ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Co-amorphous Systems of Sinomenine with Platensimycin or Sulfasalazine: Physical Stability and Excipient-Adjusted Release Behavior

There is strong interest to develop affordable treatments for the infection-associated rheumatoid arthritis (RA). Here, we present a drug-drug co-amorphous strategy against RA and the associated bacterial infection by the preparation and characterization of two co-amorphous systems of sinomenine (SIN) with platensimycin (PTM) or sulfasalazine (SULF), two potent antibiotics. Both of them were comprehensively characterized using powder X-ray diffraction, temperature-modulated differential scanning calorimetry, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The co-amorphous forms of SIN-PTM and SIN-SULF exhibited high Tgs at 139.10 ± 1.0 and 153.3 ± 0.2 °C, respectively. After 6 months of accelerated tests and 1 month of drug-excipient compatibility experiments, two co-amorphous systems displayed satisfactory physical stability. The formation of salt and strong intermolecular interactions between SIN and PTM or SULF, as well as the decreased molecular mobility in co-amorphous systems, may be the intrinsic mechanisms underlying the excellent physical stability of both co-amorphous systems. In dissolution tests, two co-amorphous systems displayed distinct reduced SIN-accumulative releases (below 20% after 6 h of release experiments), which may lead to its poor therapeutic effect. Hence, we demonstrated a controlled release strategy for SIN by the addition of a small percentage of polymers and a small-molecule surfactant to these two co-amorphous samples as convenient drug excipients, which may also be used to improve the unsatisfactory dissolution behaviors of the previously reported SIN co-amorphous systems. Several hydrogen bonding interactions between SIN and PTM or SULF could be identified in NMR experiments in DMSO-d6, which may be underlying reasons of decreased dissolution behaviors of both co-amorphous forms. These drug-drug co-amorphous systems could be a potential strategy for the treatment of infection-associated RA.
Thyroid disorders and gastrointestinal and liver dysfunction: A state of the art review

Thyroid disorders commonly impact on the gastrointestinal system and may even present with gastrointestinal symptoms in isolation; for example, metastatic medullary thyroid carcinoma typically presents with diarrhoea. Delays in identifying and treating the underlying thyroid dysfunction may lead to unnecessary investigations and treatment, with ongoing morbidity, and can potentially be life-threatening. Similarly, gastrointestinal diseases can impact on thyroid function tests, and an awareness of the concept and management of non-thyroidal illness is necessary to avoid giving unnecessary thyroid therapies that could potentially exacerbate the underlying gastrointestinal disease. Dual thyroid and gastrointestinal pathologies are also common, with presentations occurring concurrently or sequentially, the latter after a variable time lag that can even extend over decades. Such an association aetiologically relates to the autoimmune background of many thyroid disorders (e.g. Graves' disease and Hashimoto's thyroiditis) and gastrointestinal disorders (e.g. coeliac disease and inflammatory bowel disease); such autoimmune conditions can sometimes occur in the context of autoimmune polyglandular syndrome. Emphasis should also be given to the gastrointestinal side effects of some of the medications used for thyroid disease (e.g. anti-thyroid drugs causing hepatotoxicity) and vice versa (e.g. interferon therapy causing autoimmune thyroid dysfunction). In this review, we discuss disorders of the thyroid-gut axis and identify the evidence base behind the management of such disorders.
Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degenerative diseases is strongly suspected. Electron microscopy (EM), with its very high resolution in all three directions, is one of the key tools to look more closely into these issues but the huge amounts of data it produces make automated analysis necessary. State-of-the-art computer vision algorithms designed to operate on natural 2-D images tend to perform poorly when applied to EM data for a number of reasons. First, the sheer size of a typical EM volume renders most modern segmentation schemes intractable. Furthermore, most approaches ignore important shape cues, relying only on local statistics that easily become confused when confronted with noise and textures inherent in the data. Finally, the conventional assumption that strong image gradients always correspond to object boundaries is violated by the clutter of distracting membranes. In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incorporates shape features capable of describing the 3-D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries. Our experiments demonstrate that our approach is able to segment mitochondria at a performance level close to that of a human annotator, and outperforms a state-of-the-art 3-D segmentation technique.
Class sparsity signature based Restricted Boltzmann Machine

