ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Time Series Data Cleaning: A Survey

Errors are prevalent in time series data, which is particularly common in the industrial field. Data with errors could not be stored in the database, which results in the loss of data assets. At present, to deal with these time series containing errors, besides keeping original erroneous data, discarding erroneous data and manually checking erroneous data, we can also use the cleaning algorithm widely used in the database to automatically clean the time series data. This survey provides a classification of time series data cleaning techniques and comprehensively reviews the state-of-the-art methods of each type. Besides we summarize data cleaning tools, systems and evaluation criteria from research and industry. Finally, we highlight possible directions time series data cleaning.
Protein-corona formation on aluminum doped zinc oxide and gallium nitride nanoparticles

The interaction of semiconductor nanoparticles with bio-molecules attracts increasing interest of researchers, considering the reactivity of nanoparticles and the possibility to control their properties remotely giving mechanical, thermal, or electrical stimulus to the surrounding bio-environment. This work reports on a systematic comparative study of the protein-corona formation on aluminum doped zinc oxide and gallium nitride nanoparticles. Bovine serum albumin was chosen as a protein model. Dynamic light scattering, transmission electron microscopy and X-ray photoelectron spectroscopy techniques have been used to demonstrate the formation of protein-corona as well as the stability of the colloidal suspension given by BSA, which also works as a surfactant. The protein adsorption on the NPs surface studied by Bradford Assay showed the dependence on the quantity of proteins adsorbed to the available sites on the NPs surface, thus the saturation was observed at ratio higher than 5:1 (NPs:Proteins) in case of ZnO, these correlating with DLS results. Moreover, the kinetics of the proteins showed a relatively fast adsorption on the NPs surface with a saturation curve after about 25 min. GaN NPs, however, showed a very small amount of proteins adsorbed on the surface, a change in the hydrodynamic size being not observable with DLS technique or differential centrifugal sedimentation. The Circular Dichroism analysis suggests a drastic structural change in the secondary structure of the BSA after attaching on the NPs surface. The ZnO nanoparticles adsorb a protein-corona, which does not protect them against dissolution, and in consequence, the material proved to be highly toxic for Human keratinocyte cell line (HaCaT) at concentration above 25 µg/mL. In contrast, the GaN nanoparticles which do not adsorb a protein-corona, show no toxicity signs for HaCaT cells at concentration as high as 50 µg/mL, exhibiting much lower concentration of ions leakage in the culture medium as compared to ZnO nanoparticles.
PASE: Synthesizing Existing Transport Strategies for Near-Optimal Data Center Transport

Several data center transport protocols have been proposed in recent years (e.g., DCTCP, PDQ, and pFabric). In this paper, we first identify the underlying strategies used by the existing data center transports, namely, in-network Prioritization (used in pFabric), Arbitration (used in PDQ), and Self-adjusting at Endpoints (PASE) (used in DCTCP). We show that these strategies are complimentary to each other, rather than substitutes, as they have different strengths and can address each other's limitations. Unfortunately, prior data center transports use only one of these strategies. As a result, they either achieve near-optimal performance or deployment friendliness (i.e., require no changes to the data plane) but not both. Based on this insight, we design a data center transport protocol called PASE, which carefully synthesizes these strategies by assigning different transport responsibilities to each strategy. The key advantage of PASE over prior art is that it achieves both near-optimal performance as well as deployment friendliness. PASE does not require any changes in network switches (hardware or software); yet, it achieves comparable, or even better, performance than the state-of-the-art protocols (such as pFabric) that require changes to network elements. Our evaluation results show that the PASE performs well for a wide range of application workloads and network settings.
Securing Outsourced VNFs: Challenges, State of the Art, and Future Directions

It is becoming increasingly common for enterprises to outsource network functions to a third party provider such as a public cloud. Besides its well documented benefits in cost and flexibility, outsourcing also introduces security issues. Peeking into or modifying traffic destined to the cloud are not the only threats we have to deal with; it can also be desirable to protect VNF code, input policies, and states from a malicious cloud provider. In recent years several solutions have been proposed toward mitigating the threats of outsourcing VNFs, using either cryptographic or trusted hardware- based mechanisms (the latter typically applying SGX). In this article, we provide an overview of methods for protecting the security of outsourced network functions. We introduce the challenges and emerging requirements, analyze the state of the art, and identify the gaps between the requirements and existing solutions. Furthermore, we outline a potential way to fill these gaps in order to devise a more complete solution.
Efficient implementation of cryptographically useful "large" Boolean functions

