ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Modified ART 2A growing network capable of generating a fixed number of nodes

This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an appropriate vigilance threshold. Empirical experiments compare the cluster validity and the learning efficiency of ART-C 2A with those of ART 2A, as well as three closely related clustering methods, namely online K-Means, batch K-Means, and SOM, in a quantitative manner. Besides retaining the online cluster creation capability of ART 2A, ART-C 2A gives the alternative clustering solution, which allows a direct control on the number of output clusters generated by the self-organizing process.
Structural studies of SALL family protein zinc finger cluster domains in complex with DNA reveal preferential binding to an AATA tetranucleotide motif

The Spalt-like 4 transcription factor (SALL4) plays an essential role in controlling the pluripotent property of embryonic stem cells via binding to AT-rich regions of genomic DNA, but structural details on this binding interaction have not been fully characterized. Here, we present crystal structures of the zinc finger cluster 4 (ZFC4) domain of SALL4 (SALL4ZFC4) bound with different dsDNAs containing a conserved AT-rich motif. In the structures, two zinc fingers of SALL4ZFC4 recognize an AATA tetranucleotide. We also solved the DNA-bound structures of SALL3ZFC4 and SALL4ZFC1. These structures illuminate a common preference for the AATA tetranucleotide shared by ZFC4 of SALL1, SALL3, and SALL4. Furthermore, our cell biology experiments demonstrate that the DNA-binding activity is essential for SALL4 function as DNA-binding defective mutants of mouse Sall4 failed to repress aberrant gene expression in Sall4-/- mESCs. Thus, these analyses provide new insights into the mechanisms of action underlying SALL family proteins in controlling cell fate via preferential targeting to AT-rich sites within genomic DNA during cell differentiation.
Grinding and fine finishing of future automotive powertrain components

The automotive industry is undergoing a major transformation driven by regulations and a fast-paced electrification. A critical analysis of technological trends and associated requirements for major automotive powertrain components is carried out in close collaboration with industry - covering the perspectives of OEMs, suppliers, and machine builders. The main focus is to review the state of the art with regard to grinding, dressing, texturing and fine-finishing technologies. A survey of research papers and patents is accompanied by case studies that provide further insights into the production value chain. Finally, key industrial and research challenges are summarized. (c) 2021 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Multiple endocrine neoplasia type 2 (MEN2) and RET specific modifications of the ACMG/AMP variant classification guidelines and impact on the MEN2 RET database

The Multiple Endocrine Neoplasia type 2 (MEN2) RET proto-oncogene database, originally published in 2008, is a comprehensive repository of all publicly available RET gene variations associated with MEN2 syndromes. The variant-specific genotype/phenotype information, age of earliest reported medullary thyroid carcinoma (MTC) onset, and relevant references with a brief summary of findings are cataloged. The ACMG/AMP 2015 consensus statement on variant classification was modified specifically for MEN2 syndromes and RET variants using ClinGen sequence variant interpretation working group recommendations and ClinGen expert panel manuscripts, as well as manuscripts from the American Thyroid Association Guidelines Task Force on Medullary Thyroid Carcinoma and other MEN2 RET literature. The classifications for the 166 single unique variants in the MEN2 RET database were reanalyzed using the MEN2 RET specifically modified ACMG/AMP classification guidelines (version 1). Applying these guidelines added two new variant classifications to the database (likely benign and likely pathogenic) and resulted in clinically significant classification changes (e.g., from pathogenic to uncertain) in 15.7% (26/166) of the original variants. Of those clinically significant changes, the highest percentage of changes, 46.2% (12/26), were changes from uncertain to benign or likely benign. The modified ACMG/AMP criteria with MEN2 RET specifications will optimize and standardize RET variant classifications.
A Neuro-Fuzzy System to Detect IPv6 Router Alert Option DoS Packets

Detecting the denial of service attacks that solely target the router is a maximum security imperative in deploying IPv6 networks. The state-of-the-art Denial of Service detection methods aim at leveraging the advantages of flow statistical features and machine learning techniques. However, the detection performance is highly affected by the quality of the feature selector and the reliability of datasets of IPv6 flow information. This paper proposes a new neuro-fuzzy inference system to tackle the problem of classifying the packets in IPv6 networks in crucial situation of small-supervised training dataset. The proposed system is capable of classifying the IPv6 router alert option packets into denial of service and normal by utilizing the neuro-fuzzy strengths to boost the classification accuracy. A mathematical analysis from the fuzzy sets theory perspective is provided to express performance benefit of the proposed system. An empirical performance test is conducted on comprehensive dataset of IPv6 packets produced in a supervised environment. The result shows that the proposed system overcomes robustly some state-of-the-art systems.
At the crossroad: A new approach to the Upper Paleolithic art in the Western Pyrenees

