ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Visual knowledge guided intelligent generation of Chinese seal carving

We digitally reproduce the process of resource collaboration, design creation, and visual presentation of Chinese seal-carving art. We develop an intelligent seal-carving art-generation system (Zhejiang University Intelligent Seal-Carving System, http://www.next.zju.edu.cn/seal/; the website of the seal-carving search and layout system is http://www.next.zju.edu.cn/seal/search_app/) to deal with the difficulty in using a visual knowledge guided computational art approach. The knowledge base in this study is the Qiushi Seal-Carving Database, which consists of open datasets of images of seal characters and seal stamps. We propose a seal character generation method based on visual knowledge, guided by the database and expertise. Furthermore, to create the layout of the seal, we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure. Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving. Our work provides theoretical and applied references for the rebirth and innovation of seal-carving art.
Asymmetric role of non-renewable energy consumption, ICT, and financial development on ecological footprints: evidence from QARDL approach

This study examines long-term connection and short-term dynamics concerning ecological footprint and six independent variables, named fossil fuel consumption, energy consumption, financial depth, trade, GDP, and ICT for Pakistan's duration from 1960 to 2019. The "QARDL-quantile autoregressive distributed lag" technique is used for time series and panel estimation. The QARDL model exhibits the connection between variables over the quantiles range, reflecting varying stages of Pakistan's ecological footprint. The results exhibit noticeable quantile-varying co-integration connection among ecological footprint and six independent variables. The results accentuate the significant influence of energy consumption, strong financial position, economic growth, and ICT technologies on ecological well-being, which assists in understanding short and long-term impact on the environment in Pakistan.
Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.
Speech Enhancement With Inventory Style Speech Resynthesis

We present a new method for the enhancement of speech. The method is designed for scenarios in which targeted speaker enrollment as well as system training within the typical noise environment are feasible. The proposed procedure is fundamentally different from most conventional and state-of-the-art denoising approaches. Instead of filtering a distorted signal we are resynthesizing a new "clean" signal based on its likely characteristics. These characteristics are estimated from the distorted signal. A successful implementation of the proposed method is presented. Experiments were performed in a scenario with roughly one hour of clean speech training data. Our results show that the proposed method compares very favorably to other state-of-the-art systems in both objective and subjective speech quality assessments. Potential applications for the proposed method include jet cockpit communication systems and offline methods for the restoration of audio recordings.
Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions

Infrastructure-as-a-Service (IaaS) cloud computing offers customers (tenants) a scalable and economical way to provision virtual machines (VMs) on demand while charging them only for the leased computing resources by time. However, due to the VM contention on shared computing resources in datacenters, this new computing paradigm inevitably brings noticeable performance overhead (i.e., unpredictable performance) of VMs to tenants, which has become one of the primary issues of the IaaS cloud. Consequently, increasing efforts have recently been devoted to guaranteeing VM performance for tenants. In this survey, we review the state-of-the-art research on managing the performance overhead of VMs, and summarize them under diverse scenarios of the IaaS cloud, ranging from the single-server virtualization, a single mega datacenter, to multiple geodistributed datacenters. Specifically, we unveil the causes of VM performance overhead by illustrating representative scenarios, discuss the performance modeling methods with a particular focus on their accuracy and cost, and compare the overhead mitigation techniques by identifying their effectiveness and implementation complexity. With the obtained insights into the pros and cons of each existing solution, we further bring forth future research challenges pertinent to the modeling methods and mitigation techniques of VM performance overhead in the IaaS cloud.
MGARD plus : Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction

Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale, but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which treats the multilevel decomposition as a preconditioner and terminates the decomposition process at an appropriate level. 3) We leverage a set of algorithmic optimization strategies to significantly improve the performance of multilevel decomposition/recomposition. 4) We evaluate our proposed method using four real-world scientific datasets and compare with several state-of-the-art lossy compressors. Experiments demonstrate that our optimizations improve the decomposition/recomposition performance of the existing multilevel method by up to 70x, and the proposed compression method can improve compression ratio by up to 2x compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion.
A comparative evaluation of outlier detection algorithms: Experiments and analyses

