ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Techno-economic assessment of microbial electrosynthesis from CO2 and/or organics: An interdisciplinary roadmap towards future research and application

Microbial electrosynthesis (MES) allows carbon-waste and renewable electricity valorization into industrially-relevant chemicals. MES has received much attention in laboratory-scale research, although a technoeconomic-driven roadmap towards validation and large-scale demonstration of the technology is lacking. In this work, two main integrated systems were modelled, centered on (1) MES-from-CO2 and (2) MES from short-chain carboxylates, both for the production of pure, or mixture of, acetate, n-butyrate, and n-caproate. Twenty eight key parameters were identified, and their impact on techno-economic feasibility of the systems assessed. The main capital and operating costs were found to be the anode material cost (59%) and the electricity consumption (up to 69%), respectively. Under current state-of-the-art MES performance and economic conditions, these systems were found non-viable. However, it was demonstrated that sole improvement of MES performance, independent of improvement of non-technological parameters, would result in profitability. In otherwise state-of-the-art conditions, an improved electron selectivity (>= 36%) towards n-caproate, especially at the expense of acetate, was showed to result in positive net present values (i.e. profitability; NPV). Cell voltage, faradaic efficiency, and current density also have significant impact on both the capital and operating costs. Variation in electricity cost on overall process feasibility was also investigated, with a cost lower than 0.045 (sic) kWh(-1) resulting in positive NPV of the state-of-the-art scenario. Maximum purification costs were also determined to assess the integration of a product's separation unit, which was showed possible at positive NPV. Finally, we briefly discuss CO2 electroreduction versus MES, and their potential market complementarities.
An Exploratory Trial of Brief Mindfulness-Based Zentangle Art Workshops in Family Social Services during COVID-19: Transitioning from Offline to Online

Mindfulness-based art therapy has shown to improve psychological well-being. Zentangle is an easy-to-learn, mindfulness-based art therapy suitable for everyone. We reported the transition from face-to-face to online Zentangle workshops in family social services during COVID-19. We explored feedback from face-to-face workshops and the acceptability of an online approach utilizing information communication technology (ICT) to achieve greater service reach, satisfaction, and knowledge and related outcomes. Under the Hong Kong Jockey Club SMART Family-Link Project and in collaboration with Caritas Integrated Family Service Centre-Aberdeen, this study was conducted in two phases: a four-session, face-to-face workshop (phase one) and eleven online single-session workshops (phase two) from September 2019 to September 2020. A total of 305 participants joined the workshops. Phase one participants (n = 11) reported high satisfaction (4.7 out of 5), increases in knowledge (4.2/5) and confidence (3.9/5) towards managing stress, increases in knowledge (4.1/5) and confidence (3.9/5) in showing support and care towards family members, and an increase in knowledge towards strengthening family relationships (4.0/5). Phase two participants (n = 294) also reported high satisfaction (4.7/5) and strongly agreed that ICT helped with learning Zentangle more conveniently, that they had increased knowledge and interest in Zentangle (all 4.7/5), and would definitely join the workshop again (4.8/5). The qualitative data supported the quantitative findings. We are the first to report on the utilization of ICT in an exploratory trial of brief, online Zentangle art workshops targeting the general public, with high satisfaction and positive participant experiences with ICT integration, learning Zentangle, and enhanced psychological and family well-being. This study provided preliminary evidence on the use of ICT to successfully transition face-to-face to online workshops and reach a wider audience.
Symbolic expression in Pleistocene Sahul, Sunda, and Wallacea

The pace of research undertaken in Sunda (Southeast Asia) through to Sahul (Greater Australia) has increased exponentially over the last three decades, resulting in spectacular discoveries ranging from new hominin species, significant extension to the age for first human occupation in the region, as well as the identification of what is currently the oldest known rock art in the world. These breakthroughs cast the archaeological record of complexity in Sunda, Sahul, and Wallacea in an entirely different light to that of several decades ago, placing it on an equal footingto that of Africa, Asia, and Europe. The archaeological record of these regions now points to rich and diverse early modern human (Homo sapien) societies engaged in complex symbolic and technological behaviours demonstrating capacities for innovation and self-expression found in all modern human groups now around the globe. Here we provide a comprehensive review of all Pleistocene symbolic evidence reported for Sahul, Sunda, and Wallacea to date. We explore how recent findings have changed our perceptions of the first modern human colonists and our understanding of the origins and development of the rich and diverse cultures that arose in each region through time. (C) 2019 Elsevier Ltd. All rights reserved.
A Quantitative Measure of Conformational Changes in Apo, Holo and Ligand-Bound Forms of Enzymes