We present low cost hardware architecture for implementing state-of-the-art theoretical constructions of secure Boolean functions suitable for stream ciphers. Using a pipelined architecture, we show that it is possible to implement systems which use Boolean functions of a relatively large number of variables. Our architecture is reconfigurable and provide a universal circuit for a certain class of secure Boolean functions.
Improving efficiency of entropy-driven DNA amplification biosensing through producing two label-free signal strands in one cycle

Entropy-driven circuits (EDC) provides one isothermal and non-enzymatic signal amplification strategy. But the efficiency of EDC signal amplification is not enough high because only one single strand can be produced in each cycle of the typical EDC system. In this work, we proposed one strategy to improve the amplification efficiency of EDC-based biosensing. In this strategy, two signal strands were produced in one cycle. The G-triplex (G3)-forming sequence was used as signal strand, and the G3/thioflavin T (G3/ThT) was used as label-free fluorescence reporter in this EDC-based biosensing. The detection limit of this method was estimated to be 3.4 pM for target DNA, which was about 10 times lower than that of the conventional EDC method. Furthermore, the response time was shortened from more than 1 h-0.5 h. In a word, one enzyme-free and label-free EDC strategy was proposed to construct an efficient nucleic acid biosensing platform.
Authenticating the writings of Julius Caesar

In this paper, we shed new light on the authenticity of the Corpus Caesarianum, a group of five commentaries describing the campaigns of Julius Caesar (100-44 BC), the founder of the Roman empire. While Caesar himself has authored at least part of these commentaries, the authorship of the rest of the texts remains a puzzle that has persisted for nineteen centuries. In particular, the role of Caesar's general Aulus Hirtius, who has claimed a role in shaping the corpus, has remained in contention. Determining the authorship of documents is an increasingly important authentication problem in information and computer science, with valuable applications, ranging from the domain of art history to counter-terrorism research. We describe two state-of-the-art authorship verification systems and benchmark them on 6 present-day evaluation corpora, as well as a Latin benchmark dataset. Regarding Caesar's writings, our analyses allow us to establish that Hirtius's claims to part of the corpus must be considered legitimate. We thus demonstrate how computational methods constitute a valuable methodological complement to traditional, expert-based approaches to document authentication. (C) 2016 Elsevier Ltd. All rights reserved.
Geometrical-based approach for robust human image detection

In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches.
Flagella, Chemotaxis and Surface Sensing

Based on genome analyses, it has been estimated that more than half of the bacteria have made an important investment into motility since they possess genes encoding the flagellar motor, the flagellum, chemosensory pathways and chemoreceptors. The metabolic burden associated with gene maintenance, protein synthesis and operating these systems is very important. A central question is thus to establish the physiological benefits that compensate such an important investment. In this chapter, we illustrate that benefits are multiple and diverse, including access to nutrients and preferred niches, biofilm formation and bacterial dispersal. There is also evidence that the complete range of advantages still remains to be defined. In these research efforts, Pseudomonas aeruginosa (PA) has played a central role and is among the central model species. Research conducted on PA had a significant impact in the field and has motivated many experiments in the study of other model bacterial species.
Segmentation-based tracking by support fusion

In this paper we present a novel fusion framework to combine the diverse outputs of arbitrary trackers, which are typically not directly combinable, allowing for significantly increasing the tracking quality. Our main idea is first to transform individual tracking outputs such as motion inliers, bounding boxes, or specific target image features to a shared pixel-based representation and then to run a fusion step on this representation. The fusion process additionally provides a segmentation, which, in turn, further allows for a dynamic weighting of the specific trackers' contributions. In particular, we demonstrate our fusion concept by combining three diverse heterogeneous tracking approaches that significantly differ in methodology as well as in their reported outputs. In the experiments we show that the proposed fusion strategy can successfully handle highly complex non-rigid object scenarios where the individual trackers and state-of-the-art (non-rigid object and fusion based) trackers fail. We demonstrate high performance on a large number of challenging sequences, where we clearly outperform the individual trackers as well as state-of-the-art tracking approaches. (c) 2013 Elsevier Inc. All rights reserved.
DRESS LIGHT DESIGN

