Miracle-dz/UniFAR-FTUs huggingface dataset:
Thrombophilia in pregnancy: a systematic review

Growing evidence suggests that thrombophilia is associated with venous thromboembolism (VTE) and adverse pregnancy outcomes. However, methodological limitations have made it difficult to obtain a clear overview of the overall risks. We conducted a systematic review to determine the risk of VTE and adverse pregnancy outcomes associated with thrombophilia in pregnancy. The effectiveness of prophylactic interventions during pregnancy was also evaluated. Major electronic databases were searched, relevant data abstracted and study quality assessed by two independent reviewers. Odds ratios (ORs) stratified by thrombophilia type were calculated for each outcome. A total of 79 studies were included in our review. The risks for individual thrombophilic defects were determined for VTE (ORs, 0·74–34·40); early pregnancy loss (ORs, 1·40–6·25); late pregnancy loss (ORs, 1·31–20·09); pre‐eclampsia (ORs, 1·37–3·49); placental abruption (ORs, 1·42–7·71) and intrauterine growth restriction (ORs, 1·24–2·92). Low‐dose aspirin plus heparin was the most effective in preventing pregnancy loss in thrombophilic women (OR, 1·62). Our findings confirm that women with thrombophilia are at risk of developing VTE and complications in pregnancy. However, despite the increase in relative risk, the absolute risk of VTE and adverse outcomes remains low. There is also a lack of controlled trials of antithrombotic intervention to prevent pregnancy complications. Thus, at present, universal screening for thrombophilia in pregnancy cannot be justified clinically.
Potential of bone marrow mesenchymal stem cells in management of Alzheimer's disease in female rats

Alzheimer's disease (AD) has been called the disease of the century with significant clinical and socioeconomic impacts. Pharmacological treatment has limited efficacy and only provides symptomatic relief without long‐term cure. Accordingly, there is an urgent need to develop novel and effective medications for AD. Stem cell‐based therapy is a promising approach to handling neurodegenerative diseases. Therefore, the current study aimed to explore the possible therapeutic role of single intravenous injection of bone marrow derived mesenchymal stem cells (BM‐MSCs) after 4 months in management of AD in the experimental model. The work also extended to compare the therapeutic potential of BM‐MSCs with 2 conventional therapies of AD; rivastigmine and cerebrolysin administered daily. BM‐MSCs were able to home at the injured brains and produced significant increases in the number of positive cells for choline acetyltransferase (ChAT) and survivin expression, as well as selective AD indicator‐1 (seladin‐1) and nestin gene expression. Histopathological examination indicated that BM‐MSCs could remove beta‐amyloid plaques from hippocampus. Significant improvement in these biomarkers was similar to or better sometimes than the reference drugs, clearly showing the potential therapeutic role of BM‐MSCs against AD through their anti‐apoptotic, neurogenic and immunomodulatory properties.
A Penalized-Likelihood Method to Estimate the Distribution of Selection Coefficients from Phylogenetic Data

We develop a maximum penalized-likelihood (MPL) method to estimate the fitnesses of amino acids and the distribution of selection coefficients (S = 2Ns) in protein-coding genes from phylogenetic data. This improves on a previous maximum-likelihood method. Various penalty functions are used to penalize extreme estimates of the fitnesses, thus correcting overfitting by the previous method. Using a combination of computer simulation and real data analysis, we evaluate the effect of the various penalties on the estimation of the fitnesses and the distribution of S. We show the new method regularizes the estimates of the fitnesses for small, relatively uninformative data sets, but it can still recover the large proportion of deleterious mutations when present in simulated data. Computer simulations indicate that as the number of taxa in the phylogeny or the level of sequence divergence increases, the distribution of S can be more accurately estimated. Furthermore, the strength of the penalty can be varied to study how informative a particular data set is about the distribution of S. We analyze three protein-coding genes (the chloroplast rubisco protein, mammal mitochondrial proteins, and an influenza virus polymerase) and show the new method recovers a large proportion of deleterious mutations in these data, even under strong penalties, confirming the distribution of S is bimodal in these real data. We recommend the use of the new MPL approach for the estimation of the distribution of S in species phylogenies of protein-coding genes.
A-LMST: An Adaptive LMST Local Topology Control Algorithm for Mobile Ad Hoc Networks

