ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Phenotypic variability in 446 CADASIL patients: Impact of NOTCH3 gene mutation location in addition to the effects of age, sex and vascular risk factors

The recent discovery that the prevalence of cysteine mutations in the NOTCH3 gene responsible for CADASIL was more than 100 times higher in the general population than that estimated in patients highlighted that the mutation location in EGFr-like-domains of the NOTCH3 receptor could have a major effect on the phenotype of the disease. The exact impact of such mutations locations on the multiple facets of the disease has not been fully evaluated. We aimed to describe the phenotypic spectrum of a large population of CADASIL patients and to investigate how this mutation location influenced various clinical and imaging features of the disease. Both a supervised and a non-supervised approach were used for analysis. The results confirmed that the mutation location is strongly related to clinical severity and showed that this effect is mainly driven by a different development of the most damaging ischemic tissue lesions at cerebral level. These effects were detected in addition to those of aging, male sex, hypertension and hypercholesterolemia. The exact mechanisms relating the location of mutations along the NOTCH3 receptor, the amount or properties of the resulting NOTCH3 products accumulating in the vessel wall, and their final consequences at cerebral level remain to be determined.
Impulse Noise Removal Using Adaptive Radial Basis Function Interpolation

A novel adaptive radial basis function interpolation-based impulse noise removal algorithm is introduced in this manuscript. This approach consists of two stages: noisy pixel detection and correction. In former step, the noise-affected pixels in an image are detected, and in the latter step, the noisy pixels are restored by adaptive radial basis function-based interpolation scheme. The radial basis function interpolation scheme is used to estimate the unknown noisy pixel value from the noise-free known neighboring pixel values. For both noisy pixel detection and correction, a center sliding window is considered at each pixel location. The proposed approach is experimented on some benchmark data sets, and its performance is evaluated using five performance evaluation measures: PSNR, MSSIM, IEF, correlation factor, and NSER on different test images by comparing it against sixteen different state-of-the-art techniques. It is found that the proposed approach gives better results than the sixteen different state-of-the-art techniques.
GaitCopy: Disentangling Appearance for Gait Recognition by Signature Copy

This paper addresses the problem of gait-based people identification by copying optical flow-based signatures. The proposed model, coined as GaitCopy, receives as input a stack of gray images and returns the gait signature of the represented subject. The novel property of this network is that it is not trained to only generate discriminative signatures, but to copy signatures generated by a Master network trained on optical flow inputs. Then, GaitCopy is enforced to extract signatures based on motion and not based on appearance, despite having been trained with pixel inputs. We implement two different versions of GaitCopy, one mainly composed of 3D convolutional layers to capture local temporal information; and a second one based on GaitSet which uses 2D convolutional layers under a temporal setup. We evaluate our approach on two public gait datasets: CASIA-B and TUM-GAID. We observe that compact networks, up to x4:2 smaller for TUM-GAID, can be obtained by using our approach, while keeping a competitive recognition accuracy with respect to the state of the art, and without the need of explicit optical flow computation. Even with such network compression, the results obtained in TUM-GAID are comparable to those of the state of the art, with an average accuracy of 97% on the test set. (Code will be publicly available upon acceptance.)
Classification of user competency levels using EEG and convolutional neural network in 3D modelling application

Competency classification is one of the main challenging tasks for the development of state-of-the-art next generation computer-aided design (CAD) system. To develop a futuristic system that can accommodate the lack of competency, the system needs to adapt to the competency level of the user. To solve this problem, we have presented a deep convolutional neural network (CNN) model that uses the Electroencephalography (EEG) of the user to classify the level of competency in 3D modeling task. The five competency levels were defined based on the task completion time, final 3D model rating and previous modeling experience. This is the first study that classifies user competency and employs the CNN model for the analysis of EEG signals in the design application. In this work, a 14-layer deep CNN model was implemented to classify competency into five different levels. The proposed technique achieved an accuracy, specificity, and sensitivity of > 88%, > 90% and > 70% respectively with 5-fold cross-validation. The results showed the applicability of a CNN model to classify the user competency and can be used as a first step in developing state-of-the-art adaptive 3D modeling systems. (C) 2020 Elsevier Ltd. All rights reserved.
FQI: feature-based reduced-reference image quality assessment method for screen content images