We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms' characteristics. (C) 2017 Elsevier Ltd. All rights reserved.
High-resolution Melting Analysis for NOTCH1 c.7541-7542delCT Mutation in Chronic Lymphocytic Leukemia: Prognostic Significance in Egyptian Patients

The present study aimed to detect the prevalence of NOTCH1 c.7541-7542delCT mutation in Egyptian CLL patients using HRM assay and to assess its relation to patients' survival. The study included 50 newly diagnosed treatment-naïve CLL patients and 50 age and sex matched healthy controls. NOTCH1 c.7541-7542delCT mutation was detected using High-resolution melting (HRM) assay and direct Sanger sequencing. Outcome parameters included progression free survival (PFS) and overall survival (OS). NOTCH1 c.7541-7542delCT mutation was detected in 5 (10.0%) of CLL patients. No controls had NOTCH1 c.7541-7542delCT mutation. Similar results were obtained by direct Sanger sequencing yielding a sensitivity and specificity of 100.0% for HRM in detection of NOTCH1 c.7541-7542delCT mutation in the studied patients. In univariate analysis, predictors of OS included Trisomy 12, high LDH, presence of NOTCH1 c.7541-7542delCT mutation and lack of CR. In multivariate analysis, only lack of CR was found as a significant predictor of OS. HRM analysis is a sensitive method for detection of NOTCH1 c.7541-7542delCT mutation in CLL patients. This mutation may be linked to poor disease prognosis.
State of Art IoT and Edge Embedded Systems for Real-Time Machine Vision Applications

IoT and edge devices dedicated to run machine vision algorithms are usually few years lagging currently available state-of-the-art technologies for hardware accelerators. This is mainly due to the non-negligible time delay required to implement and assess related algorithms. Among possible hardware platforms which are potentially being explored to handle real-time machine vision tasks, multi-core CPU and Graphical Processing Unit (GPU) platforms remain the most widely used ones over Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC)-based platforms. This is mainly due to the availability of powerful and user friendly software development tools, in addition to their lower cost, and obviously their high computation power with reasonable form factor and power consumption. Nevertheless, the trend now is towards a System-On-Chip (SOC) processors which combine ASIC/FPGA accelerators with GPU/multicore CPUs. This paper presents different state of the art IoT and edge machine vision technologies along with their performance and limitations. It can be a good reference for researchers involved in designing state of the art IoT embedded systems for machine vision applications.
N-Hydroxy pipecolic acid methyl ester is involved in Arabidopsis immunity

The biosynthesis of N-hydroxy pipecolic acid (NHP) has been intensively studied, though knowledge on its metabolic turnover is still scarce. To close this gap, we discovered three novel metabolites via metabolite fingerprinting in Arabidopsis thaliana leaves after Pseudomonas infection and UV-C treatment. Exact mass information and fragmentation by tandem mass spectrometry (MS/MS) suggest a methylated derivative of NHP (MeNHP), an NHP-OGlc-hexosyl conjugate (NHP-OGlc-Hex), and an additional NHP-OGlc-derivative. All three compounds were formed in wild-type leaves but were not present in the NHP-deficient mutant fmo1-1. The identification of these novel NHP-based molecules was possible by a dual-infiltration experiment using a mixture of authentic NHP and D9-NHP standards for leaf infiltration followed by UV-C treatment. Interestingly, the signal intensity of MeNHP and other NHP-derived metabolites increased in ugt76b1-1 mutant plants. For MeNHP, we unequivocally determined the site of methylation at the carboxylic acid moiety. MeNHP application by leaf infiltration leads to the detection of a MeNHP-OGlc as well as NHP, suggesting MeNHP hydrolysis to NHP. This is in line with the observation that MeNHP infiltration is able to rescue the fmo1-1 susceptible phenotype against Hyaloperonospora arabidopsidis Noco 2. Together, these data suggest MeNHP as an additional storage or transport form of NHP.
A new group of eubacterial light-driven retinal-binding proton pumps with an unusual cytoplasmic proton donor

