ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Functional characterization of secologanin synthase-like homologous genes suggests their involvement in the biosynthesis of diverse metabolites in the secoiridoid biosynthetic pathway of Camptotheca acuminata Decne

The assignment of functions based on homology has recently been challenged by the frequent discovery of functional divergence among homologous gene family members of enzymes involved in plant secondary metabolism. Secologanin synthase (SLS) is the key CYP450 enzyme that acts critically in the biosynthesis of Strychnos alkaloid scaffold. In this study, to fully elucidate the mechanism that underlies metabolic variation, the CYP450 paralogs that participate in oxidative transformation of the secoiridoid pathway were functionally characterized by combining multitiered strategies of metabolite profiling, phylogenetic analyses, biochemistry assays and reverse genetics techniques. Five CaSLSs-like homologous genes were mined and isolated from an integrative multi-omics database of Camptotheca acuminata. Protein sequences, structural comparisons, and phylogenetic analyses confirmed that CaSLS1-2 and CaSLS4-5 were grouped into the SLS clade, and only CaSLS3 clustered into the 7DLH clade. Five homologs, including two previously identified enzymatic genes, were thus designated as CaSLAS1, CaSLAS2, Ca7DLH, CaSLS4 and CaSLS5. Enzymatic assays of the recombinant proteins in yeast showed that CaSLAS1 and CaSLAS2 displayed multi-catalytic activities of SLS, secologanic acid synthase (SLAS) and secoxyloganin synthase (SXS). Additionally, the reactions of CaSLASs enzymes generated stereospecific isomers of secoiridoid products, and a new product of secoxyloganin was observed. CaSLS5, a third SLS enzyme isoform that catalyzes the formation of secologanin, was reported for the first time. However, CaSXS enzymatic activities in vitro had little physiological impact on the biosynthesis of camptothecin (CPT) in Camptotheca acuminata. The primary and secondary roles of CaSLSs-like genes in secoiridoid metabolism were confirmed by virus-induced gene silencing (VIGS) in plant leaves. Efficient silencing and transcriptional downregulation of CaSLAS2, compared with the CaSLAS1 homologs, resulted in a greater reduction of the accumulation of CPT within silenced plants, and CaSLS5 had barely any effect on the contents of metabolites in planta. Thus, CaSLAS2, rather than CaSLAS1, appeared to function as a major participant in the biosynthesis of CPT, and there were redundant functions in the CaSLSs-like enzymes. Consistent with such roles, CaSLAS2 was ubiquitously expressed at very high levels in Camptotheca tissues, and CaSLAS2 was specifically expressed in young leaves. In contrast, CaSLS5 was poorly expressed in every tissue tested. Our findings demonstrate that homologs that belong to the CYP72 gene family are functionally diverse and exhibit divergence and thereby uncover an expanding group of enzymatic genes that determine the chemo-diversity of the iridoid pathway.
Faber: A Hardware/SoftWare Toolchain for Image Registration

Image registration is a well-defined computation paradigm widely applied to align one or more images to a target image. This paradigm, which builds upon three main components, is particularly compute-intensive and represents many image processing pipelines' bottlenecks. State-of-the-art solutions leverage hardware acceleration to speed up image registration, but they are usually limited to implementing a single component. We present Faber, an open-source HW/SW CAD toolchain tailored to image registration. The Faber toolchain comprises HW/SW highly-tunable registration components, supports users with different expertise in building custom pipelines, and automates the design process. In this direction, Faber provides both default settings for entry-level users and latency and resource models to guide HW experts in customizing the different components. Finally, Faber achieves from 1.5x to 54x in speedup and from 2x to 177x in energy efficiency against state-of-the-art tools on a Xeon Gold.
Chx10+V2a interneurons in spinal motor regulation and spinal cord injury