In this study, a reduced-reference image-quality-assessment (IQA) method for screen content images, named as feature-quality-index (FQI) is proposed. The proposed method is based on the fact that the human visual system is more sensitive towards change in features than intensity or structure. Reduced features from the reference and distorted images are first extracted. In order to find the preserved features in the distorted image, a feature matching process with a reduced number of distance calculations is proposed, namely reduced-distance method. To reflect the importance of the matched features and their distance, the inner product between the normalised scale and distance vector is obtained. Extensive comparisons are performed on two available benchmark databases namely SIQAD and QACS, with eight reduced-reference, and nine full-reference state-of-the-art IQA techniques to demonstrate the consistency, accuracy, and robustness of the proposed FQI. The subjective evaluation of mean opinion score shows that FQI outperforms the current state-of-the-art IQA techniques.
Ori-Finder 2022: A Comprehensive Web Server for Prediction and Analysis of Bacterial Replication Origins

The replication of DNA is a complex biological process that is essential for life. Bacterial DNA replication is initiated at genomic loci referred to as replication origins (oriCs). Integrating the Z-curve method, DnaA box distribution, and comparative genomic analysis, we developed a web server to predict bacterial oriCs in 2008 called Ori-Finder, which contributes to clarify the characteristics of bacterial oriCs. The oriCs of hundreds of sequenced bacterial genomes have been annotated in the genome reports using Ori-Finder and the predicted results have been deposited in DoriC, a manually curated database of oriCs. This has facilitated large-scale data mining of functional elements in oriCs and strand-biased analysis. Here, we describe Ori-Finder 2022 with updated prediction framework, interactive visualization module, new analysis module, and user-friendly interface. More species-specific indicator genes and functional elements of oriCs are integrated into the updated framework, which has also been redesigned to predict oriCs in draft genomes. The interactive visualization module displays more genomic information related to oriCs and their functional elements. The analysis module includes regulatory protein annotation, repeat sequence discovery, homologous oriC search, and strand-biased analyses. The redesigned interface provides additional customization options for oriC prediction. Ori-Finder 2022 is freely available at http://tubic.tju.edu.cn/Ori-Finder/ and https://tubic.org/Ori-Finder/.
State of the art of smart homes

In this paper we present a review of the state of the art of smart homes. We will first look at the research work related to smart homes from various view points; first in the view point of specific techniques such as smart homes that utilize computer vision based techniques, smart homes that utilize audio-based techniques and then smart homes that utilize multimodal techniques. Then we look at it from the view point of specific applications of smart homes such as eldercare and childcare applications, energy efficiency applications and finally in the research directions of multimedia retrieval for ubiquitous environments. We will summarize the smart homes based research into these two categories. In the survey we found out that some well-known smart home applications like video based security applications has seen the maturity in terms of new research directions while some topics like smart homes for energy efficiency and video summarization are gaining momentum. (C) 2012 Elsevier Ltd. All rights reserved.
Recent advances in phosphorescent OLEDs for small- and large-area-display sizes

State-of-the-art phosphorescent organic light-emitting diode (PHOLED(TM)) lifetime and efficiency performances for a range of emission colors are reported. Lifetimes in excess of 100,000 hours were demonstrated at display luminance levels for yellow-green and NTSC deep-red emission. In addition, external quantum efficiencies close to the theoretical maximum are demonstrated for long-lived PHOLEDs.
Systematic Review of In Vitro Antimicrobial Susceptibility Testing for Bacillus anthracis, 1947-2019

Bacillus anthracis, the causative agent of anthrax, is a high-consequence bacterial pathogen that occurs naturally in many parts of the world and is considered an agent of biowarfare or bioterrorism. Understanding antimicrobial susceptibility profiles of B. anthracis isolates is foundational to treating naturally occurring outbreaks and to public health preparedness in the event of an intentional release. In this systematic review, we searched the peer-reviewed literature for all publications detailing antimicrobial susceptibility testing of B. anthracis. Within the set of discovered articles, we collated a subset of publications detailing susceptibility testing that followed standardized protocols for Food and Drug Administration-approved, commercially available antimicrobials. We analyzed the findings from the discovered articles, including the reported minimal inhibitory concentrations. Across the literature, most B. anthracis isolates were reported as susceptible to current first-line antimicrobials recommended for postexposure prophylaxis and treatment. The data presented for potential alternative antimicrobials will be of use if significant resistance to first-line antimicrobials arises, the strain is bioengineered, or first-line antimicrobials are not tolerated or available.
The increased inter-brain neural synchronization in prefrontal cortex between simulated patient and acupuncturist during acupuncture stimulation: Evidence from functional near-infrared spectroscopy hyperscanning