Determination of the native geometry of the enzymes and ligand complexes is a key step in the process of structure-based drug designing. Enzymes and ligands show flexibility in structural behavior as they come in contact with each other. When ligand binds with active site of the enzyme, in the presence of cofactor some structural changes are expected to occur in the active site. Motivation behind this study is to determine the nature of conformational changes as well as regions where such changes are more pronounced. To measure the structural changes due to cofactor and ligand complex, enzyme in apo, holo and ligand-bound forms is selected. Enzyme data set was retrieved from protein data bank. Fifteen triplet groups were selected for the analysis of structural changes based on selection criteria. Structural features for selected enzymes were compared at the global as well as local region. Accessible surface area for the enzymes in entire triplet set was calculated, which describes the change in accessible surface area upon binding of cofactor and ligand with the enzyme. It was observed that some structural changes take place during binding of ligand in the presence of cofactor. This study will helps in understanding the level of flexibility in protein-ligand interaction for computer-aided drug designing.
Rock art of the upper Lluta valley, northernmost of Chile (South Central Andes): A visual approach to socio-economic changes between Archaic and Formative periods (6,000-1,500 years BP)

Though they are generally characterized on the basis of faunal remains or lithic industries, in the highlands of northernmost Chile, the cultural aspects of the socio-economic changes, between Archaic and Formative periods (6000-1500 years BP), from hunter-gatherer to pastoral modes of life, a consequence of the domestication of camelids, can be discussed through the numerous scenes painted on the stone surfaces of rock shelters. The originality of these representations lies in the precision with which certain practices are represented, and in the socio-economic and symbolic relationships that between humans and animals, specifically with the camelids of the Andes. The present study is based on the analysis of these scenes, with the human-animal relationship, and the graphic superpositions, at six rock-art sites in the upper Lluta valley in precordillera or andean foothill, of the northernmost of Chile. We observe that the technical investment and the objective of the scenes become increasingly complex and focused on the control and possible protection of the animal. In the absence of archaeological contexts related to domestication in this region of the South Central Andes, this new study provides an innovative approach to the progressive changes of practices related to animal management, several hundreds of miles from the area where in situ domestication is evidenced. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.
Strengthened removal of emerging contaminants over S/Fe codoped activated carbon fabricated by a mild one-step thermal transformation scheme

Thermal transformation of carbonized materials to functional activated carbon (AC) is a simplified, economical and eco-friendly strategy, which has great potential in the practical applications of water purification. Herein, a S/Fe codoped activated carbon (S/Fe@AC) with only 0.90 wt% S and 0.76 wt% Fe was creatively fabricated by one synchronous method of physical activation, carbothermal reduction and sulfidation in the solid phase. The formed iron sulfide shell significantly enhances the antioxidation ability of nanoscale zero-valent iron (NZVI, >180 d) and dramatically improves the hydrophobicity of the composite. Meanwhile, the doped thiophenic S in AC enhances the hydrophobicity and increases the specific surface area to 1194.14 m2 g-1. Incorporating with AC in turn greatly strengthens the dispersibility and stability of sulfurized NZVI particles. Compared to NZVI@AC, AC and NZVI, the removal capacity of S/Fe@AC for the representative hydrophobic contaminant-triclosan (TCS) increases to 519.68 mg g-1 by 66.60%, 78.60% and 981.21%, respectively, outperforming most of the previously reported materials. The strong hydrophobic and π-π interactions, and weak hydrogen bonding and electrostatic repulsion are responsible for the excellent removal performance for TCS. More importantly, the improved chemical property (29.38%) of the composite caused by the doped S/Fe has a greater effect on TCS removal compared with the changed physical structure (14.56%). Furthermore, the stable S/Fe@AC shows strong anti-interference capability and exceptional regenerability. These intriguing discoveries provide new insights into the design of advanced and sustainable adsorbing materials for emerging contaminants.
Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)

The QArt-Learn approach for style painting categorization based on Qualitative Color Descriptors (QCD), color similarity (SimQCD), and quantitative global features (i.e. average of brightness, hue, saturation and lightness and brightness contrast) is presented in this paper. k-Nearest Neighbor (k-NN) and support vector machine (SVM) techniques have been used for learning the features of paintings from the Baroque, Impressionism and Post-Impressionism styles. Specifically two classifiers are built, and two different parameterizations have been applied for the QCD. For testing QArt-Learn approach, the Painting-91 dataset has been used, from which the paintings corresponding to Velazquez, Vermeer, Monet, Renoir, van Gogh and Gauguin were extracted, resulting in a set of 252 paintings. The results obtained have shown categorization accuracies higher than 65%, which are comparable to accuracies obtained in the literature. However, QArt-Learn uses qualitative color names which can describe style color palettes linguistically, so that they can be better understood by non-experts in art since QCDs are aligned with human perception. (C) 2017 Elsevier Ltd. All rights reserved.
Unsupervised densely attention network for infrared and visible image fusion

