positioning method, in a Cricket sensor network, as single nodes only can measure the distance to the target, the nodes need to form groups of three to be able to perform trilateration. Also, since the trilateration method ignores distance-measurement errors, adequate error covariance information is estimated for pseudo measurement model using a self-tuning method. Each distinct group makes local pseudo measurements with a time delay and fused using matrix weight fusion. Based on these algorithms, localization performance enhancement of a moving target is achieved.
Redundant Wavelet Transform in Video Signal Processing

In this paper, the authors conduct a review on redundant wavelet transform (RDWT) in video signal processing. As an over-complete version of discrete wavelet transform (DWT), RDWT provides several advantages over the traditional DWT. In video coding area, we utilize its shift-invariant property and its phase-diverse multihypothesis, which are not provided by DWT, to build high performance video coding system. While in video watermarking area, the RDWT provides a more accurate estimation on locations where watermarks can be embedded. Thus, using RDWT, we can embed more watermarks than DWT approach without harming the non-perceivable requirement. After exploring several successful applications in video coding and video watermarking areas, the authors give a discuss on some future issues.
"Analysis of Directional Features - Stroke and Contour for Handwritten Character Recognition"

Machine simulation of human functions like recognition of the text is a challenging task. The Off-line Handwritten Character Recognition requires more research to reach the ultimate goal of machine recognition of the text. An attempt is made towards English language by a large number of researchers since six decades. But for Indian languages it is still a dream. We propose a method on offline isolated English character. The method is also applied to Marathi vowels. The image acquired is preprocessed to remove all unwanted details from the image so that the image is suitable for feature extraction. Feature extraction plays an important role in handwritten recognition. The two feature extraction methods based on directional features are considered. The first method uses stroke distribution of a character. The second method uses contour extraction. The Two directional features are compared with two different correlation techniques separately to check the suitability of the recognition method. First correlation technique calculates the dissimilarity between reference pattern and test pattern, and the other calculates the similarity between reference pattern and test pattern. The result of the comparison is to classify the character under consideration to a class if hit. If miss, the confusion information is extracted for the analysis.
Improving the Selection Error Recognition in a Chinese Grammar Error Detection System

In this paper we report how we improve our Chinese grammar error detection system. We focus on the recognition of word selection error, which is the hardest error type defined in 2015 NLP-TEA shared task CGED. Four major error types was defined in CGED shared task, including redundant word error, missing word error, word selection error and word disorder error. Based on the conditional random fields (CRF) model, our system trains a linear tagger that can be used to recognize the errors in learners' essays. In 2015 CGED shared task, we have found that our system can achieve high precision and accuracy. Our previous system used the features in the sentence only, however, it is not sufficient to recognize the word selection error. In this paper, we propose that by integrating the collocation feature into the CRF model, our system can improve the performance on the recognition of the selection errors.
The Maximum Likelihood Approach to Complex ICA

We derive the form of the best non-linear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case, and that they coincide with the real case. We discuss several special cases for the score function as well as adaptive scores.
Entity Typing : A Critical Step for Mining Structures from Massive Unstructured Text

We have been studying learning and mining graphs or networks. However, where do most real networks come from? Although some networks come from well-structured and explicitly connected nodes and links, a majority of networks come from massive unstructured text data, and it takes human efforts to extract them and build them explicitly. Unfortunately, manual data curation and extraction of structures from unstructured data can be costly, unscalable, and error-prone. We have been investigating a data-driven approach to building structured networks from unstructured text data. First, quality phrases can be mined from massive text corpus, serving as basic semantic units, mostly being entities. Second, types can be inferred for such entities from such massive text data with distant supervision and relationships among entities can be uncovered by network embedding as well. Therefore, entity typing is a critical step for mining structures from unstructured text data. In this study, we focus on how to conduct entity typing with a data-driven approach. We show that “rough” entity types can be identified from massive text data with a distant supervision approach via some domain-independent knowledge-bases. However, for refined typing, even the type labels in a knowledge bases can be noisy (i.e., incorrect for the entity mention’s local context). We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type-path for each training example in a top-down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 20XX ACM X-XXXXX-XX-X/XX/XX . $15.00.
High-Coverage Security Testing for Windows Kernel Drivers

In Windows kernel drivers, different function paths will be called according to the DeviceIoControlCode parameter in DeviceIoControl request. In order to achieve high-coverage security testing of these function paths, a new testing method using symbolic testing is proposed in this paper. With the automatic detection of Dispatch routine, symbolic testing is used to analyse the Dispatch function, walk through all supported function paths and get all DeviceIoControlCodes and check constraints. More specific test cases are generated with the guide of the codes and constraints. To compare the coverage of function paths, drivers of six famous security software are tested. Traditional testing method performs an average of 35% coverage, while ours performs 90%. Our tool discovers a previously-undiscovered priviledge escalation vulnerability of BitDefender2012 and some denial-of-service vulnerabilities, which prove the validity of our method.
Collateral and debt maturity choice: A signaling model

This paper derives optimal loan policies under asymmetric information where banks offer loan contracts of long and short duration, backed or unbacked with collateral. The main novelty of the paper is that it analyzes a setting in which high quality firms use collateral as a complementary device along with debt maturity to signal their superiority. The least-cost signaling equilibrium depends on the relative costs of the signaling devices, the difference in firm quality and the proportion of good firms in the market. Model simulations suggest a non-monotonic relationship between firm quality and debt maturity, in which high quality firms have both long-term secured debt and short-term secured or non-secured debt.
Z-module reasoning: an equality-oriented proving method with built-in ring axioms

A Z-module reasoning method has been formulated for equality-oriented theoremproving in basic ring theories. This method incorporates equality theory and a set of basic axioms of rings into inference rules based on linearization, identity paramodulation, and integer Gaussimr elimination. Z-module reasoning proves a theorem by means of two distinct types of reasoning. One type of reasoning employs paramodulation-based deduction to deduce a set of identity vectors from a denial of the theorem. The other type of reasomng employs integer array manipulation to calculate the truth of the theorem in terms of the Z-module generated by these vectors. This paper is devoted to a formal description of Z-module reasoning, including motivation and background, formal defimtion, soundness and completeness theorems, the finite hnearization property on standard polynomial sets, and the existence of a decisional Z-module reasoning prover for homogeneous equational sets. The relation of Z-module reasoning with other work and issues concerning the refinements and future research of the method E also discussed.