ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Art-geoscience encounters and entanglements in the watery realm

This paper critically explores a 40-year collaboration between a geomorphologist and a relief printmaker from the perspective of the emerging art-science paradigm in the geosciences. Drawing on the authors' work and practice worldwide, 'standard art-science' (the artist as communicator and observer) and emerging 'transdisciplinary/paradisciplinary' practices are explored in the watery realm. While standard art-science 'encounters' were viewed favourably from the viewpoint of community engagement, especially by commissioning bodies, they did not measurably improve the explanation of science to the public nor offer new avenues for creative investigation. In light of this, the authors undertook a series of explicitly interdisciplinary/transdisciplinary 'entanglements' by co-conceiving projects, carrying out joint fieldwork and 'data' collection and, most importantly, working together in the studio and laboratory. These projects suggest that multi-scalar approaches are required when using art-geoscience to explore environmental issues which impact significantly on individuals and communities.
18β-glycyrrhetinic acid protects neuronal cells from ferroptosis through inhibiting labile iron accumulation and preventing coenzyme Q10 reduction

Ferroptosis is a new form of iron-dependent cell death. A growing body of evidence suggests that abnormal ferroptosis is involved in developing neurodegenerative diseases. 18β-glycyrrhetinic acid (GA) is a major bioactive component of licorice with multiple biological activities including neuroprotection. Give the role of ferroptosis in the neurodegenerative diseases, we hypothesized that the neuroprotective effect of GA might be associated with its ability to protect neuro-cells from ferroptosis. Results demonstrated that GA was able to prevent a well-known ferroptosis inducer ferroptosis inducer 56 (FIN56)-triggered ferroptosis in HT22 mouse neuronal cell. Further mechanistic investigation revealed that the protection of GA on ferroptosis is attributed its inhibiting effect on cellular labile iron accumulation and up-regulating coenzyme Q10 (CoQ10) levels. The findings of the present study uncovered a novel mechanism involved in the neuroprotective effect of GA, and imply that GA could be developed as a novel agent to manage ferroptosis-related diseases.
A Multidisciplinary Aesthetic Treatment Approach for Peg Lateral of the Maxillary Incisors

Developmental anomaly of the maxillary lateral incisors most commonly leads to the occurrence of peg lateral. It is a variant of microdontia where the lateral incisors are smaller than the normal size. This appears as unilaterally or bilaterally. This condition is characterised by the converging of the mesial and distal surfaces forming a cone shape. A variety of treatment options exist for this anomaly including orthodontic treatment, restorative technique and veneer. This case report deals with an individual presenting with peg lateral of the maxillary arch along with midline diastema. The multidisciplinary treatment protocol of orthodontic treatment involving minor tooth movement and space closure in conjunction with a restorative technique for correction was preferred.
Discussion: < Chronology of western Pyrenean Paleolithic cave art: A critical examination > by Blanca Ochoa and Marcos Garcia-Diez

In a recent paper by Ochoa and Garcia-Diez (2013) the available evidences for a chronology of western Pyrenean Paleolithic cave art are critically analyzed and discussed, and an alternative chronological organization is proposed on the basis of stylistic comparison. In this paper we discuss the critics made to the immediate context dating proposals in Altxerri B, Askondo and Etxeberri by giving the detailed information that has been recently published (Garate and Rios-Garaizar, 2012; Garate et al., 2012; Gonzalez-Sainz et al., 2013). We also discuss the validity of the stylistic comparisons proposed by Ochoa and Garcia-Diez (2013) for the Gravettian and the Magdalenian art. Finally we discuss the problems for establishing a reliable chronological framework for Paleolithic rock art in this area. (C) 2014 Elsevier Ltd and INQUA. All rights reserved.
Scalable Packet Classification on FPGA

Multi-field packet classification has evolved from traditional fixed 5-tuple matching to flexible matching with arbitrary combination of numerous packet header fields. For example, the recently proposed OpenFlow switching requires classifying each packet using up to 12-tuple packet header fields. It has become a great challenge to develop scalable solutions for next-generation packet classification that support higher throughput, larger rule sets and more packet header fields. This paper exploits the abundant parallelism and other desirable features provided by current field-programmable gate arrays (FPGAs), and proposes a decision-tree-based, 2-D multi-pipeline architecture for next-generation packet classification. We revisit the techniques for traditional 5-tuple packet classification and propose several optimization techniques for the state-of-the-art decision-tree-based algorithm. Given a set of 12-tuple rules, we develop a framework to partition the rule set into multiple subsets each of which is built into an optimized decision tree. A tree-to-pipeline mapping scheme is carefully designed to maximize the memory utilization while sustaining high throughput. The implementation results show that our architecture can store either 10K real-life 5-tuple rules or 1K synthetic 12-tuple rules in on-chip memory of a single state-of-the-art FPGA, and sustain 80 and 40 Gbps throughput for minimum size (40 bytes) packets, respectively.
A two-pole 900/1800-MHz microstrip filter for dual-band applications