The pattern of mobile nodes movement and node removals or additions cause frequent and unpredictable changes in the topology of mobile ad hoc networks. Network performance can thus vary significantly under different mobility models, as well as from the variation in parameters of a given mobility model, such as node movement speed and number of nodes in the network. Hence, the efficiency of a network depends not only on its control protocols, but also on its topology. In this paper, we first compare the impact that mobility parameter variations produces on typical network metrics - routing packets generated, routing overhead and route discovery average delay - in networks with and without topology control. Results from experiments in a wireless network simulator show that, in networks with topology control using 2 or 3 hops for route discovery depending on the node degree, the number of routing packets decreases, therefore improving the operation and efficiency of the network. Based on these results, a local adaptive self-configuration LMST topology control was then proposed and analysed, producing better results as the network size increases.
Classifying handheld Augmented Reality: Three categories linked by spatial mappings

Handheld Augmented Reality (AR) relies on a spatial coupling of the on-screen content with the physical surrounding. To help the design of such systems and to classify existing AR systems, we present a framework made of three categories and two spatial relationships. Our framework highlights spatial relationships between the physical world, the representation of the physical world on screen and the augmentation on screen. Within this framework, we study the relaxing of the spatial coupling between the digital information and the physical surrounding in order to enhance interaction by breaking the constraints of physical world interaction.
Efficient Data Embedding TechniqueComparison Based On LSB Replacement andPixel Pair Matching Method

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quan tized Gaussian random matrix, are unknown. An adaptive statis tical test is designed such that its probability distribution is always independent of the unknown image parameters, while ensuring a high probability of hidden bits detection. This test is based on the likelihood ratio test except that the unknown parameters are re placed by estimates based on a local linear regression model. It is shown that this test maximizes the probability of detection as the image size becomes arbitrarily large and the quantization step van ishes. This provides an asymptotic upperbound for the detection of hidden bits based on the LSB replacement mechanism. Numer ical results on real natural images show the relevance of the method and the sharpness of the asymptotic expression for the probability of detection.
Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model

The triplet Markov field (TMF) model is powerful in the nonstationary synthetic aperture radar (SAR) image analysis. Taking the speckle noise and the correlation of nonstationarities in two multitemporal SAR images into account, we propose a change-detection method based on the TMF model in this letter. The third field U in the TMF model is redefined to describe the nonstationary textural similarity between the two images for change detection. The corresponding prior energy of (X, U) is reconstructed. The adaptive weight parameter in prior energy is introduced to cope with the detection tradeoff issue. An automatic estimation of the parameter is obtained with low level of complexity. The Bayesian maximum posterior marginal criterion is utilized with the TMF model to obtain change detection. Experimental results on real SAR images validate the superiority of the proposed TMF method over the Markov random field method.
Data redundancy and duplicate detection in spatial join processing

The partition-based spatial-merge join (PBSM) of J.M. Patel and D.J. DeWitt (1996) and the size separation spatial join (S/sup 3/J) of N. Koudas and K.C. Sevcik (1997) are considered to be among the most efficient methods for processing spatial (intersection) joins on two or more spatial relations. Neither method assumes the presence of pre-existing spatial indices on the relations. In this paper, we propose several improvements to these join algorithms. In particular, we deal with the impact of data redundancy and duplicate detection on the performance of these methods. For PBSM, we present a simple and inexpensive online method to detect duplicates in the response set. There is no longer any need to eliminate duplicates in a final sorting phase, as was originally suggested. We also investigate the impact of different internal algorithms on the total run-time of PBSM. For S/sup 3/J, we break with the original design goal and introduce controlled redundancy of data objects. Results of a large set of experiments with real data sets reveal that our suggested modifications to PBSM and S/sup 3/J result in substantial performance improvements, where PBSM is generally superior to S/sup 3/J.
Personality Structure and Human Interaction: The Developing Synthesis of Psycho-Dynamic Theory