The main corridor that has acted as an entrance to the Iberian Peninsula through the Western Pyrenees was as an important communication route in Western Europe during the Paleolithic. This is an area where, despite having documented intense human occupation during different periods of the Upper Paleolithic, the symbolic activity seems incomprehensibly limited compared to other regions, such as the Cantabrian, the Northern Pyrenees, or Dordogne. The reactivation of research during this last decade, when referring to cave art, leads to a very different model than the one that had been previously posed for this region, mainly due to a series of new discoveries, some of which are worth mentioning, such as Askondo and Lumentxa, or others of lower interest such as Astigarraga, Praile Aitz, and Aitzbitarte IV. The re-examination and revision of previously known representations has also contributed to the development of this new model, especially thanks to the research carried out in sites such as Santimamine, Altxerri, Isturitz, Oxocelhaya, Sasiziloaga, Sinhikole, or Sainte Colome, as has the revision of the great amount of portable art, mainly from Isturitz, as well as the discovery of new objects from Antolina, Ekain, Praile Aitz, Aitzbitarte III, Santa Catalina, Arlanpe, and Bourrouilla. Due to these advances, the image that we have of the artistic activity around the epicentre that was the Gulf of Biscay has substantially varied in a way that allows us to carry out a more detailed approximation to a series of relevant questions such as the chronology of the artistic representations, the variability of their contexts, the technical and formal relations among different neighbouring regions, and their relation to the non-symbolic archaeological record. (C) 2014 Elsevier Ltd and INQUA. All rights reserved.
Caught in the act-Visualizing ribonucleases during eukaryotic ribosome assembly

Ribosomes are essential macromolecular machines responsible for translating the genetic information encoded in mRNAs into proteins. Ribosomes are composed of ribosomal RNAs and proteins (rRNAs and RPs) and the rRNAs fulfill both catalytic and architectural functions. Excision of the mature eukaryotic rRNAs from their precursor transcript is achieved through a complex series of endoribonucleolytic cleavages and exoribonucleolytic processing steps that are precisely coordinated with other aspects of ribosome assembly. Many ribonucleases involved in pre-rRNA processing have been identified and pre-rRNA processing pathways are relatively well defined. However, momentous advances in cryo-electron microscopy have recently enabled structural snapshots of various pre-ribosomal particles from budding yeast (Saccharomyces cerevisiae) and human cells to be captured and, excitingly, these structures not only allow pre-rRNAs to be observed before and after cleavage events, but also enable ribonucleases to be visualized on their target RNAs. These structural views of pre-rRNA processing in action allow a new layer of understanding of rRNA maturation and how it is coordinated with other aspects of ribosome assembly. They illuminate mechanisms of target recognition by the diverse ribonucleases involved and reveal how the cleavage/processing activities of these enzymes are regulated. In this review, we discuss the new insights into pre-rRNA processing gained by structural analyses and the growing understanding of the mechanisms of ribonuclease regulation. This article is categorized under: Translation > Ribosome Biogenesis RNA Processing > rRNA Processing.
Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores

Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering issues like the excessive feature dimension, small sample size and missing data. Due to the limited data, a compact but expressive feature set is desirable as it can reduce the dimension and avoid the potential overfitting issue. Therefore, we pioneer the path signature method to further explore the essential hidden dynamic patterns of longitudinal cortical features. To form a hierarchical and more informative temporal representation, in this work, a novel cortical feature based path signature neural network (CF-PSNet) is proposed with stacked differentiable temporal path signature layers for prediction of individual cognitive scores. By introducing the existence embedding in path generation, we can improve the robustness against the missing data. Benefiting from the global temporal receptive field of CF-PSNet, characteristics consisted in the existing data can be fully leveraged. Further, as there is no need for the whole brain to work for a certain cognitive ability, a top K selection module is used to select the most influential brain regions, decreasing the model size and the risk of overfitting. Extensive experiments are conducted on an in-house longitudinal infant dataset within 9 time points. By comparing with several recent algorithms, we illustrate the state-of-the-art performance of our CF-PSNet (i.e., root mean square error of 0.027 with the time latency of 518 milliseconds for each sample).
Monocular Visual Scene Understanding: Understanding Multi-Object Traffic Scenes

