ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Glutathione S-transferase genetic polymorphisms and fluoride-induced reproductive toxicity in men with idiopathic infertility

Male infertility caused by idiopathic oligoasthenospermia (OAT) is known as idiopathic male infertility. Glutathione S-transferase (GST) and fluoride may play important roles in idiopathic male infertility, but their effects are still unknown. Our study examined the relationship between GST polymorphisms and fluoride-induced toxicity in idiopathic male infertility and determined the underlying mechanism. Sperm, blood, and urine samples were collected from 560 males. Fluoride levels were measured by a highly selective electrode method, and GST genotypes were identified using polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (PCR-RFLP). Semen parameters, DNA fragmentation index (DFI), mitochondrial membrane potential (MMP), and oxidative stress (OS) biomarkers were statistically assessed at the P < 0.05 level. Compared with healthy fertile group, semen parameters, fluoride levels, OS biomarkers, sex hormone levels, and MMP and DFI levels were lower in the idiopathic male infertility group. For glutathione S-transferase M1 (GSTM1[-]) and glutathione S-transferase T1 (GSTT1[-]) or glutathione S-transferase P1 (GSTP1) mutant genotypes, levels of semen fluoride, OS, MMP, and DFI were considerably higher, and the mean levels of sperm parameters and testosterone were statistically significant in GSTM1(+), GSTT1(+), and GSTP1 wild-type genotypes. Both semen and blood fluoride levels were associated with oxidative stress in idiopathic male infertility patients. Elevated fluoride in semen with the genotypes listed above was linked to reproductive quality in idiopathic male infertility patients. In conclusion, GST polymorphisms and fluorine may have an indicative relationship between reproductive quality and sex hormone levels, and OS participates in the development of idiopathic male infertility.
Drug testing for mitragynine and kratom: Analytical challenges and medico-legal considerations

Mitragyna speciosa, known as kratom, is a tropical tree native to Southeast Asia that has long been used to increase energy and in traditional medicine. Kratom leaves contain several indole alkaloids including mitragynine, mitraciliatine, speciogynine, and speciociliatine, which have the same molecular formula and connectivity, but different spatial arrangements (i.e., diastereomers). A routine liquid-chromatographic-high-resolution mass-spectrometric (LC-HRMS) multi-analyte method for addictive and herbal drugs in urine did not separate mitragynine from speciogynine and speciociliatine. Separation and individual measurement of the four diastereomers was possible with an improved LC method. All diastereomers were detected in 29 patient urine samples who tested positive for mitragynine with the routine method, albeit at variable absolute amounts and relative proportions. The presence of all diastereomers rather than individual substances indicated that they originated from the intake of kratom (i.e., plant material). Speciociliatine dominated in most samples (66%), whereas mitragynine and mitraciliatine were the highest in 17% each. A kratom product (powdered plant material) marketed in Sweden contained all diastereomers with mitragynine showing the highest level. In Sweden, there are signs of an increasing use of kratom in society, based on the results from drug testing, the number of poisons center consultations on intoxications, and customs seizure statistics. Because there may be health risks associated with kratom use, including dependence, serious adverse reactions, and death, analytical methods should be able to identify and quantify all diastereomers. In Sweden, this is important from a legal perspective, as only mitragynine is classified, whereas the other three diastereomers, and kratom (plant material), are not.
Safety-Optimized Inductive Powering of Implantable Medical Devices: Tutorial and Comprehensive Design Guide

A tutorial and comprehensive guide are presented for the design of planar spiral inductors with maximum energy delivery in biomedical implants. Rather than maximizing power transfer efficiency (PTE), the ratio of the received power to the square of the magnetic flux density is maximized in this technique. This ensures that the highest power is delivered for a given level of safe electromagnetic radiation, as measured by the specific absorption rate (SAR) in the tissue. By using quasi-static field approximations, the maximum deliverable power under SAR constraints is embedded in a lumped-element model of a 2-coil inductive link, from which planar coil geometries are derived. To compare the proposed methodology with the conventional approach that maximizes PTE, the results of both techniques are compared for three examples of state-of-the-art designs. It is demonstrated that the presented technique increases the maximum deliverable power while operating at a given level of non-ionizing radiation by factors of 8x, 410x, and 560x as compared to the three existing designs, and maintaining moderate link efficiencies of 12%, 23%, and 12%, respectively.
Recent advances in improving intranasal allergen-specific immunotherapy; focus on delivery systems and adjuvants