The patient-acupuncturist interaction was a critical influencing factor for acupuncture effects but its mechanism remains unclear. This study aimed to examine the inter-brain mechanism of patient-acupuncturist dyad during acupuncture stimulation in a naturalistic clinical setting. Seventy healthy subjects (simulated "patients") were randomly assigned to two groups and received verum acupuncture group or sham acupuncture by one acupuncturist. Functional near-infrared spectroscopy hyperscanning was used to simultaneously record the neural responses of "patient"-acupuncturist dyad during acupuncture stimulation in each group. The results showed that inter-brain neural synchronization (INS) in the prefrontal cortex (PFC) of "patient"-acupuncturist dyad was significantly increased during verum but not sham acupuncture stimuli, and positively correlated with the needling sensations of "patients." Granger causality analysis demonstrated that there were no significant differences in INS direction between the "patient" and the acupuncturist. This study identified the increase of INS between "patient" and acupuncturist, and suggested that PFC was important to the interaction of "patient"-acupuncturist dyad.
Defensive Strongholds and Fortified Castles in Poland-From the Art of Fortifications to Tourist Attractions

The scientific problem undertaken is the importance of castles for the functioning of cultural tourism in the opinion of the inhabitants of Central Europe. What is the use of medieval monuments for the art of fortifications today? The main method of research is a diagnostic survey carried out with the use of a survey on a group of N, important according to the statistics in the number of n = 614 respondents. Statistical analyzes were performed using Statistica version 13.3. On the basis of the presented research results, it can be concluded that the interests of the respondents are very broad and varied. Taking into account the relatively large group of respondents, the research results can be considered reliable. An important goal was supplementing the knowledge, meeting the needs of learning about history, and getting acquainted with the prevailing historical tradition in castles of Europe. The questions presented here accent the interests of castles for the functioning of cultural tourism in the opinion of the inhabitants of Central Europe.
Rock art provides new evidence on the biogeography of kudu (Tragelaphus imberbis), wild dromedary, aurochs (Bos primigenius) and African wild ass (Equus africanus) in the early and middle Holocene of north-western Arabia

Aim: Our knowledge of the prehistoric distribution of animal species is so far largely dependent on the location of excavated archaeological and palaeontological sites. In the absence of excavated faunal remains, many species that were present in the Levant and North Africa have been assumed to have been absent on the Arabian Peninsula. Here, we explore representations of four species that were identifiable in the rock art, but had not previously been reported in north-western Arabia. Location: Jubbah and Shuwaymis UNESCO world heritage rock art sites in Ha'il province, north-western Saudi Arabia. Methods: In total, the rock art panels surveyed and recorded in Jubbah and Shuwaymis contain 6,618 individual animal depictions. Species were identified based on diagnostic features of the anatomy. The resulting dataset was then compared to the faunal spectrum reported in the (archaeo) zoological literature. Results: The rock art dataset provides evidence that the distributions of lesser kudu (Tragelaphus imberbis), wild camel and African wild ass (Equus africanus) extended into the north-west of Arabia and that the engravers may have had knowledge of aurochs (Bos primigenius). Main conclusions: The presence of previously undocumented mammal species in Arabia provides new information regarding their distribution, as well as the types of habitat and vegetation that were available in prehistoric landscapes. Moreover, the presence of kudu on the Arabian Peninsula indicates that the identification of palaeo-distributions based exclusively on faunal remains may miss key species in the Afro-Eurasian faunal exchange.
Aeronautical Networks for In-Flight Connectivity: A Tutorial of the State-of-the-Art and Survey of Research Challenges