A short review of some state-of-the-art examples in dress light design is given. The review follows on from the publication with participation of the author in the Svetotekhnika Journal #4 2011.
Diffuse Optical Tomography Enhanced by Clustered Sparsity for Functional Brain Imaging

Diffuse optical tomography (DOT) is a noninvasive technique which measures hemodynamic changes in the tissue with near infrared light, which has been increasingly used to study brain functions. Due to the nature of light propagation in the tissue, the reconstruction problem is severely ill-posed. For linearized DOT problems, sparsity regularization has achieved promising results over conventional Tikhonov regularization in recent experimental research. As extensions to standard sparsity, it is widely known that structured sparsity based methods are often superior in terms of reconstruction accuracy, when the data follows some structures. In this paper, we exploit the structured sparsity of diffuse optical images. Based on the functional specialization of the brain, it is observed that the in vivo absorption changes caused by a specific brain function would be clustered in certain region(s) and not randomly distributed. Thus, a new algorithm is proposed for this clustered sparsity reconstruction (CSR). Results of numerical simulations and phantom experiments have demonstrated the superiority of the proposed method over the state-of-the-art methods. An example from human in vivo measurements further confirmed the advantages of the proposed CSR method.
Should we manage the process of inventing? Designing for patentability

Intellectual property is considered to provide the infrastructure of innovation, and companies could proactively generate their intellectual assets and strengthen the business opportunities by focusing on discovery phases. This paper examines whether the invention process can be managed and finds that patents appear not only as a result of inventive activity but as the purpose as well. By building on recent design theories such as the concept-knowledge design theory, this research introduces a general framework that enables controlling for 'patentability' criteria, describes a patent in a unique way using actions, effects, and associated knowledge, and defines a patentable subject matter based on the notion of the person skilled in the art. Using the introduced model, several patent design methods are compared and their performances are characterized. The model was tested within the European semiconductor manufacturer, STMicroelectronics. The results indicate that the quality of patent proposals depends on the capacity to extend existing knowledge combinations, to overcome the initial design reasoning of the person skilled in the art, and to ensure novelty and sufficient inventive step. Finally, the proposed model in this research, the 'design-for-patentability' model, demonstrates that there is an unexplored property of the concept-knowledge design theory-non-substitution-showing that the order within design is irreversible and influences the quality of results.
Leukocyte integrin α4β7 associates with heat shock protein 70

The leukocyte integrin cell adhesion molecules α4β7 and αEβ7 mediate the homing and retention of lymphocytes to the gut, and sites of inflammation. Here we have identified heat shock protein 70 (HSP70) as a major protein that associates with the cytoplasmic domain of the integrin β7 subunit. HSPs are molecular chaperones that protect cells from stress but more recently have been reported to also regulate cell adhesion and invasion via modulation of β1, β2, and β3 integrins and integrin-associated signalling molecules. Several HSP70 isoforms including HSP70-3, HSP70-1L, HSP70-8, and HSP70-9 were specifically precipitated from T cells by a bead-conjugated β7 subunit cytoplasmic domain peptide and subsequently identified by high-resolution liquid chromatography-tandem mass spectrometry. In confirmation, the β7 subunit was co-immunoprecipitated from a T cell lysate by an anti-HSP70 antibody. Further, recombinant human HSP70-1a was precipitated by β7 cytoplasmic domain-coupled beads. The HSP70 inhibitor KNK437 decreased the expression of HSP70 without affecting the expression of the β7 integrin. It significantly inhibited α4β7-mediated adhesion of T cells to mucosal addressin cell adhesion molecule 1 (MAdCAM-1), suggesting HSP70 is critical for maintaining β7 integrin signalling function. The functional implications of the association of β7 integrins with the different isoforms of HSP70 warrants further investigation.
Automatic visual inspection for printed circuit board via novel Mask R-CNN in smart city applications

