Miracle-dz/UniFAR-FTUs huggingface dataset:
Retinoic acid signaling controls the formation, proliferation and survival of the blastema during adult zebrafish fin regeneration

Adult teleosts rebuild amputated fins through a proliferation-dependent process called epimorphic regeneration, in which a blastema of cycling progenitor cells replaces the lost fin tissue. The genetic networks that control formation of blastema cells from formerly quiescent stump tissue and subsequent blastema function are still poorly understood. Here, we investigated the cellular and molecular consequences of genetically interfering with retinoic acid (RA) signaling for the formation of the zebrafish blastema. We show that RA signaling is upregulated within the first few hours after fin amputation in the stump mesenchyme, where it controls Fgf, Wnt/β-catenin and Igf signaling. Genetic inhibition of the RA pathway at this stage blocks blastema formation by inhibiting cell cycle entry of stump cells and impairs the formation of the basal epidermal layer, a signaling center in the wound epidermis. In the established blastema, RA signaling remains active to ensure the survival of the highly proliferative blastemal population by controlling expression of the anti-apoptotic factor bcl2. In addition, RA signaling maintains blastema proliferation through the activation of growth-stimulatory signals mediated by Fgf and Wnt/β-catenin signaling, as well as by reducing signaling through the growth-inhibitory non-canonical Wnt pathway. The endogenous roles of RA in adult vertebrate appendage regeneration are uncovered here for the first time. They provide a mechanistic framework to understand previous observations in salamanders that link endogenous sources of RA to the regeneration process itself and support the hypothesis that the RA signaling pathway is an essential component of vertebrate tissue regeneration.
Interfacial Bonding in a Nanoclay/Polymer Composite

In this study, X-ray photoelectron spectroscopy (XPS) was conducted to analyze the chemical composition between epoxy matrix and nanocomposite. This experiment revealed that a chemical bonding at an interface between the matrix and nanoclay of the composites did exist. Thus, such bonding can enhance the mechanical and thermal properties of resultant polymer composites as reported in many literatures.
A Global Relaxation Labeling Approach to Coreference Resolution

This paper presents a constraint-based graph partitioning approach to coreference resolution solved by relaxation labeling. The approach combines the strengths of groupwise classifiers and chain formation methods in one global method. Experiments show that our approach significantly outperforms systems based on separate classification and chain formation steps, and that it achieves the best results in the state of the art for the same dataset and metrics.
Learning Through Time in the Thalamocortical Loops

We present a comprehensive, novel framework for understanding how the neocortex, including the thalamocortical loops through the deep layers, can support a temporal context representation in the service of predictive learning. Many have argued that predictive learning provides a compelling, powerful source of learning signals to drive the development of human intelligence: if we constantly predict what will happen next, and learn based on the discrepancies from our predictions (error-driven learning), then we can learn to improve our predictions by developing internal representations that capture the regularities of the environment (e.g., physical laws governing the time-evolution of object motions). Our version of this idea builds upon existing work with simple recurrent networks (SRN's), which have a discretely-updated temporal context representations that are a direct copy of the prior internal state representation. We argue that this discretization of temporal context updating has a number of important computational and functional advantages, and further show how the strong alpha-frequency (10hz, 100ms cycle time) oscillations in the posterior neocortex could reflect this temporal context updating. We examine a wide range of data from biology to behavior through the lens of this LeabraTI model, and find that it provides a unified account of a number of otherwise disconnected findings, all of which converge to support this new model of neocortical learning and processing. We describe an implemented model showing how predictive learning of tumbling object trajectories can facilitate object recognition with cluttered backgrounds.
Structured Sparsity with Group-Graph Regularization