How has a theory of man as a social being to be formulated if we are to do justice to his individuality, to the subtle ways in which his love and hate compete within his relations with others and to the anxieties and resistances he shows when he seeks to change himself? To answer this question is the task which the author sets himself. After assessing Freud's basic principles, Guntrip proceeds to make a uniquely comprehensive review of subsequent theoretical contributions to psychoanalysis with special emphasis on the work of Fairbairn and Melanie Klein.From a background of philosophy, theology and social studies, Dr. Guntrip went on to take a personal psychoanalysis and to become a full time psychotherapist, and it is from this combination of wide knowledge and intensive work with people beset by conflicts in their relations with themselves and others that he evolves his views. After assessing Freud's basic principles, he proceeds to make a uniquely comprehensive review of subsequent theoretical contributions to psychoanalysis with special emphasis on the work of Fairbairn and Melanie Klein, as it is in their writings that he considers the most needed developments have been made, namely, the placing of the theory of personality squarely in the realm of human interaction. In the first part of Dr. Guntrip's book all students of personality will find an arresting survey of the development of psychoanalytic thought; in the latter they will meet highly stimulating and profound views on the origins and nature of the conflicting forces in human relationships. In particular he traces the progress of research beyond the problems of guilt and depression to the deeper and graver problems of the inadequate and Schizoid personality; thus finding the causes of mental ill health not in he secondary conflicts over sex and aggression, but in the primary problem of fear, and the struggle to cope with the frightened and helpless child in the depths of the unconscious.
Bioinformatics Applications Note Systems Biology Simple Neurite Tracer: Open Source Software for Reconstruction, Visualization and Analysis of Neuronal Processes

Motivation: Advances in techniques to sparsely label neurons unlock the potential to reconstruct connectivity from 3D image stacks acquired by light microscopy. We present an application for semi-automated tracing of neurons to quickly annotate noisy datasets and construct complex neuronal topologies, which we call the Simple Neurite Tracer. Availability: Simple Neurite Tracer is open source software, licensed under the GNU General Public Licence (GPL) and based on the public domain image processing software ImageJ. The software and further documentation are available via http://fiji.sc/Simple_Neurite_Tracer as part of the package Fiji, and can be used on Windows, Mac OS and Linux. Documentation and introductory screencasts are available at the same URL.
Universal blocks of the AdS/CFT Scattering Matrix

We identify certain blocks in the S-matrix describing the scattering of bound states of the AdS5 x S5 superstring that allow for a representation in terms of universal R-matrices of Yangian doubles. For these cases, we use the formulas for Drinfeld's second realization of the Yangian in arbitrary bound-state representations to obtain the explicit expressions for the corresponding R-matrices. We then show that these expressions perfectly match with the previously obtained S-matrix blocks.
A new regularization method based on the magnitude response function of Chebyshev filter for inverse Problems

In this paper, we set forth a new family of regularizing filters based on the magnitude response function of Chebyshev-I lowpass filter. The corresponding regularization strategies for inverse problem are constructed. The optimum asymptotic order of the regularized solution is obtained by a priori choice of the regularization parameter. Finally, numerical results are given to demonstrate the effectiveness of the new regularization method.
Korean Red Ginseng Extract Attenuates 3-Nitropropionic Acid-Induced Huntington's-Like Symptoms

Korean red ginseng (KRG) possesses neuroprotective activity. However, the potential neuroprotective value of KRG for the striatal toxicity is largely unknown. We investigated whether KRG extract (KRGE) could have a neuroprotective effect in a 3-nitropropionic acid- (3-NP) induced (i.p.) Huntington's disease (HD) model. KRGE (50, 100, and 250 mg/kg/day, p.o.) was administrated 10 days before 3-NP injection (pre-administration), from the same time with 3-NP injection (co-administration), or from the peak point of neurological impairment by 3-NP injection (post-administration). Pre-administration of KRGE produced the greatest neuroprotective effect in this model. Pre-administration of KRGE significantly decreased 3-NP-induced neurological impairment, lethality, lesion area, and neuronal loss in the 3-NP-injected striatum. KRGE attenuated microglial activation and phosphorylation of mitogen-activated protein kinases (MAPKs) and nuclear factor-kappa B (NF-κB) signal pathway. KRGE also reduced the level of mRNA expression of tumor necrosis factor-alpha, interleukin- (IL-) 1β, IL-6, inducible nitric oxide synthase, and OX-42. Interestingly, the intrathecal administration of SB203580 (a p38 inhibitor) or PD98059 (an inhibitor of MAPK Kinase, MEK) increased the survival rate in the 3-NP-induced HD model. Pre-administration of KRGE may effectively inhibit 3-NP-induced striatal toxicity via the inhibition of the phosphorylation of MAPKs and NF-κB pathways, indicating its therapeutic potential for suppressing Huntington's-like symptoms.
Formalizing Conceptual Spaces