The increasing population in the whole world demands adequate infrastructure to satisfy varied requirements. To fulfill this requirement, the introduction of information techniques presents an opportunity for the development of smart cities. For instance, an automatic visual inspection can be employed to replace the role of workers in quality management and streamlines automation. Previously, a large amount of machine vision-based algorithms has been presented to address this problem. However, accurate detection of various tiny integrated circuits remains an unresolved issue. To bridged this gap, a novel deep learning-based approach was proposed for instance segmentation in printed circuit board images. By adding the geometric attention-guided mask branch into the fully convolutional one-stage object detector under the framework of Mask R-CNN, it can produce a segmentation mask for each bounding box to enhance the identification accuracy. To evaluate the ability of the proposed approach, the comparison experiments were conducted between state-of-the-art techniques and ours. Experimental results demonstrate that the presented algorithm outperformed the state-of-the-art both in precision, sensitivity, and accuracy for both small devices like resistors and capacitors as well as integrated circuits.
Dengue and Zika virus differential infection of human megakaryoblast MEG-01 reveals unique cellular markers

Platelet count is widely used for the diagnosis and follow-up of patients with dengue. Despite its close viral structural and symptomatic homology, ZIKV infection does not typically induce significant thrombocytopenia. To determine the effect of DENV-2 and ZIKV infection on human platelet precursors we utilized MEG-01 cell line to evaluate the viral infection, viability, innate gene expression and release of platelet-like particles (PLPs). DENV-2 induced a higher proportion of cell death at 48-72 h post-infection than ZIKV. The median range of intracellular NS1+/E+ cells was 11.2% (3.3%-25%) and 5% (3%-8.1%) for DENV-2 and ZIKV, respectively (p = 0.03). MEG-01 cells infected with DENV-2 quickly expressed higher levels of IFN-β, indolamine 2,3-dioxygenase and CXCL10 mRNA compared to ZIKV infected cells and DENV-2 but not ZIKV infection reduced the number PLPs from stimulated MEG-01 cells. The results shed light into mechanisms including thrombocytopenia present in patients with DENV but absent in ZIKV infections.
Development of venetoclax performance using its new derivatives on BCL-2 protein inhibition

Cancer cells are resistant to apoptosis and this is one of the most obvious symptoms of cancer in humans. One of the most exciting strategies for treating cancer is to design regulators that increase cell death and stop cell growth. Members of the BCL-2 family of proteins play an important role in the regulation of apoptosis. In this study, an attempt was made to improve the performance of one of the anticancer drugs by designing new analogs of venetoclax (VNT). For this purpose, molecular docking studies were performed to determine the best binding state of VNT and its newly designed derivatives at the protein-binding site to estimate the binding energy. The best analog in terms of free energy was VNT-12 with the lowest energy (-12.15 kcal/mol). Finally, to investigate the inhibitory effect of the compounds on BCL-2 protein, molecular dynamics simulation was used, and by performing the relevant analyses during the simulation, it was observed that the newly designed ligand had better performance in inhibiting BCL-2 protein compared to VNT.
MMICs and Mixed-Signal ICs Based on III/V Technology for Highest Frequencies and Data Rates

During the last years, the Fraunhofer IAF developed a variety of state-of-the-art millimeter-wave monolithic integrated circuits (MMICs) and modules for application in active and passive high-resolution imaging systems operating beyond 200 GHz as well as mixed-signal monolithic ICs intended for use in 100 Gbit/s optical communication systems (Ethernet). The MMICs are based on an advanced metamorphic high electron mobility transistor (mHEMT) technology in combination with grounded coplanar waveguide (GCPW) circuit topology. The mixed-signal ICs are manufactured using a state-of-the-art InP double heterojunction bipolar transistor (DHBT) technology. Examples of high-performance circuits from both technologies are presented in this paper.
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model

Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.
People detection with omnidirectional cameras using a spatial grid of deep learning foveatic classifiers

A novel deep-learning people detection algorithm using omnidirectional cameras is presented, which only requires point-based annotations, unlike most of the prominent works that require bounding box annotations. Thus, the effort of manually annotating the needed training databases is significantly reduced, allowing a faster system deployment. The algorithm is based on a novel deep neural network architecture that implements the concept of Grid of Spatial-Aware Classifiers, but allowing end-to-end training that improves the performance of the whole system. The designed algorithm satisfactorily handles the severe geometric distortions of the omnidirectional images, which typically degrades the performance of state-of-the-art detectors, without requiring any camera calibration. The algorithm has been evaluated in well-known omnidirectional image databases (PIROPO, BOMNI, and MW-18Mar) and compared with several works of the state of the art.(c) 2022 The Author(s). Published by Elsevier Inc.& nbsp;