One of the main functions of microbial rhodopsins is outward-directed light-driven proton transport across the plasma membrane, which can provide sources of energy alternative to respiration and chlorophyll photosynthesis. Proton-pumping rhodopsins are found in Archaea (Halobacteria), multiple groups of Bacteria, numerous fungi, and some microscopic algae. An overwhelming majority of these proton pumps share the common transport mechanism, in which a proton from the retinal Schiff base is first transferred to the primary proton acceptor (normally an Asp) on the extracellular side of retinal. Next, reprotonation of the Schiff base from the cytoplasmic side is mediated by a carboxylic proton donor (Asp or Glu), which is located on helix C and is usually hydrogen-bonded to Thr or Ser on helix B. The only notable exception from this trend was recently found in Exiguobacterium, where the carboxylic proton donor is replaced by Lys. Here we describe a new group of efficient proteobacterial retinal-binding light-driven proton pumps which lack the carboxylic proton donor on helix C (most often replaced by Gly) but possess a unique His residue on helix B. We characterize the group spectroscopically and propose that this histidine forms a proton-donating complex compensating for the loss of the carboxylic proton donor.
Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information

We present a new statistical approach combining temporal and spatial information for robust online background subtraction (BS) in videos. Temporal information is modeled by coupling finite mixtures of generalized Gaussian distributions with foreground/background co-occurrence analysis. Spatial information is modeled by combining multiscale inter-frame correlation analysis and histogram matching. We propose an online algorithm that efficiently fuses both information to cope with several BS challenges, such as cast shadows, illumination changes, and various complex background dynamics. In addition, global video information is used through a displacement measuring technique to deal with pan-tilt-zoom camera effects. Experiments with comparison with recent state-of-the-art methods have been conducted on standard data sets. Obtained results have shown that our approach surpasses several state-of-the-art methods on the aforementioned challenges while maintaining comparable computational time.
A COP1-GATA2 axis suppresses AR signaling and prostate cancer

Androgen receptor (AR) signaling is crucial for driving prostate cancer (PCa), the most diagnosed and the second leading cause of death in male patients with cancer in the United States. Androgen deprivation therapy is initially effective in most instances of AR-positive advanced or metastatic PCa. However, patients inevitably develop lethal castration-resistant PCa (CRPC), which is also resistant to the next-generation AR signaling inhibitors. Most CRPCs maintain AR expression, and blocking AR signaling remains a main therapeutic approach. GATA2 is a pioneer transcription factor emerging as a key therapeutic target for PCa because it promotes AR expression and activation. While directly inhibiting GATA2 transcriptional activity remains challenging, enhancing GATA2 degradation is a plausible therapeutic strategy. How GATA2 protein stability is regulated in PCa remains unknown. Here, we show that constitutive photomorphogenesis protein 1 (COP1), an E3 ubiquitin ligase, drives GATA2 ubiquitination at K419/K424 for degradation. GATA2 lacks a conserved [D/E](x)xxVP[D/E] degron but uses alternate BR1/BR2 motifs to bind COP1. By promoting GATA2 degradation, COP1 inhibits AR expression and activation and represses PCa cell and xenograft growth and castration resistance. Accordingly, GATA2 overexpression or COP1 mutations that disrupt COP1-GATA2 binding block COP1 tumor-suppressing activities. We conclude that GATA2 is a major COP1 substrate in PCa and that COP1 promotion of GATA2 degradation is a direct mechanism for regulating AR expression and activation, PCa growth, and castration resistance.
Levels of neuroticism can predict attentional performance during cross-modal nonspatial repetition inhibition

Inhibition of return (IOR) refers to the slower response to targets presented at previously attended locations, and such repetition-induced inhibition has been found to be differentially associated with personality traits. Although it has been well documented how personality traits affect spatial IOR, a mechanism associated with the attentional orienting network, there is not yet a consensus as to the relationship between personality traits and nonspatial repetition inhibition, a mechanism associated with the attentional executive network. The present study herein examined how the Big Five personality traits relate to cross-modal nonspatial repetition inhibition. Participants completed the NEO-PI-R and performed a cross-modal nonspatial repetition inhibition task built on the prime-neutral cue-target paradigm, in which the relationships of the identities and modalities between the prime and the target were manipulated. The results showed a significant nonspatial inhibitory effect and the effect was larger under the visual-auditory condition than under the auditory-visual condition. More importantly, neuroticism was associated with decreased cross-modal nonspatial inhibitory effect, presumably due to impaired attentional control. However, such a result was only found in the visual-auditory condition. We propose that retrieving previous prime representations under the visual-auditory condition requires a large consumption of cognitive resources, making inhibitory control more difficult for individuals with high neuroticism. These findings provide new insight into the influence of personality traits on attentional performance requiring nonspatial inhibitory control and enrich the relationship between neuroticism and repetition-induced inhibition.
Auto-responsive technologies for thermal renovation of opaque facades