In many learning tasks with structural properties, structural sparsity methods help induce sparse models, usually leading to better interpretability and higher generalization performance. One popular approach is to use group sparsity regularization that enforces sparsity on the clustered groups of features, while another popular approach is to adopt graph sparsity regularization that considers sparsity on the link structure of graph embedded features. Both the group and graph structural properties co-exist in many applications. However, group sparsity and graph sparsity have not been considered simultaneously yet. In this paper, we propose a g2-regularization that takes group and graph sparsity into joint consideration, and present an effective approach for its optimization. Experiments on both synthetic and real data show that, enforcing group-graph sparsity lead to better performance than using group sparsity or graph sparsity only.
Research Progress of Molecular Imprinting Technology

Molecular imprinting technology（MIT）is a kind of new-emerging polymerizing technology with specific selectivity and affinity ability, An elucidation on the basic principles, and the broad applications of molecularly imprinted polymers (MIPs) in some fields are given in this paper. It also gives an overview of some recent development in the field and offers an insight into how the area is likely to evolve in the future.
Identification of a serine protease inhibitor which causes inclusion vacuole reduction and is lethal to Chlamydia trachomatis

The mechanistic details of the pathogenesis of Chlamydia, an obligate intracellular pathogen of global importance, have eluded scientists due to the scarcity of traditional molecular genetic tools to investigate this organism. Here we report a chemical biology strategy that has uncovered the first essential protease for this organism. Identification and application of a unique CtHtrA inhibitor (JO146) to cultures of Chlamydia resulted in a complete loss of viable elementary body formation. JO146 treatment during the replicative phase of development resulted in a loss of Chlamydia cell morphology, diminishing inclusion size, and ultimate loss of inclusions from the host cells. This completely prevented the formation of viable Chlamydia elementary bodies. In addition to its effect on the human Chlamydia trachomatis strain, JO146 inhibited the viability of the mouse strain, Chlamydia muridarum, both in vitro and in vivo. Thus, we report a chemical biology approach to establish an essential role for Chlamydia CtHtrA. The function of CtHtrA for Chlamydia appears to be essential for maintenance of cell morphology during replicative the phase and these findings provide proof of concept that proteases can be targeted for antimicrobial therapy for intracellular pathogens.
Dynein Intermediate Chain Mediated Dynein–Dynactin Interaction Is Required for Interphase Microtubule Organization and Centrosome Replication and Separation inDictyostelium

Cytoplasmic dynein intermediate chain (IC) mediates dynein–dynactin interaction in vitro (Karki, S., and E.L. Holzbaur. 1995. J. Biol. Chem. 270:28806–28811; Vaughan, K.T., and R.B. Vallee. 1995. J. Cell Biol. 131:1507–1516). To investigate the physiological role of IC and dynein–dynactin interaction, we expressed IC truncations in wild-type Dictyostelium cells. ICΔC associated with dynactin but not with dynein heavy chain, whereas ICΔN truncations bound to dynein but bound dynactin poorly. Both mutations resulted in abnormal localization to the Golgi complex, confirming dynein function was disrupted. Striking disorganization of interphase microtubule (MT) networks was observed when mutant expression was induced. In a majority of cells, the MT networks collapsed into large bundles. We also observed cells with multiple cytoplasmic asters and MTs lacking an organizing center. These cells accumulated abnormal DNA content, suggesting a defect in mitosis. Striking defects in centrosome morphology were also observed in IC mutants, mostly larger than normal centrosomes. Ultrastructural analysis of centrosomes in IC mutants showed interphase accumulation of large centrosomes typical of prophase as well as unusually paired centrosomes, suggesting defects in centrosome replication and separation. These results suggest that dynactin-mediated cytoplasmic dynein function is required for the proper organization of interphase MT network as well as centrosome replication and separation in Dictyostelium.
Differential Subcellular Localization of Protein Phosphatase-1 α, γ1, and δ Isoforms during Both Interphase and Mitosis in Mammalian Cells