Chx10-expressing V2a (Chx10+V2a) spinal interneurons play a large role in the excitatory drive of motoneurons. Chemogenetic ablation studies have demonstrated the essential nature of Chx10+V2a interneurons in the regulation of locomotor initiation, maintenance, alternation, speed, and rhythmicity. The role of Chx10+V2a interneurons in locomotion and autonomic nervous system regulation is thought to be robust, but their precise role in spinal motor regulation and spinal cord injury have not been fully explored. The present paper reviews the origin, characteristics, and functional roles of Chx10+V2a interneurons with an emphasis on their involvement in the pathogenesis of spinal cord injury. The diverse functional properties of these cells have only been substantiated by and are due in large part to their integration in a variety of diverse spinal circuits. Chx10+V2a interneurons play an integral role in conferring locomotion, which integrates various corticospinal, mechanosensory, and interneuron pathways. Moreover, accumulating evidence suggests that Chx10+V2a interneurons also play an important role in rhythmic patterning maintenance, left-right alternation of central pattern generation, and locomotor pattern generation in higher order mammals, likely conferring complex locomotion. Consequently, the latest research has focused on postinjury transplantation and noninvasive stimulation of Chx10+V2a interneurons as a therapeutic strategy, particularly in spinal cord injury. Finally, we review the latest preclinical study advances in laboratory derivation and stimulation/transplantation of these cells as a strategy for the treatment of spinal cord injury. The evidence supports that the Chx10+V2a interneurons act as a new therapeutic target for spinal cord injury. Future optimization strategies should focus on the viability, maturity, and functional integration of Chx10+V2a interneurons transplanted in spinal cord injury foci.
Improved sufficient condition of l(1-2)-minimisation for robust signal recovery

By means of the restricted isometry property of order k and the restricted orthogonality property of order (k,k), this Letter mainly establishes an improved sufficient condition for a recently emerged $\ell _{1-2}$l1-2-minimisation to guarantee the robust signal recovery. The obtained condition is proved to be much better than the state-of-the-art ones for almost all parameters k.
Noise Limits of CMOS Current Interfaces for Biosensors: A Review

Current sensing readout is one of the most frequent techniques used in biosensing due to the charge-transfer phenomena occurring at solid-liquid interfaces. The development of novel nanodevices for biosensing determines new challenges for electronic interface design based on current sensing, especially when compact and efficient arrays need to be organized, such as in recent trends of rapid label-free electronic detection of DNA synthesis. This paper will review the basic noise limitations of current sensing interfaces with particular emphasis on integrated CMOS technology. Starting from the basic theory, the paper presents, investigates and compares charge-sensitive amplifier architectures used in both continuous-time and discrete-time approaches, along with their design trade-offs involving noise floor, sensitivity to stray capacitance and bandwidth. The ultimate goal of this review is providing analog designers with helpful design rules and analytical tools. Also, in order to present a comprehensive overview of the state-of-the-art, the most relevant papers recently appeared in the literature about this topic are discussed and compared.
A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.
A light-weight stereo matching network based on multi-scale features fusion and robust disparity refinement

In recent years, convolutional-neural-network based stereo matching methods have achieved significant gains compared to conventional methods in terms of both speed and accuracy. Current state-of-the-art disparity estimation algorithms require many parameters and large amounts of computational resources and are not suited for applications on edge devices. In this paper, an end-to-end light-weight network (LWNet) for fast stereo matching is proposed, which consists of an efficient backbone with multi-scale feature fusion for feature extraction, a 3D U-Net aggregation architecture for disparity computation, and color guidance in a 2D convolutional neural network (CNN) for disparity refinement. MobileNetV2 is adopted as an efficient backbone in feature extraction. The channel attention module is applied to improve the representational capacity of features and multi-resolution information is adaptively incorporated into the cost volume via cross-scale connections. Further, a left-right consistency check and color guidance refinement are introduced and a robust disparity refinement network is designed with skip connections and dilated convolutions to capture global context information and improve disparity estimation accuracy with little computational cost and memory space. Extensive experiments on Scene Flow, KITTI 2015, and KITTI 2012 demonstrate that the proposed LWNet achieves competitive accuracy and speed when compared with state-of-the-art stereo matching methods.
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation

Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and time-consuming task, which also suffers from high inter- and intra- observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-to-end framework that integrates: a) entropy minimization, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods.
Development and application of a multiplex qPCR assay for the detection of duck circovirus, duck Tembusu virus, Muscovy duck reovirus, and new duck reovirus

A multiplex qPCR assay was developed to simultaneously detect duck circovirus (DuCV), duck Tembusu virus (DTMUV), Muscovy duck reovirus (MDRV), and novel duck reovirus (NDRV), but it did not amplify other viruses, including duck virus enteritis (DVE), infectious bursal disease virus (IBDV), avian reovirus (ARV), H5 avian influenza virus (H5 AIV), H7 avian influenza virus (H7 AIV), H9 avian influenza virus (H9 AIV), Newcastle disease virus (NDV), and Muscovy duck parvovirus (MDPV), and the detection limit for DuCV, DTMUV, MDRV, and NDRV was 1.51 × 101 copies/μL. The intra- and interassay coefficients of variation were less than 1.54% in the repeatability test with standard plasmid concentrations of 1.51 × 107, 1.51 × 105, and 1.51 × 103 copies/μL. The developed multiple qPCR assay was used to examine 404 clinical samples to verify its practicability. The positivity rates for DuCV, DTMUV, MDRV, and NDRV were 26.0%, 9.9%, 4.0%, and 4.7%, respectively, and the mixed infection rates for DuCV + DTMUV, DuCV + MDRV, DuCV + NDRV, MDRV + NDRV, DTMUV + MDRV, and DTMUV + NDRV were 2.7%, 1.2%, 1.2%, 1.0%, 0.5%, and 0.7%, respectively.
Climate change and higher education: Assessing factors that affect curriculum requirements

Pervasive misinformation about climate change might be reduced if colleges were to include the topic within general education curriculum. This paper analyzes the general education (or "core") curriculum in the top 100 universities and liberal-arts colleges in the U.S. to assess the proportion of core courses that highlight climate change or climate science. The probability that a student takes at least one climate change course via the core curriculum is estimated at 0.17 across all schools. The probability is higher at research universities than at liberal arts colleges, in core programs that have more science and social science courses, and at public universities in states with a Democrat-controlled legislature than in states with a Republican-controlled or split legislature. Drawing on cases of best practices in the U.S. identified from the data set, the authors discuss strategies that could ensure a higher likelihood that the core curriculum includes education on climate science and climate change. The study advances the broader research literature on sustainability in higher education programs by bringing it into conversation with research on the college core curriculum and by focusing both on the specific issue of climate-change education. (C) 2017 Elsevier Ltd. All rights reserved.
HEVC-based lossless intra coding for efficient still image compression

Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is the compression of high resolution video, its performance in still image compression is at par with state-of-the-art still image compression standards. This work explores the possibility of incorporating the efficient intra prediction techniques employed in HEVC into the compression of high resolution still images. In the lossless coding mode of HEVC, sample- based angular intra prediction (SAP) methods have shown better prediction accuracy compared to the conventional block-based prediction (BP). In this paper, we propose an improved sample-based angular intra prediction (ISAP), which enhances the accuracy of the highly crucial intra prediction within HEVC. The experimental results show that ISAP in lossless compression of still images outclasses archival tools, state-of-the-art image compression standards and other HEVC-based lossless image compression codecs.
Analytical strategies in neurotransmitter measurements: A mini literature review

Neurotransmitters (NTs) are endogenous, polar, low-molecular-weight compounds that play multiple pivotal roles in the central nervous system. NTs are involved in communicating information, responding to stress, regulating motor coordination, and allowing interneuronal communication in living organisms. It is essential to determine the distribution of NTs in brain regions to better understand drug dependence and abuse, neurological disorders, psychological disorders, and aging. Monitoring NT levels is also important in diagnosing and avoiding serious illnesses. We here review chromatography-based analytical techniques, including pretreatment methods (e.g., microdialysis and solid-phase microextraction), as well as detection strategies (e.g., MS and electrochemistry), focusing on developments in these techniques over the past 5 years. We then highlight recent advances in electrochemical and fluorescence imaging methods in vivo and the disadvantages and advantages of such technologies, including high spatiotemporal resolution, polymer specificity, and high sensitivity. Finally, we summarize and compare the complementary advantages of chromatography-based analytical techniques and biosensors and discuss trends in the development of NT detection technologies.
Palaeolithic ceramic technology: The artistic origins and impacts of a technological innovation