Auto-responsive technologies (ARTs) operate in an intrinsic mode undergoing reversible changes in one or more of their properties in direct response to external stimuli variations. The aim of this paper is to identify their potential use for the thermal renovation of opaque facades of buildings in order to reach climate adaptivity. Adaptive facade concept offers a huge potential for thermal renovation, by improving occupants' comfort, promoting sector decarbonization, and being an opportunity for adapting facades to climate change. A literature review permitted the systematization of thermal renovation adaptive strategies (TRAS) in which ARTs can be useful. The facade reversible changes (outputs) required for each strategy were identified, as well as the possible adaptation mechanisms to obtain them. The technologies responding within the recognized adaptation mechanisms were described and compared, their readiness level identified and their role within the TRAS was assigned. An approach to assess the suitability of each ART regarding TRAS for existing facades was conceived. It uses the criteria of aesthetic change, additional space and demolition needs, localization and area of intervention. The role of each ART within each TRAS, here systematized, will support the definition of the ART's operational range for each application and climate, and will promote the research and development of these technologies for thermal renovation of buildings' facades. (C) 2020 Elsevier B.V. All rights reserved.
Interactive Sand Art Drawing Using RGB-D Sensor

We present an interactive system using one RGB-D sensor, which allows a user to use bare hands to perform sand drawing. Our system supports the common sand drawing functions, such as sand erosion, sand spilling, and sand leaking. To use hands to manipulate the virtual sand, we design four key hand gestures. The idea is that the gesture of one hand controls the drawing actions. The motion and gesture of the other hand control the drawing positions. There are three major steps. First, our system adopts a vision-based bare-hand detection method which computes the hand position and recognizes the hand gestures. Second, the drawing positions and the drawing actions are sent to a sand drawing subsystem. Finally, the subsystem performs the sand drawing actions. Experimental results show that our system enables users to draw a rich variety of sand pictures.
Synthesis and Late-Stage Diversification of BN-Embedded Dibenzocorannulenes as Efficient Fluorescence Organic Light-Emitting Diode Emitters

We report the synthesis and late-stage diversification of a new class of hetero-buckybowl, BN-embedded dibenzocorannulenes (B2 N2 -DBCs). The synthesis is achieved via one-shot halogenative borylation, comprising the nitrogen-directed haloboration of alkyne and an intramolecular bora-Friedel-Crafts reaction, which provides BN-embedded dibenzocorannulene possessing two bromo substituents (B2 N2 -DBC-Br). B2 N2 -DBC-Br undergoes diversification via coupling reactions to provide a variety of arylated derivatives (B2 N2 -DBC-R), exhibiting strong blue fluorescence. An organic light-emitting diode (OLED) employing one of the derivatives as an emitter exhibited a high external quantum efficiency of 6.6 % and long operational lifetime of 907 h at an initial luminance of 1000 cd m-2 , indicating the significant potential for the development of efficient and stable hetero-buckybowl-based OLED materials.
Pixel Modeling Using Histograms Based on Fuzzy Partitions for Dynamic Background Subtraction

We propose a novel pixel-modeling approach for background subtraction using histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal distribution of pixel values is represented by a histogram based on a triangular partition. The threshold for background segmentation is set adaptively according to the shape of the histogram. Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the proposed algorithm functions well across a wide spectrum of challenging environments. The performance of the proposed method is evaluated against more than 20 state-of-the-art methods in complex outdoor environments, particularly in those consisting of highly dynamic backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed method performs effectively in terms of both the true positive rate and the noise suppression ability. Further, it outperforms other state-of-the-art methods by a significant margin.