Restricted Boltzmann Machines (RBMs) have been extensively utilized in machine learning as core units in constructing deep learning architectures such as Deep Boltzmann Machines (DBMs) and Deep Belief Networks (DBNs). However, they are prone to overfitting and several regularization techniques have been proposed to mitigate this effect. In this paper, we propose the semi-supervised class sparsity signature based RBM formulation by combining unsupervised generative training of the RBM with a supervised sparsity regularizer. The proposed approach, termed as cssRBM, enforces sparsity at the class level to ensure that coherent and discriminative representations are learnt during training. Combining unsupervised learning with supervised learning allows the model to utilize external training data to learn better generative features while the supervised learning enables fine-tuning for discrimination using the learned features. We construct both DBMs and DBN5 with cssRBM units and evaluate the performance on multiple publicly available benchmark datasets. Experiments on the MNIST and CIFAR-10 databases demonstrate that the proposed approaches are comparable with state-of-the-art deep learning architectures in the literature. We also evaluate the performance on one of the most challenging face databases, i.e., the Point and Shoot Challenge dataset. The results show that the proposed approaches improve state-of-the-art results by 15% on the PaSC database. (C) 2016 Elsevier Ltd. All rights reserved.
Recalibrating Fully Convolutional Networks With Spatial and Channel "Squeeze and Excitation" Blocks

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we aim toward an alternate direction of recalibrating the learned feature maps adaptively, boosting meaningful features while suppressing weak ones. The recalibration is achieved by simple computational blocks that can be easily integrated in F-CNNs architectures. We draw our inspiration from the recently proposed "squeeze and excitation" (SE) modules for channel recalibration for image classification. Toward this end, we introduce three variants of SE modules for segmentation: 1) squeezing spatially and exciting channel wise; 2) squeezing channel wise and exciting spatially; and 3) joint spatial and channel SE. We effectively incorporate the proposed SE blocks in three state-of-the-art F-CNNs and demonstrate a consistent improvement of segmentation accuracy on three challenging benchmark datasets. Importantly, SE blocks only lead to a minimal increase in model complexity of about 1.5%, while the Dice score increases by 4%-9% in the case of U-Net. Hence, we believe that SE blocks can be an integral part of future F-CNN architectures.
An overlapping semantic community detection algorithm base on the ARTs multiple sampling models

Since the semantic Social Network (SSN) is a new kind of complex networks, the traditional community detection algorithms require giving the number of the communities and could not detect the overlapping communities. To solve this problem, we propose improving multiple sampling models ARTs, consisting of ART, LART, ARTF and LARTF, sampling the textual information specific to node, link, node field, and link field correspondingly. The proposed ARTs models separate the semantic community detection into context sampling and communities detecting stage. After the context sampling, the quantized semantic coordinate is allocated to each sampling element, by which the cohesion for each sampling field can be established, avoiding the presetting of the number of communities. As the ARTs models are not easy to convergence, we explore the multiple sampling to accelerate the convergence, and the parameters of ARTs are analyzed by experimental analysis. In evaluation aspect, some traditional evaluation models are extended for semantic community measurement. Finally, efficiency of ARTs is verified by experiment. (C) 2014 Elsevier Ltd. All rights reserved.
Lysobacter selenitireducens sp. nov., isolated from river sediment