This paper analyses the assemblages of Upper Palaeolithic ceramic figurines and figurine fragments from Czech Republic ("Pavlovian") and Croatia, which are some of the first iterations of this material and technological innovation in Europe. Using chaine operatoire methodology, this paper compares both the technologies and gestures involved in the manufacture of these artefacts as well as the impact of these new materials on art and society in each context. These analyses reveal how the introduction of this innovative material and the associated technologies used to make ceramic art proved to be an important catalyst for more experimentation and play in the production of art, which led to innovations in artistic expression. Furthermore, this research highlights the need to study Palaeolithic ceramic artefacts using quantitative and nuanced analytical methodologies that move beyond the traditional focus on the most iconographically-striking Palaeolithic art. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.
Folding Features and Dynamics of 3D Genome Architecture in Plant Fungal Pathogens

The folding and dynamics of three-dimensional (3D) genome organization are fundamental for eukaryotes executing genome functions but have been largely unexplored in nonmodel fungi. Using high-throughput sequencing coupled with chromosome conformation capture (Hi-C) data, we generated two chromosome-level assemblies for Puccinia striiformis f. sp. tritici, a fungus causing stripe rust disease on wheat, for studying 3D genome architectures of plant pathogenic fungi. The chromatin organization of the fungus followed a combination of the fractal globule model and the equilibrium globule model. Surprisingly, chromosome compartmentalization was not detected. Dynamics of 3D genome organization during two developmental stages of P. striiformis f. sp. tritici indicated that regulation of gene activities might be independent of the changes of genome organization. In addition, chromatin conformation conservation was found to be independent of genome sequence synteny conservation among different fungi. These results highlighted the distinct folding principles of fungal 3D genomes. Our findings should be an important step toward a holistic understanding of the principles and functions of genome architecture across different eukaryotic kingdoms. IMPORTANCE Previously, our understanding of 3D genome architecture has mainly come from model mammals, insects, and plants. However, the organization and regulatory functions of 3D genomes in fungi are largely unknown. In this study, we comprehensively investigated P. striiformis f. sp. tritici, a plant fungal pathogen, and revealed distinct features of the 3D genome, comparing it with the universal folding feature of 3D genomes in higher eukaryotic organisms. We further suggested that there might be distinct regulatory mechanisms of gene expression that are independent of chromatin organization changes during the developmental stages of this rust fungus. Moreover, we showed that the evolutionary pattern of 3D genomes in this fungus is also different from the cases in mammalian genomes. In addition, the genome assembly pipeline and the generated two chromosome-level genomes will be valuable resources. These results highlighted the unexplored distinct features of 3D genome organization in fungi. Therefore, our study provided complementary knowledge to holistically understand the organization and functions of 3D genomes across different eukaryotes.
'NEW URBANITY' AND CONTEMPORARY FORMS OF PUBLIC ART NOTES ON CITIZEN FIREFIGHTER (K. HUNTER)

In the course of global economic restructuring, 'new urbanity' has become a key concept within German-speaking urban studies. Despite a certain ambiguity, the concept often conveys a positive image of the traditional European city and its supposed urban qualities. The present paper aims to challenge this image by drawing upon contemporary forms of public art. Taking Kenny Hunters sculpture Citizen Firefighter as a case study, alternative concepts Of urbanity are explored. Using qualitative interviews with the artist, the relationship between artistic and academic imaginations of urban life will be discussed. In this way a more balanced image of the traditional European city can be achieved.