ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability

Successful development of wind farms relies on the optimal siting of wind turbines to maximize the power capacity under stochastic wind conditions and wake losses caused by neighboring turbines. This paper presents a novel method to quickly generate approximate optimal layouts to support infrastruc-ture design decisions. We model the quadratic integer formulation of the discretized layout design problem with an undirected graph that succinctly captures the spatial dependencies of the design pa-rameters caused by wake interactions. On the undirected graph, we apply probabilistic inference using sequential tree-reweighted message passing to approximate turbine siting. We assess the effectiveness of our method by benchmarking against a state-of-the-art branch and cut algorithm under varying wind regime complexities and wind farm discretization resolutions. For low resolutions, probabilistic infer-ence can produce optimal or nearly optimal turbine layouts that are within 3% of the power capacity of the optimal layouts achieved by state-of-the-art formulations, at a fraction of the computational cost. As the discretization resolution (and thus the problem size) increases, probabilistic inference produces optimal layouts with up to 9% more power capacity than the best state-of-the-art solutions at a much lower computational cost. (c) 2021 Elsevier Ltd. All rights reserved.
Fast MR Image Reconstruction for Partially Parallel Imaging With Arbitrary k-Space Trajectories

Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. The proposed method is a combination of variable splitting, the classical penalty technique and the optimal gradient method. Variable splitting and the penalty technique reformulate the SENSE model with sparsity regularization as an unconstrained minimization problem, which can be solved by alternating two simple minimizations: One is the total variation and wavelet based denoising that can be quickly solved by several recent numerical methods, whereas the other one involves a linear inversion which is solved by the optimal first order gradient method in our algorithm to significantly improve the performance. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the computation efficiency and achieves state-of-the-art reconstruction quality.
DNA damage contributes to age-associated differences in SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to disproportionately affect older individuals. How aging processes affect SARS-CoV-2 infection and disease progression remains largely unknown. Here, we found that DNA damage, one of the hallmarks of aging, promoted SARS-CoV-2 infection in vitro and in vivo. SARS-CoV-2 entry was facilitated by DNA damage caused by extrinsic genotoxic stress or telomere dysfunction and hampered by inhibition of the DNA damage response (DDR). Mechanistic analysis revealed that DDR increased expression of angiotensin-converting enzyme 2 (ACE2), the primary receptor of SARS-CoV-2, by activation of transcription factor c-Jun. Importantly, in vivo experiment using a mouse-adapted viral strain also verified the significant roles of DNA damage in viral entry and severity of infection. Expression of ACE2 was elevated in the older human and mice tissues and positively correlated with γH2AX, a DNA damage biomarker, and phosphorylated c-Jun (p-c-Jun). Finally, nicotinamide mononucleotide (NMN) and MDL-800, which promote DNA repair, alleviated SARS-CoV-2 infection and disease severity in vitro and in vivo. Taken together, our data provide insights into the age-associated differences in SARS-CoV-2 infection and a novel approach for antiviral intervention.
Maternal Delta-9-Tetrahydrocannabinol Exposure Induces Abnormalities of the Developing Heart in Mice

Introduction: Cannabis is increasingly being consumed by pregnant women for recreational purposes as well as for its antiemetic and anxiolytic effects despite limited studies on its safety during pregnancy. Importantly, phytocannabinoids found in cannabis can pass through the placenta and enter the fetal circulation. Recent reports suggest gestational cannabis use is associated with negative fetal outcomes, including fetal growth restriction and perinatal intensive care, however, the effects of delta-9-tetrahydrocannabinol (THC) on fetal heart development remains to be elucidated. Materials and Methods: We aimed to determine the outcomes of maternal THC exposure on fetal heart development in mice by administering 0, 5, or 10 mg/kg/day of THC orally to C57BL/6 dams starting at embryonic day (E)3.5. Offspring were collected at E12.5 for molecular analysis, at E17.5 to analyze cardiac morphology or at postnatal day (PND)21 to assess heart function. Results: Maternal THC exposure in E17.5 fetuses resulted in an array of cardiac abnormalities with an incidence of 44% and 55% in the 5 and 10 mg/kg treatment groups, respectively. Maternal THC exposure in offspring resulted in ventricular septal defect, higher semilunar valve volume relative to orifice ratio, and higher myocardial wall thickness. Notably, cell proliferation within the ventricular myocardium was increased, and expression of multiple cardiac transcription factors was downregulated in THC-exposed E12.5 fetuses. Furthermore, heart function was compromised with lower left ventricular ejection fraction, fractional shortening, and cardiac output in PND21 pups exposed to THC compared to controls. Discussion: The results show that maternal THC exposure during gestation induces myocardial hyperplasia and semilunar valve thickening in the fetal heart and postnatal cardiac dysfunction. Our study suggests that maternal cannabis consumption may induce abnormalities in the developing heart and cardiac dysfunction in postnatal life.
Condensation water in heritage touristic caves: Isotopic and hydrochemical data and a new approach for its quantification through image analysis