Following recent advances in detection, context modeling, and tracking, scene understanding has been the focus of renewed interest in computer vision research. This paper presents a novel probabilistic 3D scene model that integrates state-of-the-art multiclass object detection, object tracking and scene labeling together with geometric 3D reasoning. Our model is able to represent complex object interactions such as inter-object occlusion, physical exclusion between objects, and geometric context. Inference in this model allows us to jointly recover the 3D scene context and perform 3D multi-object tracking from a mobile observer, for objects of multiple categories, using only monocular video as input. Contrary to many other approaches, our system performs explicit occlusion reasoning and is therefore capable of tracking objects that are partially occluded for extended periods of time, or objects that have never been observed to their full extent. In addition, we show that a joint scene tracklet model for the evidence collected over multiple frames substantially improves performance. The approach is evaluated for different types of challenging onboard sequences. We first show a substantial improvement to the state of the art in 3D multipeople tracking. Moreover, a similar performance gain is achieved for multiclass 3D tracking of cars and trucks on a challenging dataset.
An Ensemble Colorization Method

Colorization is a well established problem concerning recovering the potential colors of monochrome images. In the past decade several highly effective computer-assisted methods have been proposed for solving the colorization problem, among which two methods achieved the state-of-the-art performance. The two methods are totally different, and each of them has its advantages and disadvantages. We propose an ensemble method unifying the two state-of-the-art methods to better exploit their advantages. Empirical results demonstrate the superiority of our ensemble method over using each of the two methods alone.
Flood or Non-Flooded: A Comparative Study of State-of-the-Art Models for Flood Image Classification Using the FloodNet Dataset with Uncertainty Offset Analysis

Natural disasters, such as floods, can cause significant damage to both the environment and human life. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. In this paper, we aimed to evaluate the performance of state-of-the-art (SOTA) computer vision models for flood image classification, by utilizing a semi-supervised learning approach on a dataset named FloodNet. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. Furthermore, we also introduced a technique of varying the uncertainty offset lambda in the models to analyze its impact on the performance. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. The results of this study provide a quantitative comparison of the performance of different CNN architectures for flood image classification, as well as the impact of different uncertainty offset lambda. These findings can aid in the development of more accurate and efficient disaster response and recovery systems, which could help in minimizing the impact of natural disasters.
Differential Evolutionary Superpixel Segmentation

Superpixel segmentation has been of increasing importance in many computer vision applications recently. To handle the problem, most state-of-the-art algorithms either adopt a local color variance model or a local optimization algorithm. This paper develops a new approach, named differential evolutionary superpixels, which is able to optimize the global properties of segmentation by means of a global optimizer. We design a comprehensive objective function aggregating within-superpixel error, boundary gradient, and a regularization term. Minimizing the within-superpixel error enforces the homogeneity of superpixels. In addition, the introduction of boundary gradient drives the superpixel boundaries to capture the natural image boundaries, so as to make each superpixel overlaps with a single object. The regularizer further encourages producing similarly sized superpixels that are friendly to human vision. The optimization is then accomplished by a powerful global optimizer-differential evolution. The algorithm constantly evolves the superpixels by mimicking the process of natural evolution, while using a linear complexity to the image size. Experimental results and comparisons with eleven state-of-the-art peer algorithms verify the promising performance of our algorithm.
Segmentation of the True Lumen of Aorta Dissection via Morphology-Constrained Stepwise Deep Mesh Regression

The lumen of aortic dissection (AD) has important clinical value for preoperative diagnosis, interoperative intervention, and post-operative evaluation of AD diseases. AD segmentation is challenging because (i) fitting its irregular profile by using traditional models is difficult, and (ii) the size of the AD image is usually so big that many algorithms have to perform down-sampling to reduce the computational burden, thereby reducing the resolution of the result. In this paper, an automatic AD segmentation algorithm, in which a 3D mesh is gradually moved to the surface of AD based on the offset estimated by a deep mesh deformation module, is presented. AD morphology is used to constrain the initial mesh and guide the deformation, which improves the efficiency of the deep network and avoids down-sampling. Moreover, a stepwise regression strategy is introduced to solve the mesh folding problem and improve the uniformity of the mesh points. On an AD database that involves 35 images, the proposed method obtains the mean Dice of 94.12% and symmetric 95% Hausdorff distance of 2.85 mm, which outperforms five state-of-the-art AD segmentation methods. The average processing time is 16.6 s, and the memory used to train the network is only 0.36 GB, indicating that this method is easy to apply in clinical practice.
Research, Development and Simulation of Quantum Cryptographic Protocols

Cryptography is the art of hiding information. Quantum cryptography is the next level in cryptography. This paper presents the development and the software simulation of BB84 without eavesdropper, BB84 with eavesdropper, BB84 with QBTT eavesdropper detection method and BSPA quantum cryptographic communication protocol.
A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation

