ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A noise resistant image matching method using angular radial transform

In this paper, we extend the concept of the optimal similarity measure, originally developed for Zernike moments (ZMs) which belong to a class of orthogonal rotation invariant moments (ORIMs), to angular radial transform (ART) which is non-orthogonal. The proposed distance measure not only uses the magnitude of the ART coefficients but also incorporates phase component unlike the existing L-1-distance and L-2-distance measures which use only the magnitude of ART in image matching problems. Experimental results show that the new distance measure outperforms L-2-distance measure. The performance of the proposed method is highly robust to Gaussian noise and salt-and-pepper noise even at very high level of noise. The results are compared with the ZMs-based optimal similarity measure. It is shown that the recognition rate of the proposed distance measure is comparable to that of the ZMs, however, at very low computational complexity. (C) 2014 Elsevier Inc. All rights reserved.
Brain O-GlcNAcylation: From Molecular Mechanisms to Clinical Phenotype

O-GlcNAc is the attachment of β-N-acetylglucosamine to the hydroxyl group of serine and threonine in nuclear and cytoplasmic proteins. It is generally not further elongated but exists as a monosaccharide that can be rapidly added or removed. Thousands of proteins involved in gene transcription, protein translation and degradation as well as the regulation of signal transduction contain O-GlcNAc. Brain is one of the tissues where O-GlcNAc is the most highly expressed and deletion of neuronal O-GlcNAc leads to death early in development. O-GlcNAc is also important for normal adult brain function, where dynamic processes like learning and memory at least in part depend on the modification of specific proteins by O-GlcNAc. Conversely, too much or too little O-GlcNAc in the brain contributes to several disorders including obesity, intellectual disability and Alzheimer's disease. In this chapter, we describe the expression and regulation of O-GlcNAc in the nervous system.
Dose finding studies for therapies with late-onset toxicities: A comparison study of designs

An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model-based approaches in general outperform the model-assisted approaches, with an interval censored approach and a modified version of the time-to-event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods.
Measurements for indoor air quality assessment at the Capodimonte Museum in Naples (Italy)

The state of works of art exhibited inside museums can be strongly influenced by indoor air quality, due to chemical activity of gaseous pollutants and particulate matter. For that reason it is important to carry out periodic air quality controls to check if the concentration levels of air pollutants comply with the limits specified by the national laws. In this work we show results obtained in a monitoring campaign carried out at the Museum of Capodimonte in Naples, one of the most important museums in Southern Italy. Results concern indoor monitoring of inorganic and organic gaseous pollutants and PM10 and PM2.5 fractions; moreover we also present the comparison between indoor and outdoor particulate matter concentration. From this analysis emerged a satisfying indoor condition with respect to gaseous pollutants, without any limit exceedance; in contrast particulate matter exhibits high concentration levels with frequent exceedances. Comparison with outdoor concentrations demonstrates the influence of dust and organic matter transported from the park surrounding the museum due to wind and visitors stamping.
Efficient Epileptic Seizure Prediction Based on Deep Learning

Epilepsy is one of the worlds most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed. The goal is to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time. The features extraction and classification processes are combined into a single automated system. Raw EEG signal without any preprocessing is considered as the input to the system which further reduces the computations. Four deep learning models are proposed to extract the most discriminative features which enhance the classification accuracy and prediction time. The proposed approach takes advantage of the convolutional neural network in extracting the significant spatial features from different scalp positions and the recurrent neural network in expecting the incidence of seizures earlier than the current methods. A semi-supervised approach based on transfer learning technique is introduced to improve the optimization problem. A channel selection algorithm is proposed to select the most relevant EEG channels which makes the proposed system good candidate for real-time usage. An effective test method is utilized to ensure robustness. The achieved highest accuracy of 99.6 and lowest false alarm rate of 0.004 ${{{\bf h}}<^>{ - 1}}$ along with very early seizure prediction time of 1 h make the proposed method the most efficient among the state of the art.
Low resolution face recognition using a two-branch deep convolutional neural network architecture

We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality. (C) 2019 Published by Elsevier Ltd.
Molecular 'email': Electrochemical aptasensing of fish pathogens, molecular information encoding, encryption and hiding applications

DNA with data encoding and molecular recognition is rarely used in combination with electrochemistry for multipurpose integrated applications (especially in sensing, information communication and security). Herein, we demonstrated an electrochemical aptasensing, information communication and safety system for detection of fish pathogens (Aeromonas hydrophila or Edwardsiella tarda) and molecular information encryption and hiding. Two fish pathogens can be easily and quickly detected by electrochemistry, respectively, with high selectivity and sensitivity (detection limit lower than 1 cfu/mL) without the need for traditional time-consuming biochemical culturing process. The specific interaction of the probe (DNA aptamer) with targets (pathogens) on the tiny and imperceptible electrochemical platform provides protection for hiding DNA aptamers containing the encoded message, but also offers a foundation for developing of molecular cryptography and steganography. This electrochemical system, which is similar to mail communication, does not record information on paper, but a molecular mail that records information through DNA and reads information using electrochemical sensing, or more precisely, molecular electrochemical mail (namely molecular 'email'). Our study proved that the combination of the recognition and encoding capabilities of DNA aptamers with electrochemistry can open a new door for molecular-level digitization technology. In the future, large-capacity, easy-to-operate, resettable, and flexible molecular crypto-steganography will be developed for molecular cascade communication and control.
TFIIH stabilization recovers the DNA repair and transcription dysfunctions in thermo-sensitive trichothiodystrophy