A two-pole 900/1800-MH,7 dual-band bandpass filter using microstrip open-loop resonators is proposed. Dual-band characteristics art obtained by using the fundamental and first spurious resonance of the resonators. Both the internal and external couplings of the filter are appropriately designed to simultaneously satisfy the dual-band requirements. The measured filtering characteristics of the designed filter agree well with the simulated responses. (c) 2006 Wiley Periodicals, Inc.
OCT-1 Expression in Patients with Chronic Myeloid Leukemia: A Comparative Analysis with Respect to Response to Imatinib Treatment

The introduction of tyrosine kinase inhibitors (TKI) has resulted in a significant improvement in the treatment of CML patients. However, some CML patients are resistant to imatinib therapy, the initial TKI therapy in the CML. Therefore, it is important to find prognostic markers for resistance. The OCT-1 gene involved in imatinib uptake is also suspected to cause imatinib resistance. The aim of this study was to investigate the role of OCT-1 in imatinib resistance by comparing OCT-1 expression levels in imatinib resistant and imatinib sensitive patients with chronic myeloid leukemia (CML). This study was conducted on 101 patients with CML [imatinib sensitive (n = 51) and imatinib resistant (n = 50)] who were treated with imatinib. Gene expression analysis was done using QRT-PCR. The relative expression levels of OCT-1 were calculated using 2(-ΔΔCT) method. OCT1 mRNA expression levels were 0.149 (0.011-2.532) and 0.119 (0.008-2.868) in imatinib-sensitive group and imatinib-resistant group, respectively. OCT-1 expression levels were not significantly different in the imatinib-sensitive group when compared to imatinib resistant group (p > 0.05). OCT-1 expression was also similar in BCR-ABL1 kinase domain mutation positive and negative cases (p > 0.05). The imatinib-resistant group had a higher rate of hydroxyurea or interferon-alpha treatment prior to imatinib therapy and a lower rate for first-line imatinib as the only treatment than the imatinib-sensitive group (p = 0.002 and p = 0.002, respectively). According to the results of our study, OCT-1 does not have a biomarker feature in the evaluation of imatinib response. In addition, the study should be performed in larger patient groups.
Relative sensitivity value (RSV): A metric for measuring input parameter influence in life cycle assessment modeling

Life cycle assessment (LCA) is a commonly used tool to quantify life cycle environmental footprints of products. Uncertainty in LCA modeling, particularly from uncertainty in production practices (represented through input parameter arguments), can lead to incorrect conclusions and hamper decision-making. Characterization of uncertainty through stochastic means and sensitivity analysis is utilized in a small fraction of LCA case studies, and the majority of studies default to scenario analysis due to its lower barrier to implementation and its results are easier to interpret. In this article, we introduce a sensitivity metric, relative sensitivity value (RSV), which allows LCA practitioners to gauge the relative influence of production practices on life cycle impacts in multiple phases and impact categories. Relative sensitivity value bridges the gap between scenario analysis and global sensitivity analysis, and it allows an LCA practitioner to provide an easy-to-interpret metric for quantifying the degree to which incremental changes in production practices influences the life cycle environmental footprint. We present the methodology used to calculate RSV and provide programming code, which can be readily used by an LCA practitioner to calculate RSV for their LCA model. We demonstrate the usage of RSV through a livestock husbandry LCA case study, in which we show how RSV results may be presented and interpreted, and how conclusions regarding production practices may be drawn. Integr Environ Assess Manag 2023;19:547-555. © 2022 SETAC.
Generative Consistency for Semi-Supervised Cerebrovascular Segmentation From TOF-MRA

Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is a critical step in computer-aided diagnosis. In recent years, deep learning models have proved its powerful feature extraction for cerebrovascular segmentation. However, they require many labeled datasets to implement effective driving, which are expensive and professional. In this paper, we propose a generative consistency for semi-supervised (GCS) model. Considering the rich information contained in the feature map, the GCS model utilizes the generation results to constrain the segmentation model. The generated data comes from labeled data, unlabeled data, and unlabeled data after perturbation, respectively. The GCS model also calculates the consistency of the perturbed data to improve the feature mining ability. Subsequently, we propose a new model as the backbone of the GSC model. It transfers TOF-MRA into graph space and establishes correlation using Transformer. We demonstrated the effectiveness of the proposed model on TOF-MRA representations, and tested the GCS model with state-of-the-art semi-supervised methods using the proposed model as backbone. The experiments prove the important role of the GCS model in cerebrovascular segmentation. Code is available at https://github.com/MontaEllis/SSL-For-Medical-Segmentation.
The role of the cave in the expression of prehistoric societies