Point cloud recognition is an essential task in industrial robotics and autonomous driving. Recently, several point cloud processing models have achieved state-of-the-art performances. However, these methods lack rotation robustness, and their performances degrade severely under random rotations, failing to extend to real-world scenarios with varying orientations. To this end, we propose a method named self-contour-based transformation (SCT), which can be flexibly integrated into various existing point cloud recognition models against arbitrary rotations. SCT provides efficient rotation and translation invariance by introducing contour-aware transformation (CAT), which linearly transforms the Cartesian coordinates of points to translation and rotation-invariant representations. We prove that CAT is a rotation- and translation-invariant transformation based on the theoretical analysis. Furthermore, the frame alignment module is proposed to enhance the discriminative feature extraction by capturing contours and transforming self-contour-based frames into intraclass frames. Extensive experimental results show that SCT outperforms the state-of-the-art approaches under arbitrary rotations in effectiveness and efficiency on synthetic and real-world benchmarks. Furthermore, the robustness and generality evaluations indicate that SCT is robust and is applicable to various point cloud processing models, which highlights the superiority of SCT in industrial applications.
ATP-binding cassette transporter A7 (ABCA7) loss of function alters Alzheimer amyloid processing

The ATP-binding cassette transporter A7 (ABCA7) has been identified as a susceptibility factor of late onset Alzheimer disease in genome-wide association studies. ABCA7 has been shown to mediate phagocytosis and affect membrane trafficking. The current study examined the impact of ABCA7 loss of function on amyloid precursor protein (APP) processing and generation of amyloid-β (Aβ). Suppression of endogenous ABCA7 in several different cell lines resulted in increased β-secretase cleavage and elevated Aβ. ABCA7 knock-out mice displayed an increased production of endogenous murine amyloid Aβ42 species. Crossing ABCA7-deficient animals to an APP transgenic model resulted in significant increases in the soluble Aβ as compared with mice expressing normal levels of ABCA7. Only modest changes in the amount of insoluble Aβ and amyloid plaque densities were observed once the amyloid pathology was well developed, whereas Aβ deposition was enhanced in younger animals. In vitro studies indicated a more rapid endocytosis of APP in ABCA7 knock-out cells that is mechanistically consistent with the increased Aβ production. These in vitro and in vivo findings indicate a direct role of ABCA7 in amyloid processing that may be associated with its primary biological function to regulate endocytic pathways. Several potential loss-of-function ABCA7 mutations and deletions linked to Alzheimer disease that in some instances have a greater impact than apoE allelic variants have recently been identified. A reduction in ABCA7 expression or loss of function would be predicted to increase amyloid production and that may be a contributing factor in the associated Alzheimer disease susceptibility.
Comparing stakeholder incentives across state-of-the-art renewable support mechanisms

Traditional support policies for green energy have greatly contributed to the rise in prosumer numbers. However, it is believed that they will soon start to exert negative impact on stakeholders and on the grid. Policy makers advise to phase out two of the most widely applied policies - net metering and feed-in tariff, in favor of support policies that scale better with rising renewable generation. This work quantifies the impact of these traditional policies in future "what-if" scenarios and confirms the need for their replacement. Based on simulations with real data, we compare net metering and feed-in tariff to four state-of-the-art market-based mechanisms, which involve auction, negotiation and bitcoin-like currency. The paper examines the extent to which each of these mechanisms motivates not only green energy production but also its consumption. The properties and characteristics of the above mechanisms are evaluated from the perspective of key stakeholders in the low voltage grid - prosumers, consumers and energy providers. The outcome of this study sheds light on current and future issues that are relevant for policy makers in the evolving landscape of the smart grid. (C) 2018 Elsevier Ltd. All rights reserved.
Liver Metastasis of Thymoma: Case Report and Review of the Literature

The 2021 "World Health Organization (WHO) Classification of Thoracic Tumours" classifies epithelial tumors of the thymus (thymomas) based on cytomorphology. Thymomas with benign cytomorphology are classified as type A, AB, B1, B2, and B3, while those with malignant cytomorphology are classified as thymic carcinoma. Although all thymomas have malignant potential, extra-thoracic metastasis of thymomas is exceedingly rare and the exact incidence is not known. Literature review demonstrated 39 cases of thymoma with extra-thoracic metastases reported since the publication of the 1999 WHO Classification of Thoracic Tumours. Nine of these cases presented with metastasis to the liver in the setting of concurrent metastasis to other organs, while only three cases metastasized solely to the liver. We herein report a well-documented case of type B1 thymoma with relatively limited stage (IIb) with an isolated, single liver metastasis occurring seven years after primary resection in a patient with concomitant myasthenia gravis. The following report includes a review of the literature, a discussion of thymoma classification and its relevance to prognosis, and an overview of other extra-thoracic metastatic thymoma cases.