Trichothiodystrophy (TTD) is a rare hereditary disease whose prominent feature is brittle hair. Additional clinical signs are physical and neurodevelopmental abnormalities and in about half of the cases hypersensitivity to UV radiation. The photosensitive form of TTD (PS-TTD) is most commonly caused by mutations in the ERCC2/XPD gene encoding a subunit of the transcription/DNA repair complex TFIIH. Here we report novel ERCC2/XPD mutations affecting proper protein folding, which generate thermo-labile forms of XPD associated with thermo-sensitive phenotypes characterized by reversible aggravation of TTD clinical signs during episodes of fever. In patient cells, the newly identified XPD variants result in thermo-instability of the whole TFIIH complex and consequent temperature-dependent defects in DNA repair and transcription. Improving the protein folding process by exposing patient cells to low temperature or to the chemical chaperone glycerol allowed rescue of TFIIH thermo-instability and a concomitant recovery of the complex activities. Besides providing a rationale for the peculiar thermo-sensitive clinical features of these new cases, the present findings demonstrate how variations in the cellular concentration of mutated TFIIH impact the cellular functions of the complex and underlie how both quantitative and qualitative TFIIH alterations contribute to TTD clinical features.
The effect of bergamot (KoksalGarry) supplementation on lipid profiles: A systematic review and meta-analysis of randomized controlled trials

This systematic review and meta-analysis were conducted to evaluate the impact of bergamot (KoksalGarry) and its nutraceutical compounds on lipid profiles. PubMed, Web of Knowledge, Scopus, and Google Scholar searched for relevant articles. Trials investigating the effect of oral bergamot supplementation on serum levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in adults were included. The mean differences and standard deviations were pooled using a random-effects model. Fourteen trials were included in this systematic review and meta-analysis. Bergamot supplementation significantly decreased serum levels of TC (weighted mean difference (WMD): -63.60 mg/dL; 95% CI: -78.03 to -49.18; p < .001), TG (WMD: -74.72 mg/dL; 95% CI: -83.58 to -65.87; p < .001), LDL-C (WMD: -55.43 mg/dL; 95% CI: -67.26 to -43.60; p < .001), and increased HDL-C (WMD: 5.78 mg/dL; 95% CI: 3.27 to 8.28; p < .001), respectively. Our systematic review of the effects of nutraceuticals containing bergamot on lipid markers showed inconsistent results. The results showed that bergamot supplementation might improve lipid profiles. The findings for nutraceutical compounds containing bergamot were inconsistent. However, the clinical efficacy of bergamot on lipid profiles needs to be further established through higher-quality studies.
[Toward a better understanding of fatigue in schizophrenia]

Despite being one of the most common complaints of people with schizophrenia, fatigue remains largely unexplored in this population. The lack of knowledge regarding this complex symptom makes it often underdiagnosed and undertreated in schizophrenia. The aim of this brief perspective review is to outline the potential origins (distinguishing primary and secondary fatigue) and consequences of fatigue and to explore some potential treatments in this population. The current literature in schizophrenia has mainly investigated fatigue as a trait, using a self-administered questionnaire. Beyond this observational approach, which does not allow to capture the symptom in real life situations where high levels of fatigue can emerge rapidly, we propose to consider the state level of fatigue, for instance occurring after a prolonged period of cognitive activity (i.e. mental fatigue). We elaborate on the potential relationships between mental fatigue and negative symptoms of schizophrenia and propose some research avenues to test the effects of acute fatigue on effort intentions and behaviours. The consideration of the multidimensional aspects of fatigue will allow to move beyond the sole pharmacological approach to treat fatigue in schizophrenia. Targeting the cognitive as well as the performance components of fatigue through interventions such as concomitant aerobic exercise - mental training offers attractive prospects to reduce fatigue in this population and minimize its functional negative impact.
A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems

Deploying 3D single-photon Lidar imaging in real world applications presents multiple challenges including imaging in high noise environments. Several algorithms have been proposed to address these issues based on statistical or learning-based frameworks. Statistical methods provide rich information about the inferred parameters but are limited by the assumed model correlation structures, while deep learning methods show state-of-the-art performance but limited inference guarantees, preventing their extended use in critical applications. This paper unrolls a statistical Bayesian algorithm into a new deep learning architecture for robust image reconstruction from single-photon Lidar data, i.e. the algorithm's iterative steps are converted into neural network layers. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions, the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling of the system impulse response function, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms.
A novel pixel range calculation technique for texture classification