Condensation water is a major factor in the conservation of heritage caves. It can cause dissolution of the rock substrate (and the pigments of rock art drawn on it) or covering thereof with mineral components, depending on the chemical saturation degree of the condensation water. In show caves, visitors act as a source of CO2 and thus modify the microclimate, favouring negative processes that affect the conservation of the caves. In spite of their interest, studies of the chemical composition of this type of water are scarce and not very detailed. In this work we present research on the condensation water in the Nerja Cave, one of the main heritage and tourist caves in Europe. The joint analysis of isotopic, hydrochemical, mineralogical and microbiological data and the use of image analysis have allowed us to advance in the knowledge of this risk factor for the conservation of heritage caves, and to demonstrate the usefulness of image analysis to quantify the scope of the possible corrosion condensation process that the condensation water could be producing on the bedrock, speleothem and rock art. To our knowledge, this application of image analysis (relative to the condensation water in caves) is the first one of this type that has been documented.
A matrix inversion technique for the spherical modal decomposition field solution applied on the characterization of antennas in their environment

An efficient matrix-iversion technique is proposed for the spherical modal decomposition (SMD),field solution for radiating structures. The advantages of this approach compared to the classical dot-product technique are demonstrated through its application to the characterization of two radiating structures: the Yagi antenna and art antenna on the roof of a vehicle. (C) 2004 Wiley Periodicals, Inc.
A Tale of Two Sites: Neighboring Atomically Dispersed Pt Sites Cooperatively Remove Trace H2 in CO-Rich Stream

Single-atom catalysts (SACs) exhibit distinct catalytic behavior compared with nano-catalysts because of their unique atomic coordination environment without the direct bonding between identical metal centers. How these single atom sites interact with each other and influence the catalytic performance remains unveiled as designing densely populated but stable SACs is still an enormous challenge to date. Here, a fabrication strategy for embedding high areal density single-atom Pt sites via a defect engineering approach is demonstrated. Similar to the synergistic mechanism in binuclear homogeneous catalysts, from both experimental and theoretical results, it is proved that electrons would redistribute between the two oxo-bridged paired Pt sites after hydrogen adsorption on one site, which enables the other Pt site to have high CO oxidation activity at mild-temperature. The dynamic electronic interaction between neighboring Pt sites is found to be distance dependent. These new SACs with abundant Pt-O-Pt paired structures can improve the efficiency of CO chemical purification.
Overview of deep-learning based methods for salient object detection in videos

Video salient object detection is a challenging and important problem in computer vision domain. In recent years, deep-learning based methods have contributed to significant improvements in this domain. This paper provides an overview of recent developments in this domain and compares the corresponding methods up to date, including 1) Classification of the state-of-the-art methods and their frameworks; 2) summary of the benchmark datasets and commonly used evaluation metrics; 3) experimental comparison of the performances of the state-of-the-art methods; 4) suggestions of some promising future works for unsolved challenges. (C) 2020 Elsevier Ltd. All rights reserved.
Admittance matrices of power converters for distributed generators

To integrate distributed generators (DGs) with the power grid, interfacing power converters such as voltage source converters (VSCs) are required. However, due to the switching natures, these converters generate harmonics, which may have detrimental effects on the system. To find out how these harmonics may interact with the grid, it is essential to find the admittance matrices of the converters. In this paper, the state-of-the-art methods of modeling converters are thoroughly reviewed. Moreover, a new proposed method is also introduced, which can significantly increase the simulation efficiency. Finally, two numerical examples are presented. The results obtained by the proposed method are in great agreement with those of the state-of-the-art methods and brute-force time-domain simulation such as PSCAD/EMTDC, demonstrating the validity of the proposed method. (c) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Lithium solid-state batteries: State-of-the-art and challenges for materials, interfaces and processing