Allergen-specific Immunotherapy (AIT) is the main therapeutic strategy to control and treat allergic disorders. Intranasal Immunotherapy (INIT) was introduced as a needle-free, noninvasive, and efficient approach among various routes of allergen administration. Since direct exposure of nasal mucosa to allergen extracts could induce local and systemic reactions, recent studies focus on establishing novel formulations using various delivery systems and adjuvants to improve INIT efficacy. This review categorizes and describes natural and synthetic micro/nanoparticles such as chitosan, PLGA, liposome, exosome, and nano-emulation droplets used as delivery systems or immunomodulatory and immune-regulatory agents. Also, multiple microbial agents, including probiotics, mycobacterial and viral components, TLR ligands, and biologic agents, i.e., antibody fragments, recombinant cytokines, vitamin A, and pulsed dendritic cells (DCs), are other platforms that are discussed. In addition, future perspectives and proposed strategies to help INIT were provided.
A Fast Algorithm for Fractional QCQP and Applications to Secure Beamforming in Cognitive Nonorthogonal Multiple Access Networks

In this paper, we investigate a nonconvex fractional quadratically constrained quadratic problem (fractional QCQP), which has a wide application to the resource allocation optimization in wireless communication systems. Different from the state-of-the-art methods needing to solve semidefinite programmings or second-order cone programmings, we propose a fast algorithm for solving fractional QCQP by combining the successive convex approximation method and the consensus alternating direction method of multipliers, which has only simple computations and works very fast in applications with modest accuracy. We also apply the proposed fast algorithm to secure beamforming design for enhancing physical layer security in cognitive nonorthogonal multiple access (NOMA) networks, where secrecy rate optimization problems in both underlay and overly cognitive NOMA networks are typical fractional QCQPs and have not been well studied. Simulation results have shown that our proposed fast algorithm achieves almost the same performance as the state-of-the-art methods, however, the proposed fast algorithm has very low computation complexity.
DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images

Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applications, especially for graph-based or seed-based reconstruction methods and can help to visualize the morphology structures. There are a few hand-crafted models proposed to detect the branch points. However, they are highly dependent on the empirical setting of the parameters for different images. In this paper, we propose a DeepBranch model for branch point detection with two-level designed convolutional networks, a candidate region segmenter and a false positive reducer. On the first level, an improved 3D U-Net model with anisotropic convolution kernels is employed to detect initial candidates. Compared with the traditional sliding window strategy, the improved 3D U-Net can avoid massive redundant computations and dramatically speed up the detection process by employing dense-inference with fully convolutional neural networks (FCN). On the second level, a method based on multi-scale multi-view convolutional neural networks (MSMV-Net) is proposed for false positive reduction by feeding multi-scale views of 3D volumes into multiple streams of 2D convolution neural networks (CNNs), which can take full advantage of spatial contextual information as well as fit different sizes. Experiments on multiple 3D biomedical images of neurons, retinal blood vessels and bronchi confirm that the proposed 3D branch point detection method outperforms other state-of-the-art detection methods, and is helpful for graph-based or seed-based reconstruction methods.
EasyGuide Plasmids Support in Vivo Assembly of gRNAs for CRISPR/Cas9 Applications in Saccharomyces cerevisiae

Most CRISPR/Cas9 applications in yeast rely on a plasmid-based expression of Cas9 and its guide RNA (gRNA) containing a 20-nucleotides (nts) spacer tailored to each genomic target. The lengthy assembly of this customized gRNA requires at least 3-5 days for its precloning in Escherichia coli, purification, validation, and cotransformation with Cas9 into a yeast strain. Here, we constructed a series of 12 EasyGuide plasmids to simplify CRISPR/Cas9 applications in Saccharomyces cerevisiae. The new vectors provide templates for generating PCR fragments that can assemble up to six functional gRNAs directly into yeasts via homologous recombination between the 20-nts spacers. By dispensing precloning in E. coli, yeast in vivo gRNA assembly significantly reduces the CRISPR/Cas9 experimental workload. A highly efficient yeast genome editing procedure, involving PCR amplification of gRNAs and donors, followed by their transformation into a Cas9-expressing strain, can be easily accomplished through a quick protocol.
Zero-shot event detection via event-adaptive concept relevance mining