One of the major characteristics of prehistoric arts is that they belong to a very specific spatial context, be it open air, rocks, shelters or caves. The presence of these images in these particular places is a mark of their identity and of the heritage left by these ancient societies and their beliefs, ever since the first cultural manifestations of the Upper Palaeolithic in Europe. The specific choice of a wall or of a particular background, of a location in the cave can thus be just as significant as the image that one chooses to represent or the way in which they choose to represent it. This is why the present research intends to study the links between the images and their supports, through a family which has already shown a particular affinity with the space: the family of signs. The example of the signs highlights the fundamental role of the supports in the construction of images and the important and sometimes radical influence of the cave on their graphic identity. They illustrate thus "ways of expression" of the prehistoric men, expression of their developed and complex reasoning. (C) 2015 Elsevier Ltd and INQUA. All rights reserved.
Filter Pruning and Re-Initialization via Latent Space Clustering

Filter pruning is prevalent for pruning-based model compression. Most filter pruning methods have two main issues: 1) the pruned network capability depends on that of source pretrained models, and 2) they do not consider that filter weights follow a normal distribution. To address these issues, we propose a new pruning method employing both weight re-initialization and latent space clustering. For latent space clustering, we define filters and their feature maps as vertices and edges to be a graph, transformed into a latent space by graph convolution, alleviating to prune zero-near weight filters only. In addition, a part of filters is re-initialized with a constraint for enhancing filter diversity, and thus the pruned model is less dependent on the source network. This approach provides more robust accuracy even when pruned from the pretrained model with low accuracy. Extensive experimental results show our method decreases 56.6% and 84.6% of FLOPs and parameters of VGG16 with negligible loss of accuracy on CIFAR100, which is the state-of-the art performance. Furthermore, our method presents outperforming or comparable pruning results against state-of-the-art models on multiple datasets.
LTC: A Fast Algorithm to Accurately Find Significant Items in Data Streams

Finding top-k frequent items has been a hot issue in databases. Finding top-k persistent items is a new issue, and has attracted increasing attention in recent years. In practice, users often want to know which items are significant, i.e., not only frequent but also persistent. No prior art can address both of the above two issues at the same time. Also, for high-speed data streams, prior art cannot achieve high accuracy when the memory is tight. In this paper, we define a new issue, named finding significant items, and propose a novel algorithm namely LTC to address this issue. It includes two key techniques, Long-tail Restoring and CLOCK, as well as three optimizations. In addition, LTC is extended to support finding significant items with thresholds. We theoretically derive the correct rate and error bound, and conduct extensive experiments on three real datasets to test the performance of LTC. Our experimental results show that LTC can achieve 10(5 )times higher accuracy in terms of average relative error than other related algorithms. Lastly, LTC is applied to a DDoS detection task and it shows that finding significant items is more powerful than finding frequent items.
Low-dose, large-angled cone-beam helical CT data reconstruction using algebraic reconstruction techniques

We report on results on the use of two variants of the algebraic reconstruction techniques (ART) for reconstructing from helical cone-beam computerized tomography (CT) data: a standard one that considers a single ray in an iterative step and a block version which treats simultaneously several cone-beam projections when calculating an iterative step. Both algorithms were implemented using the modified Kaiser-Bessel window functions, also known as blobs, placed on the body-centered cubic (bcc) grid. The algorithms were used to reconstruct phantoms from data collected for the PI-geometry for four different maximum cone-beam angles (2.39, 7.13, 9.46 and 18.43 degrees). Both scattering and quantum noise (for three different noise levels) were introduced to create noisy projections that simulate low-dose examinations. The results presented here (for both noiseless and noisy data sets) point to the facts that, as opposed to a filtered back-projection algorithm, the quality of the reconstructions produced by the ART methods does not suffer from the increase in the cone-beam angle and it is more robust in the presence of noise. (c) 2006 Elsevier B.V. All rights reserved.
Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved.
Dynamic processing of hunger and thirst by common mesolimbic neural ensembles