Lithium solid-state batteries (SSBs) are considered as a promising solution to the safety issues and energy density limitations of state-of-the-art lithium-ion batteries. Recently, the possibility of developing practical SSBs has emerged thanks to striking advances at the level of materials; such as the discovery of new highly-conductive solid-state electrolytes. Consequently, the focus in research has progressively shifted towards the integration of the various components, the battery's functionality at full cell level, and the scalability of the fabrication processes. Considering these points, the development of SSBs still faces formidable challenges. This review covers the recent advances in SSB development, stressing the importance of full cell integration. The most relevant materials and fabrication processes are briefly summarized and their potential applications in SSBs are examined. The main challenges and strategies for full cell integration are then discussed highlighting the most promising materials and the best suited processing techniques. Particular attention is paid on the mutual compatibility of the cell components, the properties of the interfaces within the cell (anode-electrolyte, cathode-electrolyte, intraelectrolyte) and the strategies applied to stabilize and minimize the resistance of these interfaces via compatible processing.
Automated Phrase Mining from Massive Text Corpora

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, AutoPhrase, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, AutoPhrase has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, AutoPhrase can be extended to model single-word quality phrases.
Minimizing Retention Induced Refresh Through Exploiting Process Variation of Flash Memory

Refresh schemes have been the default approach in NAND flash memory to avoid data losses. The critical issue of the refresh schemes is that they introduce additional costs on lifetime and performance. Recent work proposed to minimize the refresh costs by using uniform refresh frequencies based on the number of program/erase (P/E) cycles. However, from our investigation, we find that the refresh costs still have a high burden on the lifetime performance. In this paper, a novel refresh minimization scheme is proposed by exploiting the process variation (PV) of flash memory. State-of-the-art flash memory always has significant PV, which introduces large variations on the retention time of flash blocks. In order to reduce the refresh costs, we first propose a new refresh frequency determination scheme by detecting the supported retention time of flash blocks. If the detected retention time is large, a low refresh frequency can be applied to minimize the refresh costs. Second, considering that the retention time requirements of data are varied with each others, we further propose a data hotness and refresh frequency matching scheme. The matching scheme is designed to allocate data to blocks with right higher supported retention time. Through simulation studies, the lifetime and performance are significantly improved compared with state-of-the-art refresh schemes.
Functionalized Cellulose Nanocrystals as Active Reinforcements for Light-Actuated 3D-Printed Structures

Conventional manufacturing techniques allow the production of photoresponsive cellulose nanocrystals (CNC)-based composites that can reversibly modify their optical, mechanical, or chemical properties upon light irradiation. However, such materials are often limited to 2D films or simple shapes and do not benefit from spatial tailoring of mechanical properties resulting from CNC alignment. Herein, we propose the direct ink writing (DIW) of 3D complex structures that combine CNC reinforcement effects with photoinduced responses. After grafting azobenzene photochromes onto the CNC surfaces, up to 15 wt % of modified nanoparticles can be introduced into a polyurethane acrylate matrix. The influence of CNC on rheological properties allows DIW of self-standing 3D structures presenting local shear-induced alignment of the active reinforcements. The printed composites, with longitudinal elastic modulus of 30 MPa, react to visible-light irradiation with 30-50% reversible softening and present a shape memory behavior. The phototunable energy absorption of 3D complex structures is demonstrated by harnessing both geometrical and photoresponsive effects, enabling dynamic mechanical responses to environmental stimuli. Functionalized CNC in 3D printable inks have the potential to allow the rapid prototyping of several devices with tailored mechanical properties, suitable for applications requiring dynamic responses to environmental changes.
Yellow polyketide pigment suppresses premature hatching in social amoeba