Zero-shot complex event detection has been an emerging task in coping with the scarcity of labeled training videos in practice. Aiming to progress beyond the state-of-the-art zero-shot event detection, we propose a new zero-shot event detection approach, which exploits the semantic correlation between an event and concepts. Based on the concept detectors pre-trained from external sources, our method learns the semantic correlation from the concept vocabulary and emphasizes on the most related concepts for the zero-shot event detection. Particularly, a novel Event-Adaptive Concept Integration algorithm is introduced to estimate the effectiveness of semantically related concepts by assigning different weights to them. As opposed to assigning weights by an invariable strategy, we compute the weights of concepts using the area under score curve. The assigned weights are incorporated into the confidence score vector statistically to better characterize the event-concept correlation. Our algorithm is proved to be able to harness the related concepts discriminatively tailored for a target event. Extensive experiments are conducted on the challenging TRECVID event video datasets, which demonstrate the advantage of our approach over the state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.
Improving the Asymptotic Radiative Transfer Model to Better Characterize the Pure Snow Hyperspectral Bidirectional Reflectance

The asymptotic radiative transfer (ART) model has been widely used in snow remote sensing. However, the anisotropic effects of snow reflectance challenge this model because of its underestimation in the forward-scattering direction. To exhibit these strong scattering properties of the snow surface, a microfacet specular kernel has been supplemented with the ART model (hereinafter named the ARTS model). In this study, we propose a method of multiplying by a correction term for improving the ART model (hereinafter named the ARTF model). We validate the performance of the ARTF model using various data sources. Our results demonstrate that: 1) the ARTF model has higher accuracy in characterizing snow bidirectional signatures, with R-2 and root mean square error (RMSE) values in the ranges from 0.722 to 0.990 and 0.007 to 0.041, respectively, than the ART (R-2 = 0.507-0.802 and RMSE = 0.038-0.088) and ARTS (R-2 = 0.686-0.962 and RMSE = 0.021-0.044) models, especially in the long-wave near-infrared region and 2) the ARTF model can effectively represent snow hyperspectral reflectance, while the ART and ARTS models significantly underestimate snow reflectance in the visible and shortwave near-infrared region. The R-2 values of these three models reach similar to 0.99, and the RMSE values of the ARTF model range from 0.012 to 0.024, which are smaller than those of the ART (RMSE = 0.021-0.061) and ARTS (RMSE = 0.021-0.049) models. These results demonstrate that the ARTF model is better than the ART and ARTS models for characterizing snow hyperspectral bidirectional reflectance.
Molecular and functional characterization of an evolutionarily conserved CREB-binding protein in the Lymnaea CNS

In eukaryotes, CREB-binding protein (CBP), a coactivator of CREB, functions both as a platform for recruiting other components of the transcriptional machinery and as a histone acetyltransferase (HAT) that alters chromatin structure. We previously showed that the transcriptional activity of cAMP-responsive element binding protein (CREB) plays a crucial role in neuronal plasticity in the pond snail Lymnaea stagnalis. However, there is no information on the molecular structure and HAT activity of CBP in the Lymnaea central nervous system (CNS), hindering an investigation of its postulated role in long-term memory (LTM). Here, we characterize the Lymnaea CBP (LymCBP) gene and identify a conserved domain of LymCBP as a functional HAT. Like CBPs of other species, LymCBP possesses functional domains, such as the KIX domain, which is essential for interaction with CREB and was shown to regulate LTM. In-situ hybridization showed that the staining patterns of LymCBP mRNA in CNS are very similar to those of Lymnaea CREB1. A particularly strong LymCBP mRNA signal was observed in the cerebral giant cell (CGC), an identified extrinsic modulatory interneuron of the feeding circuit, the key to both appetitive and aversive LTM for taste. Biochemical experiments using the recombinant protein of the LymCBP HAT domain showed that its enzymatic activity was blocked by classical HAT inhibitors. Preincubation of the CNS with such inhibitors blocked cAMP-induced synaptic facilitation between the CGC and an identified follower motoneuron of the feeding system. Taken together, our findings suggest a role for the HAT activity of LymCBP in synaptic plasticity in the feeding circuitry.
Beyond Bag-of-Words: combining generative and discriminative models for scene categorization