In this article, a fractal base method has been implemented as a texture descriptor. As, fractal deals with self-similarity, the proposed method is able to find the similarity of local patterns of various images of similar group and discriminate the images of dissimilar patterns. Images are converted to binary images by using the concept of local binary pattern. In this paper we have proposed a new approach for texture classification called Pixel Range Calculation (PRC) method, which has been implemented by converting the color images to gray images. Four benchmarking datasets, such as, ALOT (Amsterdam Library Of Textures), UMD (University of MarylanD), KTH-TIPS2b (Kungliga Tekniska Hogskolan-Textures under varying Illumination, Pose and Scale) and UIUC (University of Illinois at Urbana-Champaign) has been used for our experimental purpose. The proposed method along with two state of art method namely, Gliding Box Method (GBM) and Multi Fractal Spectrum (MFS) has been implemented and tested on the above-mentioned datasets. The experimental results based on the classification accuracy show the PRC technique of fractal dimension estimation method out performs the GBM and MFS. It has been observed that the computational complexity of PRC method is much less than other two state of art methods.
Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.
Ratiometric ECL sensor based on Apt-AuNS@Lu nanoprobe for analyzing cell swelling-induced ATP release

A novel ratiometric electrochemiluminescence (ECL) system based on gold nanostars (AuNSs) support was constructed for the determination of hypotonicity-induced ATP release from HepG2 cells. AuNS@Lu nanoprobe was used as anodic luminophore and K2S2O8 as cathodic luminophore as well as anodic co-reactant. AuNS with the large specific surface was adopted to adsorb plentiful luminol to form solid-state probe and as affinity support to immobilize ATP aptamer (Apt). The obtained nanocomposite (Apt-AuNS@Lu) generated a strong ECL signal at + 0.4 V (vs. Ag/AgCl) with co-reactant K2S2O8, because of excellent conductivity and catalytic activity of AuNS. Furthermore, graphene oxide was reduced onto indium tin oxide (ITO) electrodes to facilitate the electron transfer. Following, polydopamine (PDA) film was formed via self-polymerization, improving stability and adhesion of the electrode surface. To immobilize ATP capture aptamer (AptC), abounding AuNSs were attached to RGO/PDA surface. When the sensor was incubated in the mixture solution of Apt-AuNS@Lu and target ATP, the ECL signal of Apt-AuNS@Lu increased with the increase of ATP concentration, meanwhile, the signal of K2S2O8 declined. The ratio of the two luminophores was used for the quantitative determination of ATP. The linear range was 5 to 250 nM, and the limit of detection was 1.4 nM at (3σ)/S. The method was successfully applied to analyze ATP release from HepG2 cells stimulated by 0.45% NaCl hypotonic solution. The results showed that the release kinetics profile of ATP had a sigmoidal shape with rapid release within 10 min and then slowed. Compared to the isotonic groups, the intracellular ATP concentration was 3.7 ± 0.3 µM (n = 3) decreasing by 40.3% and the extracellular was 23.4 ± 1.2 nM (n = 3) increasing by 9.2 times in the hypotonicity for 10 min, which showed ATP release from cells and good agreement with commercial ELISA test. The proposed strategy would be beneficial to broadening application of ECL technology in studying cell biological functions.
Exposing collaborative spammer groups through the review-response graph

Deceptive opinions of online merchandises, also known as review spams, cause great loss for consumers, manufacturers and even business-to-customer platforms. However, due to the weak supervision problem, especially the lack of ground-truth labels, identifying these untruthful reviews is challenging. What's even worse is that crowdsourcing workers out of manipulation campaigns always collaborate to distort an item's reputation, rendering the product together with its brand difficult to be rehabilitated. State-of-the-art solutions on spammer group recognition highlight co-reviewing behaviours or sentiment similarity to cluster reviews, which can only yield loosely-coupled candidates of reviewer sets. In this paper, we highlight the commenting interaction between reviews and model it as a bipartite graph and discover a new low-budget spam, i.e., responsive spam. Furthermore, we recognize strong-correlated groups of spam through a propagation technique upon two widely adopted spam indicators, i.e., text duplication and posting burstiness. Comparative results show that our approach is effective and outperforms state-of-the-art solutions with great significance.
Segmentation and Track-Analysis in Time-Lapse Imaging of Bacteria

In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.
Identification of Painted Rock-Shelter Sites Using GIS Integrated with a Decision Support System and Fuzzy Logic

The conservation and protection of painted rock shelters is an important issue. Throughout the world, if unprotected, they are vulnerable to vandalism or to industrial activities such as quarrying. This research explores the integrated use of a Geographic Information System (GIS) with a multi-criteria decision support system and fuzzy logic to identify possible rock art sites over the Vindhyan Plateau in the district of Mirzapur, Central India. The methodology proposed compares results obtained by spatial modelling with validation data derived from recent exhaustive field surveys of more than forty newly discovered rock-shelters in the Vindhyan region. The zones obtained by predictive modelling are in agreement with validation datasets and show that the method can be used for new site prospection. This method represents a potential tool for landscape planners and policy makers to employ when seeking protection from anthropogenic activities of potential areas of painted rock-shelter sites and archaeological deposits.