Protein phosphatase-1 (PP-1) is involved in the regulation of numerous metabolic processes in mammalian cells. The major isoforms of PP-1, α, γ1, and δ, have nearly identical catalytic domains, but they vary in sequence at their extreme NH2 and COOH termini. With specific antibodies raised against the unique COOH-terminal sequence of each isoform, we find that the three PP-1 isoforms are each expressed in all mammalian cells tested, but that they localize within these cells in a strikingly distinct and characteristic manner. Each isoform is present both within the cytoplasm and in the nucleus during interphase. Within the nucleus, PP-1 α associates with the nuclear matrix, PP-1 γ1 concentrates in nucleoli in association with RNA, and PP-1 δ localizes to nonnucleolar whole chromatin. During mitosis, PP-1 α is localized to the centrosome, PP-1 γ1 is associated with microtubules of the mitotic spindle, and PP-1 δ strongly associates with chromosomes. We conclude that PP-1 isoforms are targeted to strikingly distinct and independent sites in the cell, permitting unique and independent roles for each of the isoforms in regulating discrete cellular processes.
Improved estimation of controlled direct effects in the presence of unmeasured confounding of intermediate variables

Adjusting for a causal intermediate is a common analytic strategy for estimating an average causal direct effect (ACDE). The ACDE is the component of the total exposure effect that is not relayed through the specified intermediate. Even if the total effect is unconfounded, the usual ACDE estimate may be biased when an unmeasured variable affects the intermediate and outcome variables. Using linear programming optimization to compute non‐parametric bounds, we develop new ACDE estimators for binary measured variables in this causal structure, and use root mean square confounding bias (RMSB) to compare their performance with the usual stratified estimator in simulated distributions of target populations comprised of the 64 possible potential response types as well as distributions of target populations restricted to subsets of 18 or 12 potential response types defined by monotonicity or no‐interactions assumptions of unit‐level causal effects. We also consider target population distributions conditioned on fixed outcome risk among the unexposed, or fixed true ACDE in one stratum of the intermediate. Results show that a midpoint estimator constructed from the optimization bounds has consistently lower RMSB than the usual stratified estimator both unconditionally and conditioned on any risk in the unexposed. When conditioning on true ACDE, this midpoint estimator performs more poorly only when conditioned on an extreme true ACDE in one stratum of the intermediate, yet outperforms the stratified estimator in the other stratum when interaction is permitted. An alternate ‘limit‐modified crude’ estimator can never perform less favourably than the stratified estimator, and often has lower RMSB. Copyright © 2005 John Wiley & Sons, Ltd.
Evaluation of English intonation based on combination of multiple evaluation scores

In this paper, we proposed a novel method for evaluating in-tonation of an English utterance spoken by a learner for into-nation learning by a CALL system. The proposed method is based on an intonation evaluation method proposed by Suzuki et al. , which uses “word importance factors,” which are calculated based on word clusters given by a decision tree. We extended Suzuki (cid:777) s method so that multiple decision trees are used and the resulting intonation scores are combined using multiple regression. As a result of an experiment, we obtained correlation coefﬁcient comparable to the correlation between human raters.
An Escherichia coli Mutant Lacking the Cold Shock-induced Palmitoleoyltransferase of Lipid A Biosynthesis

An acyltransferase induced by cold shock inEscherichia coli, designated LpxP, incorporates a palmitoleoyl moiety into nascent lipid A in place of the secondary laurate chain normally added by LpxL(HtrB) (Carty, S. M., Sreekumar, K. R., and Raetz, C. R. H. (1999)J. Biol. Chem. 274, 9677–9685). To determine whether the palmitoleoyl residue alters the properties of the outer membrane and imparts physiological benefits at low growth temperatures, we constructed a chromosomal insertion mutation in lpxP, the structural gene for the transferase. Membranes from thelpxP mutant MKV11 grown at 12 °C lacked the cold-induced palmitoleoyltransferase present in membranes of cold-shocked wild type cells but retained normal levels of the constitutive lauroyltransferase encoded by lpxL. When examined by mass spectrometry, about two-thirds of the lipid A molecules isolated from wild type E. coli grown at 12 °C contained palmitoleate in place of laurate, whereas the lipid A of cold-adapted MKV11 contained only laurate in amounts comparable with those seen in wild type cells grown at 30 °C or above. To probe the integrity of the outer membrane, MKV11 and an isogenic wild type strain were grown at 30 or 12 °C and then tested for their susceptibility to antibiotics. MKV11 exhibited a 10-fold increase in sensitivity to rifampicin and vancomycin at 12 °C compared with wild type cells but showed identical resistance when grown at 30 °C. We suggest that the palmitoleoyltransferase may confer a selective advantage upon E. coli cells growing at lower temperatures by making the outer membrane a more effective barrier to harmful chemicals.
The impact of specific occupation on mortality in the U.S. National Longitudinal Mortality Study