The nucleus accumbens (NAc) is a canonical reward center that regulates feeding and drinking but it is not known whether these behaviors are mediated by same or different neurons. We employed two-photon calcium imaging in awake, behaving mice and found that during the appetitive phase, both hunger and thirst are sensed by a nearly identical population of individual D1 and D2 neurons in the NAc that respond monophasically to food cues in fasted animals and water cues in dehydrated animals. During the consummatory phase, we identified three distinct neuronal clusters that are temporally correlated with action initiation, consumption, and cessation shared by feeding and drinking. These dynamic clusters also show a nearly complete overlap of individual D1 neurons and extensive overlap among D2 neurons. Modulating D1 and D2 neural activities revealed analogous effects on feeding versus drinking behaviors. In aggregate, these data show that a highly overlapping set of D1 and D2 neurons in NAc detect food and water reward and elicit concordant responses to hunger and thirst. These studies establish a general role of this mesolimbic pathway in mediating instinctive behaviors by controlling motivation-associated variables rather than conferring behavioral specificity.
Nanopore Sequencing Using the Full-Length 16S rRNA Gene for Detection of Blood-Borne Bacteria in Dogs Reveals a Novel Species of Hemotropic Mycoplasma

Dogs across the globe are afflicted by diverse blood- and vector-borne bacteria (VBB), many of which cause severe disease and can be fatal. Diagnosis of VBB infections can be challenging due to the low concentration of bacteria in the blood, the frequent occurrence of coinfections, and the wide range of known, emerging, and potentially novel VBB species encounterable. Therefore, there is a need for diagnostics that address these challenges by being both sensitive and capable of detecting all VBB simultaneously. We detail the first employment of a nanopore-based sequencing methodology conducted on the Oxford Nanopore Technologies (ONT) MinION device to accurately elucidate the "hemobacteriome" from canine blood through sequencing of the full-length 16S rRNA gene. We detected a diverse range of important canine VBB, including Ehrlichia canis, Anaplasma platys, Mycoplasma haemocanis, Bartonella clarridgeiae, "Candidatus Mycoplasma haematoparvum", a novel species of hemotropic mycoplasma, and Wolbachia endosymbionts of filarial worms, indicative of filariasis. Our nanopore-based protocol was equivalent in sensitivity to both quantitative PCR (qPCR) and Illumina sequencing when benchmarked against these methods, achieving high agreement as defined by the kappa statistics (k > 0.81) for three key VBB. Utilizing the ability of the ONT' MinION device to sequence long read lengths provides an excellent alternative diagnostic method by which the hemobacteriome can be accurately characterized to the species level in a way previously unachievable using short reads. We envision our method to be translatable to multiple contexts, such as the detection of VBB in other vertebrate hosts, including humans, while the small size of the MinION device is highly amenable to field use. IMPORTANCE Blood- and vector-borne bacteria (VBB) can cause severe pathology and even be lethal for dogs in many regions across the globe. Accurate characterization of all the bacterial pathogens infecting a canine host is critical, as coinfections are common and emerging and novel pathogens that may go undetected by traditional diagnostics frequently arise. Deep sequencing using devices from Oxford Nanopore Technologies (ONT) provides a solution, as the long read lengths achievable provide species-level taxonomic identification of pathogens that previous short-read technologies could not accomplish. We developed a protocol using ONT' MinION sequencer to accurately detect and classify a wide spectrum of VBB from canine blood at a sensitivity comparable to that of regularly used diagnostics, such as qPCR. This protocol demonstrates great potential for use in biosurveillance and biosecurity operations for the detection of VBB in a range of vertebrate hosts, while the MinION sequencer's portability allows this method to be used easily in the field.
Z-scheme promoted heterojunction photocatalyst (Ag@AgVO3 /rGO/CeVO4) with improved interfacial charge transfer for efficient removal of aqueous organics irradiated under LED light

A facile hydrothermal route was followed to obtain a ternary composite Ag@AgVO3/rGO/CeVO4 with in-situ deposition of Ag nanoparticles over the AgVO3 nano-belts. The in-situ deposition was promoted and enhanced with the introduction of GO. The as-synthesized composite demonstrated remarkable visible light harvesting efficiency greater than 75% in the visible region. The charge separation and light harvesting properties were achieved through the Z-scheme mechanism mediated through rGO and the electron trapping/Schottky barrier effect from Ag nanoparticles. The reduction in the width of space charge region (∼2.5 times) and simultaneous increase in the density of charge carriers (2.3∗1018) promoted the LED irradiated photocatalytic performance. The decay time of the charge carriers were prolonged in the order of 4.46 s implying the enhancement in the charge separation. The studies were extended to charge trapping and the band structure modelling. The later emphasized on the prominence of Z-scheme mechanism with hole mediated degradation pathway. The LED photocatalysis demonstrated a removal efficiency of 87.20% for MB and 55.51% for phenol with a average AQE of 29.28% (MB) and 13.90% (phenol) for the ternary. The mineralization efficiency determined through TOC analysis was found to be 71.72%, and 66.43% for MB and phenol system respectively.