ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Addendum: Implicit learning of temporal behavior in complex dynamic environments

New analyses of the data in this study (Salet et al., 2021, Psychonomic Bulletin & Review, https://doi.org/10.3758/s13423-020-01873-x ) have led us to reinterpret our main finding. Previously, we had attributed better performance for targets appearing at regular intervals versus irregular intervals to "temporal statistical learning." That is, we surmised that this benefit for the regular intervals arises because participants implicitly distilled the regular 3000 ms interval from the otherwise variable environment (i.e., irregular intervals) to predict future (regular) targets. The analyses presented in this Addendum, however, show that this benefit can be attributed to ongoing "temporal preparation" rather than temporal statistical learning.
On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and (1, s)-sparse random binary matrix [(1, s)-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and (1, s)-SRBM encoders with reduced area and total power consumption.
High-yield identification of pathogenic NF1 variants by skin fibroblast transcriptome screening after apparently normal diagnostic DNA testing

Neurofibromatosis type 1 (NF1) is caused by inactivating mutations in NF1. Due to the size, complexity, and high mutation rate at the NF1 locus, the identification of causative variants can be challenging. To obtain a molecular diagnosis in 15 individuals meeting diagnostic criteria for NF1, we performed transcriptome analysis (RNA-seq) on RNA obtained from cultured skin fibroblasts. In each case, routine molecular DNA diagnostics had failed to identify a disease-causing variant in NF1. A pathogenic variant or abnormal mRNA splicing was identified in 13 cases: 6 deep intronic variants and 2 transposon insertions causing noncanonical splicing, 3 postzygotic changes, 1 branch point mutation and, in 1 case, abnormal splicing for which the responsible DNA change remains to be identified. These findings helped resolve the molecular findings for an additional 17 individuals in multiple families with NF1, demonstrating the utility of skin-fibroblast-based transcriptome analysis for molecular diagnostics. RNA-seq improves mutation detection in NF1 and provides a powerful complementary approach to DNA-based methods. Importantly, our approach is applicable to other genetic disorders, particularly those caused by a wide variety of variants in a limited number of genes and specifically for individuals in whom routine molecular DNA diagnostics did not identify the causative variant.
Enhanced coagulation and oxidation by the Mn(VII)-Fe(III)/peroxymonosulfate process: Performance and mechanisms

To improve the performance of the conventional coagulation process, a permanganate (Mn(VII)) pre-oxidation combined with Fe(III)/peroxymonosulfate (PMS) coagulation process (Mn(VII)-Fe(III)/PMS) that can significantly improve the removal of dissolved organic carbon (DOC), turbidity, and micropollutants is proposed in this study. Compared with conventional Fe(III) coagulation, the Mn(VII)-Fe(III)/PMS process can also significantly enhance the removal of iohexol and sulfamethoxazole in raw water. During this process, the primary reduction product, Mn(IV), after Mn(VII) pre-oxidation was adsorbed on the floc surfaces and involved in the Fe(III)/PMS process. The natural organic matter (NOM) in raw water mediated the redox cycle of iron. The synergistic effect of NOM, Fe, and Mn facilitated the redox cycle of Mn(III)/Mn(IV) and Fe(III)/Fe(II) to promote the activation of PMS. The sulfate radical (SO4•-) played an important role in the degradation of micropollutants. The formation potential of the detected volatile disinfection by-product (DBP) during the subsequent chlorination was reduced by 21.9% after the Mn(VII)-Fe(III)/PMS process. This study demonstrated the promising application of the Mn(VII)-Fe(III)/PMS process for coagulation and micropollutant control and illustrated the reaction mechanism. This study provides guidance for improving conventional drinking water treatment processes.
(Dis-)Connected Dots in Dementia with Lewy Bodies-A Systematic Review of Connectivity Studies

Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Bayes Saliency-Based Object Proposal Generator for Nighttime Traffic Images

Object proposal is one of the most key preprocessing steps for nighttime vehicle detection systems in intelligent transportation systems. However, most current object proposal methods are developed on daytime data sets, and these methods demonstrate unsatisfactory results when they are used on nighttime images. Therefore, this paper presents a novel Bayes saliency-based object proposal generator for nighttime RGB traffic images to generate a modest and accurate set of proposals, which are more likely to be vehicles for preceding vehicle detection. First, we propose a new Bayes saliency detection approach in which prior estimation, feature extraction, weight estimation, and Bayes rule are used to compute saliency maps. Then, we propose a simple but effective object proposal generator based on the Bayes saliency map. Multi-scale sliding window, proposal rejecting, scoring, and non-maximum suppression are combined to generate a modest and effective set of proposals. Experimental results demonstrate that our proposed approach generates a modest set of proposals and outperforms some state-of-the-art methods on nighttime images in terms of various evaluation metrics. Furthermore, our proposed object proposal approach can improve the detection performance and the speed of several state-of-the-art vehicle detection approaches.
Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.
Feedback cascade regression model for face alignment