The aeronautical networks attract the attention of both industry and academia since Internet access during flights turns to the crucial demand from luxury with the evolving technology. This In-Flight Connectivity (IFC) necessity is currently dominated by the satellite connectivity and Air-to-Ground (A2G) network solutions. However, the high installation/equipment cost and latency of the satellite connectivity reduce its efficiency. The A2G networks are utilized through the 4G/5G ground stations deployed on terrestrial areas to solve these satellites' problems. This terrestrial deployment reduces the coverage area of A2G networks, especially for remote flights over the ocean. The Aeronautical Ad-hoc Networks (AANETs) are designed to provide IFC while solving the primary defects of dominating solutions. The AANET is an entirely novel solution under the vehicular networks since it consists of aircraft with ultra-dynamic and unstructured characteristics. These characteristics separate it from the less dynamic Flying Ad-Hoc Networks (FANETs). Therefore, the environmental and mobility effects cause specific challenges for AANETs. This article presents a holistic review of these open AANET challenges by investigating them in data link, network, and transport layers. Before giving the details of these challenges, this article explores the state-of-the-art literature about satellite and A2G networks for IFC. We then give our specific interest to the AANET by investigating its particular characteristics and open research challenges. The main starting point of this study is that there is a lack of compact research on this exciting topic, although IFC is an inevitable need for the aeronautical industry. Also, the AANET could be underlined by giving all state-of-the-art about the dominating IFC solutions. Therefore, this is the first work exploring the state-of-the-art for all the existing aeronautical networking technologies under a single comprehensive survey by deeply analyzing specific characteristics and open research challenges of AANETs. Additionally, the AANET is a novel topic and should be separately investigated from the FANETs as given in current literature.
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyperdense connections in multi-modal representation learning. Our code is publicly available.
Minimum Mean-Square Error Estimation of Mel-Frequency Cepstral Features-A Theoretically Consistent Approach

In this work, we consider the problem of feature enhancement for noise-robust automatic speech recognition (ASR). We propose a method for minimum mean-square error (MMSE) estimation of mel-frequency cepstral features, which is based on a minimum number of well-established, theoretically consistent statistical assumptions. More specifically, the method belongs to the class of methods relying on the statistical framework proposed in Ephraim and Malah's original work ("Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-32, no. 6, 1984). The method is general in that it allows MMSE estimation of mel-frequency cepstral coefficients (MFCC's), cepstral-mean subtracted (CMS-) MFCC's, autoregressive-moving-average (ARMA)-filtered CMS-MFCC's, velocity, and acceleration coefficients. In addition, the method is easily modified to take into account other compressive non-linearities than the logarithm traditionally used for MFCC computation. In terms of MFCC estimation performance, as measured by MFCC mean-square error, the proposed method shows performance which is identical to or better than other state-of-the-art methods. In terms of ASR performance, no statistical difference could be found between the proposed method and the state-of-the-art methods. We conclude that existing state-of-the-art MFCC feature enhancement algorithms within this class of algorithms, while theoretically suboptimal or based on theoretically inconsistent assumptions, perform close to optimally in the MMSE sense.
Multipath Scheduling for 5G Networks: Evaluation and Outlook

The fifth generation (5G) of cellular networks aims at providing very high data rates, ultra-reliable low latency, and massive connection density. As one of the fundamental design trends toward these objectives, 5G exploits multi-connectivity (i.e., the concurrent use of multiple access networks), where multipath transport protocols have emerged as key technology enablers. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths and has a critical impact on the protocol performance. Within this context, we present in this article the first empirical evaluation of state-of-the-art multipath schedulers based on real 5G data, for both static and mobile scenarios. Furthermore, we introduce M-Peekaboo, which builds on a state-of-the-art learning-based multipath scheduler and extends its usage to 5G networks. Our results illustrate the benefits of employing a learning-based multipath scheduler for 5G networks and motivate further studies of advanced learning schemes that can adapt more quickly to the path conditions, and take into account the emerging features and requirements of 5G and beyond networks.
From genotypes to phenotypes: expression levels of genes encompassing adaptive SNPs in black spruce

Measuring transcript levels for adaptive genes revealed polymorphisms having cis -effect upon gene expression levels related to phenotype variation in a black spruce natural population. Trees growing in temperate and boreal regions must acclimate to changes in climatic factors such as low winter temperatures to survive to seasonal variations. Common garden studies have shown that genetic variation in quantitative traits helps species to survive and adapt to environmental changes and local conditions. Twenty-four genes carrying SNPs were previously associated with genetic adaptation in black spruce (Picea mariana [Mill.] BSP). The objectives of this study were to investigate the potential role of these genes in regulation of winter acclimation and adaptation by studying their patterns of expression as a function of the physiological stage during the annual growth cycle, tissue type, and their SNP genotypic class. Considerable variability in gene expression was observed between different vegetative tissues or organs, and between physiological stages. The genes were expressed predominantly in tissues that could be linked more directly to winter acclimation and adaptation. The expression levels of several of the genes were significantly related to variation in tree height growth or budset timing and expression level variation related to SNP genotypic classes was observed in four of the genes. An interaction between genotypic classes and physiological stages was also observed for some genes, indicating genotypes with different reaction norms in terms of gene expression.