A Gram-stain-negative, yellow-pigmented, motile, flagellated and rod-shaped bacterium, designated as 13AT, was isolated from a river sediment sample of Fuyang River in Hengshui City, Hebei Province, PR China. Strain 13AT grew at 10-37 °C (optimum, 30 °C), at pH 5.0-11.0 (optimum, pH 7.0) and at 0-7 % (w/v) NaCl concentration (optimum, 0 %). Phylogenetic analysis based on the 16S rRNA gene sequence showed that strain 13AT belongs to the genus Lysobacter, and was most closely related to Lysobacter spongiicola DSM 21749T (97.8 %), Lysobacter concretionis DSM 16239T (97.5 %), Lysobacter daejeonensis GIM 1.690T (97.3 %) and Lysobacter arseniciresistens CGMCC 1.10752T (96.9 %). Meanwhile, the type species Lysobacter enzymogenes ATCC 29487T was selected as a reference strain (95.2 %). The genomic size of strain 13AT was 3.0 Mb and the DNA G+C content was 69.0 %. The average nucleotide identity values between strain 13AT and each of the reference type strains L. spongiicola DSM 21749T, L. concretionis DSM 16239T, L. daejeonensis GIM 1.690T, L. arseniciresistens CGMCC 1.10752T and L. enzymogenes ATCC 29487T were 75.9, 76.1, 77.7, 78.0 and 73.2 %, respectively. The digital DNA-DNA hybridization values between strain 13AT and each of the reference type strains were 21.7, 22.2, 21.9, 22.7 and 23.2 %, respectively. The average amino acid identity values between strain 13AT and each of the reference type strains were 72.5, 72.9, 72.3, 75.0 and 69.2 %, respectively. The major fatty acids were iso-C15 : 0, iso-C16 : 0 and summed feature 9 (iso-C17 : 1  ω9c and/or C16 : 0 10-methyl). The sole respiratory quinone was identified as ubiquinone-8. The polar lipid profile contained phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, an unidentified aminolipid, an unidentified lipid, four unidentified phospholipids and two unidentified glycolipids. Based on the phenotypic, physiological, phylogenetic and chemotaxonomic data, strain 13AT represents a novel species of the genus Lysobacter, for which the name Lysobacter selenitireducens sp. nov. is proposed. The type strain is 13AT (=JCM 34786T=GDMCC 1.2722T).
Measurement approaches for Thermal Impedance Spectroscopy of Li-ion batteries

This work introduces two novel measurement methods for capturing the thermal behaviour of Li-ion cells in the frequency domain. For the validation, we compare them to the state of the art measurement method, in which the battery is thermally stimulated by a set of discrete frequencies, each tested individually in a sequential manner. All experiments are conducted with a cylindrical 18650 LFP-Graphite cell and lead to similar thermal impedance spectra. We validate a 0-D thermal model fitted to the respective measurement results with a thermal step response in the time domain. The results show, that all three models accurately represent the thermal behaviour of a Li-ion cell, with a mean absolute error below 0.3 K on the validation test. A spider-web diagram classifies and compares the given methods based on five values showing, that the state of art thermal measurement time can be reduced by 62% while also increasing the impedance measurement resolution by measuring multiple frequencies simultaneously.
What is the interference in "verbal interference"?

Research on the interrelation between language and other components of cognition makes frequent use of verbal interference paradigms. In this, participants are engaged in a primary nonverbal task, while simultaneously repeating non-sense syllables from memory or playback to occupy their articulatory buffer, which is assumed to block internal language use. However, language production involves different subprocesses and levels of representation, and no previous study has explicitly investigated which of these are affected by an occupied articulatory buffer. Thus, the current study addresses the question whether an occupied articulatory buffer significantly interferes with conceptualization. In Experiment 1, speakers name simple objects as fast and as accurately as they can under three conditions. In an interference condition, the verbalization task runs in parallel to a secondary, syllable memorization/recall task, which was expected to induce a situation in which the articulatory buffer temporarily holds phonological information while speakers engage in conceptualization. The articulatory buffer was not occupied in two control conditions. In Experiment 2, speakers performed a similar but more complex task. They verbally responded to visual depictions of actions, again under an interference condition and two control conditions. Results obtained in both experiments suggested no interference. Taken together, the findings suggest that an occupied articulatory buffer does not significantly affect conceptualization.
Antibodies to Glycolipids in Guillain-Barré Syndrome, Miller Fisher Syndrome and Related Autoimmune Neurological Diseases