Face alignment has made great progress in recent years and the cascade regression framework is one of the main contributors. However, the performance of this framework is unsatisfactory on heavily occluded faces or those far from the frontal pose. This is because regression is sensitive to hidden landmarks and unified initialisation can often lead to the method falling into local minima. The authors propose a new pipeline of salient-to-inner-to-all to progressively compute the locations of landmarks. Additionally, a feedback process is utilised to improve the robustness of regression. They bring out a pose-invariant shape retrieval method to generate the discriminative initialisation. Experiments are performed on two benchmarks, and the experimental results demonstrate that the proposed method has a considerable improvement on the cascade regression model, and achieves favourable results compared with the state-of-the-art deep learning-based methods.
Cleaner production, Process Integration and intensification

A considerable number of studies have been performed and are ongoing for enhancing cleaner production, process integration and also process intensification. These topics reflect some of the most important challenges of our society and have been targeted in this journal. Considerable research effort has been devoted to addressing process integration and process intensification as well as environmentally friendlier production. This article has made an attempt to provide a short assessment of the current state-of-art covered in the recent publications.
Predictor-Based Tensor Regression (PBTR) for LPV subspace identification

The major bottleneck in state-of-the-art Linear Parameter Varying (LPV) subspace methods is the curse of-dimensionality during the first regression step. In this paper, the origin of the curse-of-dimensionality is pinpointed and subsequently a novel method is proposed which does not suffer from this bottleneck. The problem is related to the LPV sub-Markov parameters. These have inherent structure and are dependent on each other. But state-of-the-art LPV subspace methods parametrize the LPV sub-Markov parameters independently. This means the inherent structure is not preserved in the parametrization. In turn this leads to a superfluous parametrization with the curse-of-dimensionality. The solution lies in using parametrizations which preserve the inherent structure sufficiently to avoid the curse of-dimensionality. In this paper a novel method based on tensor regression is proposed. This novel method is named the Predictor-Based Tensor Regression method (PBTR). This method preserves the inherent structure sufficiently to avoid the curse-of-dimensionality. Simulation results show that PBTR has superior performance with respect to both state-of-the-art LPV subspace techniques and also non convex techniques. (C) 2017 Elsevier Ltd. All rights reserved.
Role of gasdermin family proteins in the occurrence and progression of hepatocellular carcinoma

Primary liver cancer is the sixth most common cancer and the third leading cause of cancer mortality worldwide, hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for 75%-85% of cases. The occurrence and progression of HCC involve multiple events. Pyroptosis is a gasdermins mediated programmed cell death and is intricately associated with cancerogenesis, including HCC. This review mainly concerns the recent research advances of the gasdermin family members in HCC. The biological roles and specific expression patterns of the family members are discussed, especially those that are involved in the regulatory pathways in the occurrence and progression of HCC. We provide the latest progress into the distinct molecular mechanisms of gasdermin family members involved in the occurrence and development of HCC.
Shigir idol: Origin of monumental sculpture and ideas about the ways of preservation of the representational tradition

The article presents some new ideas about the Big Shigir Idol erection, ways of positioning and fixing the wooden sculpture on a Mansy sanctuary in trans-Uralian taiga. According to art study criteria, the Shigir Idol can be referred to monumental sculpture. Its Mesolithic age allows to put it at the beginning of the pre-literate period monumental sculpture range in Eurasian history - sculptures of Yamnaya, Chemurchek and Okunevo cultures, Scythian deer stones and Middle Age stone idols. The Big Shigir Idol's anthropomorphic iconographic characteristics, synthesis with its decorative features demonstrate a fully formed representational tradition. In spite of the idea of Ural Mesolithic Age origin, the author believes that the Shigir Idol's representational tradition could have been formed in the local Taiga environment unlike the art and craft tradition. The discussion concerning the problem of the long existence of this tradition follows the part about the spreading of anthropomorphic representations in cultures of the Neolithic, Bronze and Iron Ages in Northern Europe, the part about the representational traditions trends. As for the problem of Mesolithic representational tradition preservation in wooden sculpture up to modern and contemporary times in modern indigenous peoples in Western Siberia, it is not possible to solve it with archaeological sources yet. The author justifies this idea and suggests an original way to solve it.
Interstitial Outburst of Angiogenic Factors During Skeletal Muscle Regeneration After Acute Mechanical Trauma