We compare mortality differences for specific and general categories of occupations using a national cohort of approximately 380,000 persons aged 25-64 from the U.S. National Longitudinal Mortality Study. Based on comparisons of relative risk obtained from Cox proportional-hazards model analyses, higher risk is observed in moving across the occupational spectrum from the technical, highly skilled occupations to less-skilled and generally more labor-intensive occupations. Mortality differences obtained for social status groups of specific occupations are almost completely accounted for by adjustments for income and education. Important differences are shown to exist for selected specific occupations beyond those accounted for by social status, income, and education. Highrisk specific occupations include taxi drivers, cooks, longshoremen, and transportation operatives. Low-risk specific occupations include lawyers, natural scientists, teachers, farmers, and a variety of engineers.
Ontology-based Adaptive Systems of Cyber Defense

In this paper we outline a holistic approach for understanding and simulating human decision making in knowledge-intensive tasks. To this purpose, we integrate semantic and cognitive models in a hybrid computational architecture. The contribution of the paper is twofold: first we describe a packetcentric ontology to represent network traffic. We show how the ontology is used to describe real-world network traffic and also serve as a basis for higher level ontologies of cyber operation, threat and risk. Second, we demonstrate how the combination of the packet-centric ontology with an adaptive cognitive agent with learning capabilities, can be used to understand the human defender reasoning processes when monitoring network traffic. Through simulation experiments we evaluated the proposed hybrid computational architecture and demonstrate its ability to successfully detect malicious port scanning within legitimate network traffic. We discuss the implications of these findings for improving our understanding of the cognitive processes and knowledge requirements of the cyber defender, as well as the possible use of the hybrid architecture as a cognitively inspired decision support tool.
A common variant association study reveals novel susceptibility loci for low HDL‐cholesterol levels in ethnic Arabs

The genetic susceptibility to acquiring low high density lipoprotein‐cholesterol (LHDLC) levels is not completely elucidated yet. In this study, we performed a common variant association study for harboring this trait in ethnic Arabs. We employed the Affymetrix high‐density Axiom Genome‐Wide ASI Array (Asian population) providing a coverage of 598,000 single nucleotide variations (SNPs) to genotype 5495 individuals in a two‐phase study involving discovery and validation sets of experiments. The rs1800775 [1.31 (1.22–1.42); p = 3.41E‐12] in the CETP gene and rs359027 [1.26 (1.16–1.36); p = 2.55E‐08] in the LMCD1 gene were significantly associated with LHDLC levels. Furthermore, rs3104435 [1.26 (1.15–1.38); p = 1.19E‐06] at the MATN1 locus, rs9835344 [1.16 (1.08–1.26); p = 8.75E‐06] in the CNTN6 gene, rs1559997 [1.3 (1.14–1.47); p = 9.48E‐06] in the SDS gene and rs1670273 [1.2 (1.1–1.31); p = 4.81E‐06] in the DMN/SYNM gene exhibited suggestive association with the disorder. Seven other variants including rs1147169 in the PLCL1 gene, rs10248618 in the DNAH11, rs476155 in the GLIS3, rs7024300 in the ABCA1, intergenic rs10836699, rs11603691 in P2RX3 and rs750134 in CORO1C gene exhibited borderline protective properties. Validation and joint meta‐analysis resulted in rs1800775, rs3104435 and rs359027 retaining their predisposing properties, while rs10836699 and rs11603691 showed protective properties. Our data show several predisposing variants across the genome for LHDLC levels in ethnic Arabs.
Practical and Secure Software-Based Attestation