Guillain-Barré syndrome (GBS) and Miller Fisher syndrome (MFS) are acute immune-mediated neuropathies, often preceded by an infection. Anti-glycolipid antibodies are frequently detected in patients' sera in the acute-phase. In particular, IgG anti-GQ1b antibodies are positive in as high as 90% of MFS cases. Anti-glycolipid antibodies are useful for the diagnosis of GBS and MFS. In addition, those antibodies may be directly involved in the pathogenetic mechanisms by binding specifically to the regions where the target glycolipid antigen is densely localized. This was proven by the development of animal models of anti-glycolipid antibody-mediated neuropathies. The presence of antibodies that specifically recognize a new conformational epitope formed by two gangliosides (ganglioside complex) in the acute-phase sera of some GBS patients suggested existence of a carbohydrate-carbohydrate interaction between glycolipids. Further intensive research is needed to clarify this point.
A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.
Computing a maximum clique in geometric superclasses of disk graphs

In the 90's Clark, Colbourn and Johnson wrote a seminal paper where they proved that maximum clique can be solved in polynomial time in unit disk graphs. Since then, the complexity of maximum clique in intersection graphs of d-dimensional (unit) balls has been investigated. For ball graphs, the problem is NP-hard, as shown by Bonamy et al. (FOCS '18). They also gave an efficient polynomial time approximation scheme (EPTAS) for disk graphs. However, the complexity of maximum clique in this setting remains unknown. In this paper, we show the existence of a polynomial time algorithm for a geometric superclass of unit disk graphs. Moreover, we give partial results toward obtaining an EPTAS for intersection graphs of convex pseudo-disks.
4D Golden-Angle Radial MRI at Subsecond Temporal Resolution

Intraframe motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly accelerated 4D (3D + time) dynamic MRI framework with subsecond temporal resolution that does not require explicit motion compensation. The method combines standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel imaging with imProved performance) reconstruction. Specifically, 4D dynamic MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., subsecond/3D volume). It does not require sequence modification to acquire additional navigation data, it is compatible with commercially available stack-of-stars sequences, and it does not need an intermediate reconstruction step. The proposed 4D dynamic MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). Our results have shown that GRASP-Pro reconstruction with the new basis estimation strategy enables highly-accelerated 4D dynamic imaging at subsecond temporal resolution (with five spokes or less for each dynamic frame per image slice) for both free-breathing non-DCE-MRI and DCE-MRI. In the abdominal phantom, better image quality with lower root mean square error and higher structural similarity index was achieved using GRASP-Pro compared with standard GRASP. With the ability to acquire each 3D image in less than 1 s, intraframe respiratory blurring can be intrinsically reduced for body applications with our approach, which eliminates the need for explicit motion detection and motion compensation.
From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model. (C) 2017 Elsevier B.V. All rights reserved.
Towards a Quantum-Inspired Binary Classifier

Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input. Several research works have demonstrated the effectiveness of machine learning algorithms but the state-of-the-art algorithms are based on the classical theories of probability and logic. Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly. From this inspiration, we propose the quantum-inspired binary classifier, which is based on quantum detection theory. We used text corpora and image corpora to explore the effect of our proposed model. Our proposed model outperforms the state-of-the-art models in terms of precision, recall, and F-measure for several topics (categories) in the 20 newsgroup text corpora. Our proposed model outperformed all the baselines in terms of recall when the MNIST handwritten image dataset was used; F-measure is also higher for most of the categories and precision is also higher for some categories. Our proposed model suggests that binary classification effectiveness can be achieved by using quantum detection theory. In particular, we found that our Quantum-Inspired Binary Classifier can increase the precision, recall, and F-measure of classification where the state-of-the-art methods cannot.