Low-molecular-weight natural products from microbes are indispensable in the development of potent drugs. However, their biological roles within an ecological context often remain elusive. Here, we shed light on natural products from eukaryotic microorganisms that have the ability to transition from single cells to multicellular organisms: the social amoebae. These eukaryotes harbor a large number of polyketide biosynthetic genes in their genomes, yet virtually none of the corresponding products can be isolated or characterized. Using complementary molecular biology approaches, including CRISPR-Cas9, we generated polyketide synthase (pks5) inactivation and overproduction strains of the social amoeba Dictyostelium discoideum. Differential, untargeted metabolomics of wild-type versus mutant fruiting bodies allowed us to pinpoint candidate metabolites derived from the amoebal PKS5. Extrachromosomal expression of the respective gene led to the identification of a yellow polyunsaturated fatty acid. Analysis of the temporospatial production pattern of this compound in conjunction with detailed bioactivity studies revealed the polyketide to be a spore germination suppressor.
Perceiving Artistic Expression: A Formal Exploration of Performance Art Salsa

This paper studies artistic expression in human movement by exploring the performance art form salsa. The motions of a salsa performance are constructed as concatenations of motion primitives, each of which specifies the movement of the dance pair over the course of eight musical beats. To analyze the syntax of artistic expression, the choreography of dance performances is represented by a transition model that is based on humanoid robot representations of the dancers. In order to assess the quality of a performance, two distinct metrics are explored. By integrating the performance metrics into the proposed transition system, it is possible to create an algorithm that is capable of autonomously recognizing the dance moves and evaluating the quality of the performance with a score. To validate the model, a dance pair performed four distinct salsa dance sequences observed by an artificially intelligent (AI) judge. The video recordings of the performances are also shown to a dance audience for evaluation. By looking at the correlation between the dance audience and the AI judge's scores, we conclude that the proposed model performs well in evaluating the artistic merit of the dance.
Assisted Relaxation Therapy for Insomnia in Older Adults With Mild Cognitive Impairment: A Pilot Study

Insomnia symptoms are prevalent in older adults with mild cognitive impairment (MCI) and can pose treatment challenges. We tested the feasibility, acceptability, and preliminary efficacy of assisted relaxation therapy (ART) to improve insomnia symptoms in community-dwelling older adults with MCI. In this pilot RCT, 25 participants were assigned to intervention or control groups for 2 weeks. The final sample (n = 20) consisted of all Black, primarily female (70%) older adults (mean age 69.10; SD = 7.45) with mean Montreal Cognitive Assessment scores of 21.10 (SD = 2.49). Recruitment was timely; attrition was low (80%). Participants were able to use ART (average use 7.00; SD = 5.07 days). Participants in the ART group improved on Insomnia Severity Index (ISI) (- 7.10; 95% CI [-11.63, -2.55]; p = .004) compared to baseline. There were clinically meaningful mean change scores on ISI for the intervention group compared to the control (- 7.10 vs. - 4.33). Results provide justification for testing ART in a fully powered clinical trial.
Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning

Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38 +/- 0.95mm, outperforming a related state-of-the-art method.
Synthesis and characterization of Black Au nanoparticles deposited over g-C3N4 nanosheets: enhanced photocatalytic degradation of methylene blue

Black AuNPs, prepared by a facile seeding growth method under ambient conditions, displayed efficient broadband absorption of the incident light over the entire visible and near-infrared regions of the solar spectrum. The spherical black AuNPs with the size of 2-4 nm were deposited over mesoporous g-C3N4 nanosheets. Novel black AuNPs/g-C3N4 plasmonic photocatalysts were used to remove methylene blue (MB) dye from an aqueous solution. The degradation efficiency for the optimal coupling of 1.3 wt.% black AuNPs with g-C3N4 (1.2 g) was found to be 85% within 60 min under visible light irradiation. The calculated kinetic constant was 0.0186 min-1 which was 6.4 and 2.9 times greater than those for g-C3N4 and AuNPs/g-C3N4 nanocomposite, respectively. The excellent potential in photocatalysis was attributed to the synergistic interactions of the g-C3N4 conduction band and the localized surface plasmon resonance effect of black AuNPs. These properties were responsible for the generation of high-energy electrons, a negative shift in the Fermi level of black AuNPs, and the migration of charge carriers. This work studied a new insight into black gold nanoparticles via the design of a visible-light-driven photocatalyst and provided a perspective on valuable photo-related applications such as water treatment.