Software-based attestation can be used for guaranteed detection of any active malware on a device. This promises a significant advance in the battle against malware, including mobile malware. However, most software based attestation methods are either heuristic or unsuitable for mobile computing – and often both. One recent software-based attestation method uses so-called memory-printing to produce a software-based attestation technique with provable properties. We describe a novel memory-printing algorithm that improves on that work by being more than an order of magnitude faster, while avoiding commonly used and questionable security assumptions. This results in a truly practical and arguable secure solution – taking less than 3 seconds on a 600 MHz processor with 256 MB RAM. Our work finds applications to malware defense and trusted computing in general, and mobile malware defense in particular.
Improper Adjustment for Baseline in Genetic Association Studies of Change in Phenotype

Objective: In studies of associations between genetic factors and outcomes where change in phenotype is of interest, proper modeling of the data, particularly the treatment of baseline trait values, is required to draw valid conclusions. Methods: The authors compared models of blood pressure response to a cold pressor test with and without inclusion of baseline blood pressure as a regressor and evaluate the resultant biases. Results: Adjustment for baseline presents a potential source of bias for assessment of genotype-phenotype associations. This bias was observed to occur both under the absence of a true effect, as well when a relation between genotype and change in phenotype was simulated. In simulations that incorporated measurement error, estimates were as great as two fold the true parameter values when unmeasured confounding was a factor. Conclusions: Adjusting for baseline introduces bias in genetic association studies when change in phenotype is the outcome of interest. Model misspecification bias may impact inference and provide one possible source of non-replication of findings in the literature.
Accurate Detection of Abnormal Tissues of Brain MR Images Using Hybrid Clustering and Deep Learning Based Classifier Methods of Image Segmentation

: Image analysis by segmentation techniques has wide range of utilities. The research on image analysis includes industrial inspection, object tracking from multiple sequential images, traffic control systems, geographical classification from satellite images, geomorphological mapping at land surfaces, crop analysis in agriculture, treatment planning in medical science and many others. Segmentation of an image is very significant in medical field by improving treatment possibilities like early diagnosis of abnormalities in brain. Manual segmentation for detection of abnormal tissues from large amount of brain MR images are difficult and time-consuming task. In this research paper, brain MR images are taken due to its quality parameters for the purpose of detecting abnormalities compared to other techniques like X-Ray, CT Scan etc. This paper reviews the trade-off between various approaches for analysing the abnormalities from brain Magnetic Resonance Images using different methods of image segmentation. In addition, the proposed study has given a roadmap to improve accuracy and precision to detect the region of interest. From multiple methods of segmentation, segmentation using hybrid clustering approach including K-means and Fuzzy C-means followed by deep learning based classifier method improves the level of precision to detect abnormal tissues from brain MR images. The result by implementing the proposed approach in MATLAB environment leads to plan precise treatment for the medical practitioners.
Theory of dependence values

A new model to evaluate dependencies in data mining problems is presented and discussed. The well-known concept of the association rule is replaced by the new definition of dependence value, which is a single real number uniquely associated with a given itemset. Knowledge of dependence values is sufficient to describe all the dependencies characterizing a given data mining problem. The dependence value of an itemset is the difference between the occurrence probability of the itemset and a corresponding “maximum independence estimate. ” This can be determined as a function of joint probabilities of the subsets of the itemset being considered by maximizing a suitable entropy function. So it is possible to separate in an itemset of cardinaltiy k the dependence inherited from its subsets of cardinality (k − 1) and the specific inherent dependence of that itemset. The absolute value of the difference between the probability p(i) of the event i that indicates the prescence of the itemset {a,b,. } and its maximum independence estimate is constant for any combination of values of Q &angl0; a,b,. &angr0; Q. In1paddition, the Boolean function specifying the combination of values for which the dependence is positive is a parity function. So the determination of such combinations is immediate. The model appears to be simple and powerful.