Semantics constitutes the highest level on the communication ladder. The mutual understanding of terms makes it possible that information systems can communicate with people and among each other. Two opposing approaches to explaining the meaning of terms exist: a realist and a cognitive one. Many efforts for solving the so-called problem of semantic interoperability in the area of Information Science are based on realist semantics, which claims that meaning is in the world. In this paper we argue for a cognitive approach to semantic interoperability, which is based on the assumption that meanings are in the heads of people. This allows us to account for the fact that different people have different conceptualizations of the world and therefore require different answers and presentations of answers to their spatio-temporal questions. The paper presents a formal approach for representing Gärdenfors’ idea of conceptual spaces—sets of quality dimensions within a geometrical structure. It complements the mathematical notion of a vector space with a standardization method from statistics to formally define conceptual vector spaces. Such spaces allow for the measurement of semantic distances between instances of concepts and also for the assignment of weights to their quality dimensions in order to represent different contexts. Mappings, such as transformations and projections, between such spaces facilitate knowledge sharing and therefore support cognitive semantic interoperability. A case study from the geospatial domain—wayfinding services with landmarks—demonstrates the usefulness and plausibility of the approach.
The Influence of Semantic and Syntactic Context Constraints on Lexical Selection and Integration in Spoken-Word Comprehension as Revealed by ERPs

An event-related brain potential experiment was carried out to investigate the influence of semantic and syntactic context constraints on lexical selection and integration in spoken-word comprehension. Subjects were presented with constraining spoken sentences that contained a critical word that was either (a) congruent, (b) semantically and syntactically incongruent, but beginning with the same initial phonemes as the congruent critical word, or (c) semantically and syntactically incongruent, beginning with phonemes that differed from the congruent critical word. Relative to the congruent condition, an N200 effect reflecting difficulty in the lexical selection process was obtained in the semantically and syntactically incongruent condition where word onset differed from that of the congruent critical word. Both incongruent conditions elicited a large N400 followed by a left anterior negativity (LAN) time-locked to the moment of word category violation and a P600 effect. These results would best fit within a cascaded model of spoken-word processing, proclaiming an optimal use of contextual information during spoken-word identification by allowing for semantic and syntactic processing to take place in parallel after bottom-up activation of a set of candidates, and lexical integration to proceed with a limited number of candidates that still match the acoustic input.
Deriving security requirements from crosscutting threat descriptions

It is generally accepted that early determination of the stakeholder requirements assists in the development of systems that better meet the needs of those stakeholders. General security requirements frustrate this goal because it is difficult to determine how they affect the functional requirements of the system.This paper illustrates how representing threats as crosscutting concerns aids in determining the effect of security requirements on the functional requirements. Assets (objects that have value in a system) are first enumerated, and then threats on these assets are listed. The points where assets and functional requirements join are examined to expose vulnerabilities to the threats. Security requirements, represented as constraints, are added to the functional requirements to reduce the scope of the vulnerabilities. These requirements are used during the analysis and specification process, thereby incorporating security concerns into the functional requirements of the system.
GEV Regression with Convex Loss Applied to Imbalanced Binary Classification

In this paper, we consider the problem of binary classification with imbalanced data. Although this problem has been studied extensively in terms of the classification performance, the probability estimation of both majority and minority class has not yet been well studied. In order to make precise class probability estimation (CPE), we propose a new approach of regression with a recently proposed convex loss function under the framework of generalized linear model. In this model, the generalized extreme value (GEV) distribution is adopted to form the asymmetric link function, which is the key role in binary classification with imbalanced data. Also, we propose a method to estimate the shape parameter in GEV distribution. Experiments on real-world datasets show that our proposed GEV regression has a good classification performance as well as a precise CPE. Besides, comparisons with other optimization algorithms also suggest a high computational efficiency in our algorithm.
PLGA particulate delivery systems for subunit vaccines: Linking particle properties to immunogenicity

ABSTRACT Among the emerging subunit vaccines are recombinant protein- and synthetic peptide-based vaccine formulations. However, proteins and peptides have a low intrinsic immunogenicity. A common strategy to overcome this is to co-deliver (an) antigen(s) with (an) immune modulator(s) by co-encapsulating them in a particulate delivery system, such as poly(lactic-co-glycolic acid) (PLGA) particles. Particulate PLGA formulations offer many advantages for antigen delivery as they are biocompatible and biodegradable; can protect the antigens from degradation and clearance; allow for co-encapsulation of antigens and immune modulators; can be targeted to antigen presenting cells; and their particulate nature can increase uptake and cross-presentation by mimicking the size and shape of an invading pathogen. In this review we discuss the pros and cons of using PLGA particulate formulations for subunit vaccine delivery and provide an overview of formulation parameters that influence their adjuvanticity and the ensuing immune response.