This paper proposes an efficient framework for scene categorization by combining generative model and discriminative model. A state-of-the-art approach for scene categorization is the Bag-of-Words (BoW) framework. However, there exist many categories in scenes. Generally when a new category is considered, the codebook in BoW framework needs to be re-generated, which will involve exhaustive computation. In view of this, this paper tries to address the issue by designing a new framework with good scalability. When an additional category is considered, much lower computational cost is needed while the resulting image signatures are still discriminative. The image signatures for training discriminative model are carefully designed based on the generative model. The soft relevance value of the extracted image signatures are estimated by image signature space modeling and are incorporated in Fuzzy Support Vector Machine (FSVM). The effectiveness of the proposed method is validated on UIUC Scene-15 dataset and NTU-25 dataset, and it is shown to outperform other state-of-the-art approaches for scene categorization.
Regularizing the Deepsurv Network Using Projection Loss for Medical Risk Assessment

State-of-the-art deep survival prediction approaches expand network parameters to accommodate performance over a fine discretization of output time. For medical applications where data are limited, the regression-based Deepsurv approach is more advantageous because its continuous output design limits unnecessary network parameters. Despite the practical advantage, the typical network lacks control over the feature distribution causing the network to be more prone to noisy information and occasional poor prediction performance. We propose a novel projection loss as a regularizing objective to improve the time-to-event Deepsurv model. The loss formulation maximizes the lower bound of the multiple-correlation coefficient between the network's features and the desired hazard value. Reducing the loss also theoretically lowers the upper bound on the likelihood of discordant pair and improves C-index performance. We observe superior performances and robustness of regularized Deepsurv over many state-of-the-art approaches in our experiments with five public medical datasets and two cross-cohort validation tasks.
Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains.
A Unifying Approach to Moment-Based Shape Orientation and Symmetry Classification

In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods.
ART FOR CHANGE: Transformative learning and youth empowerment in a changing climate

Young people represent a powerful force for social change, and they have an important role to play in climate change responses. However, empowering young people to be "systems changers" is not straightforward. It is particularly challenging within educational systems that prioritize instrumental learning over critical thinking and creative actions. History has shown that by creating novel spaces for reflexivity and experimentation, the arts have played a role in shifting mindsets and opening up new political horizons. In this paper, we explore the role of art as a driver for societal transformation in a changing climate and consider how an experiment with change can facilitate reflection on relationships between individual change and systems change. Following a review of the literature on transformations, transformative learning and the role of art, we describe an experiment with change carried out with students at an Art High School in Lisbon, Portugal, which involved choosing one sustainable behavior and adopting it for 30 days. A transformative program encouraged regular reflection and group discussions. During the experiment, students started developing an art project about his or her experience with change. The results show that a transformative learning approach that engages students with art can support critical thinking and climate change awareness, new perspectives and a sense of empowerment. Experiential, arts-based approaches also have the potential to create direct and indirect effects beyond the involved participants. We conclude that climate-related art projects can serve as more than a form of science communication. They represent a process of opening up imaginative spaces where audiences can move more freely and reconsider the role of humans as responsible beings with agency and a stake in sustainability transformations.
Image Recovery based on Local and Nonlocal Regularizations

Recently, a nonlocal low-rank regularization based compressive sensing approach (NLR) which exploits structured sparsity of similar patches has shown the state-of-the-art performance in image recovery. However, NLR cannot efficiently preserve local structures because it ignores the relationship between pixels. In addition, the surrogate logdet function used in NLR cannot well approximate the rank. In this paper, a novel approach based on local and nonlocal regularizations toward exploiting the sparse-gradient property and nonlocal low-rank property (SGLR) has been proposed. Weighted schatten-p norm and l (q) norm have been used as better non-convex surrogate functions for the rank and l0 norm. In addition, an efficient iterative algorithm is developed to solve the resulting recovery problem. The experimental results have demonstrated that SGLR outperforms existing state-of-the-art CS algorithms.