Angiogenesis is a key event during tissue regeneration, but the intimate mechanisms controlling this process are still largely unclear. Therefore, the cellular and molecular interplay along normal tissue regeneration should be carefully unveiled. To this matter, we investigated by xMAP assay the dynamics of some angiogenic factors known to be involved in tissue repair, such as follistatin (FST), Placental Growth Factor-2 (PLGF-2), epidermal growth factor (EGF), betacellulin (BTC), and amphiregulin (AREG) using an animal model that mimics acute muscle contusion injuries. In situ immunofluorescence was used for the evaluation and tissue distribution of their cellular sources. Tissue levels of explored factors increased significantly during degeneration and inflammatory stage of regeneration, peaking first week postinjury. However, except for PLGF-2 and EGF, their levels remained significantly elevated after the inflammatory process started to fade. Serum levels were significantly increased only after 24 h for AREG and EGF. Though, for all factors except FST, the levels in injured samples did not correlate with serum or contralateral tissue levels, excluding the systemic influence. We found significant correlations between the levels of EGF and AREG, BTC, FST and FST and AREG in injured samples. Interstitial cells expressing these factors were highlighted by in situ immunolabeling and their number correlated with measured levels dynamics. Our study provides evidence of a dynamic level variation along the regeneration process and a potential interplay between selected angiogenic factors. They are synthesized, at least partially, by cell populations residing in skeletal muscle interstitium during regeneration after acute muscle trauma.
Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages-no-DR, mild, moderate, severe and proliferative DR-as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.
The State-of-the-Art of Human-Drone Interaction: A Survey

Drones have expanded from military operations to performing a broad range of civilian applications. As drone usage increases, humans will interact with such systems more often, therefore, it is important to achieve a natural human-drone interaction. Although some knowledge can be derived from the field of human-robot interaction, drones can fly in a 3D space, which essentially changes how humans can interact with them, making human-drone interaction a field of its own. This paper is the first survey on the emerging field of human-drone interaction focusing on multi-rotor systems, providing an overview of existing literature and the current state of the art in the field. This work begins with an analysis and comparison of the drone models that are commonly used by end-users and researchers in the field of human-drone interaction. Following, the current state of the field is discussed, including the roles of humans in HDI, innovative control methods, remaining aspects of interaction, and novelty drone prototypes and applications. This paper concludes by presenting a discussion of current challenges and future work in the field of human-drone interaction.
Transient neonatal hemolytic anemia due to the novel gamma globin gene mutation HBG2:C.290T>C, p.Leu97Pro (hemoglobin Wareham)

Unstable gamma globin variants can cause transient neonatal hemolytic anemia. We have identified a novel variant in a newborn who presented with jaundice and anemia requiring phototherapy and red blood cell transfusion. The patient was found to be heterozygous for the mutation HGB2:c.290T>C, p.Leu97Pro, which we have termed hemoglobin (Hb) Wareham. This substitution is expected to generate an unstable hemoglobin with increased oxygen affinity based on the homologous mutation previously described in the beta globin gene, which is termed as Hb Debrousse. The patient fully recovered by 9 months of age as expected with the transition from fetal to adult hemoglobin.
Robust and energy-efficient carbon nanotube FET-based MVL gates: A novel design approach

In this paper energy-efficient multiple valued logic (MVL) circuits based on carbon nanotube field effect transistor (CNTFET) are proposed. These circuits are designed based on the unique properties of CNTFETs, such as having same mobility for electrons and holes and also capability of adopting desirable threshold voltage by adjusting the CNTs diameters. The proposed designs have high driving capability, larger noise margins and higher robustness as compared to the previous CNTFET-based designs. The proposed quaternary circuits are examined using HSPICE simulator with the standard CNTFET technology. Simulation results demonstrate more energy-efficient and robust operation of the proposed designs, as compared to the other state-of-the-art CNTFET-based MVL circuits, recently presented in the literature. According to the simulation results the proposed STNOT, STNAND and STNOR circuits have on average 82%, 76% and 45% lower power-delay product (PDP), respectively as compared to their state-of-the-art counterparts. In addition, the proposed QNOT, QNAND and QNOR circuits have the average PDP improvements of 79%, 42% and 61%, respectively, as compared the other recently presented CNTFET-based quaternary designs. (C) 2015 Elsevier Ltd. All rights reserved.
Integrated Microwave Photonics for Radio Access Networks

We explore the advantages that integrated microwave photonics (IMWP) can bring to access networks. We first of all review the most common architectures of radio access networks (RANs) to identify the segments where microwave photonic components and radio over fiber links are located. Then, we provide a short description of the basic principles of IMWP with the aim of illustrating the current state of the art of this technology and its potentials. We discuss the possibilities of incorporating IMWP technology into the RAN front-haul. In particular, we first of all identify the required MWP functionalities and then discuss the feasibility of implementing these in light of the current and near future state of the art.