Integrating the information of infrared and visible images without human supervision is a long-standing problem. A key technical challenge in this domain is how to extract features from heterogeneous data-sources and fuse them appropriately. Prior deep learning works either extract the middle layers information or use costly training step to improve fusion performance, which limited their performances in cluttered scenes and real-time applications. In this paper, we introduce a novel and pragmatic unsupervised infrared and visible image fusion method based on a pre-trained deep network, which employs a densely connection structure and incorporates the attention mechanism to achieve high fusion performance. Furthermore, we propose to use the cross-dimensional weighting and aggregation to compute the attention map for infrared and visible image fusion. The attention map enables more efficient feature extraction and captures more structure information from source images. We evaluate our method and compare it with ten typical state-of-the-art fusion methods. Extensive experimental results demonstrate that our method achieves state-of-the-art fusion performance in both subjective and objective evaluation.
Dietary zinc intake and body mass index as modifiers of the association between household pesticide exposure and infertility among US women: a population-level study

Clinical studies on the relationship between pesticide exposure at home and infertility in the general population are scarce. Whether the antioxidant nutrients or other health-related factors affect the pesticide-infertility relationship remains unknown. This nationwide study screened 29,400 participants of the National Health and Nutrition Examination Surveys conducted between 2013 and 2018. The participants were subdivided according to dietary zinc intake based on the recommended dietary allowances as the low-zinc and high-zinc groups (< 8 and ≥ 8 mg/day, respectively), and according to body mass index (BMI; cut-off 28 kg/m2) as the low-BMI and high-BMI groups. Participants who were exposed to pesticides at home had an increased risk of infertility (odds ratio [OR] = 1.56, 95% confidence intervals [CI]: 1.06-2.29). The incidence of infertility differed in low-zinc and high-zinc groups (OR, 95% CI: 2.38, 1.40-4.06 vs. 0.98, 0.53-1.79, respectively), indicating an interaction between pesticide exposure and zinc intake in households (P = 0.047), which suggests that a zinc-rich diet may reduce the risk of pesticide-induced infertility. Similarly, the relationship between pesticide exposure and infertility risk differed in the low-BMI and high-BMI groups (OR, 95% CI: 0.90, 0.42-1.93 vs. 2.23, 1.39-3.58, respectively; P = 0.045), suggesting that high BMI may intensify the infertility risk caused by pesticide exposure. These new findings reveal the antagonistic and synergistic effect of zinc and obesity, respectively, in pesticide-induced infertility risk and suggest that individuals who are obese and on a low-zinc diet may be more susceptible to infertility induced by household pesticide exposure.
SPHARM-Net: Spherical Harmonics-Based Convolution for Cortical Parcellation

We present a spherical harmonics-based convolutional neural network (CNN) for cortical parcellation, which we call SPHARM-Net. Recent advances in CNNs offer cortical parcellation on a fine-grained triangle mesh of the cortex. Yet, most CNNs designed for cortical parcellation employ spatial convolution that depends on extensive data augmentation and allows only predefined neighborhoods of specific spherical tessellation. On the other hand, a rotation-equivariant convolutional filter avoids data augmentation, and rotational equivariance can be achieved in spectral convolution independent of a neighborhood definition. Nevertheless, the limited resources of a modern machine enable only a finite set of spectral components that might lose geometric details. In this paper, we propose (1) a constrained spherical convolutional filter that supports an infinite set of spectral components and (2) an end-to-end framework without data augmentation. The proposed filter encodes all the spectral components without the full expansion of spherical harmonics. We show that rotational equivariance drastically reduces the training time while achieving accurate cortical parcellation. Furthermore, the proposed convolution is fully composed of matrix transformations, which offers efficient and fast spectral processing. In the experiments, we validate SPHARM-Net on two public datasets with manual labels: Mindboggle-101 (N=101) and NAMIC (N=39). The experimental results show that the proposed method outperforms the state-of-the-art methods on both datasets even with fewer learnable parameters without rigid alignment and data augmentation. Our code is publicly available at https://github.com/Shape-Lab/SPHARM-Net.
Carotid wall segmentation in longitudinal ultrasound images using structured random forest

Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean +/- standard deviation of 0.66mm +/- 0.14, which is closer to the ground truth value 0.67mm +/- 0.15 as compared to the state-of-the-art techniques. (C) 2018 Elsevier Ltd. All rights reserved.
Segmentation of agriculture products images by fuzzy ART

Computer vision has proven successful for online measurement of several agriculture products with applications ranging from routine inspection to the complex vision guided robotic control. Agriculture products image segmentation is a major step of computer vision tasks. This paper proposes a segmentation method of agriculture products images by combining fuzzy clustering with wavelet transform. The new approach employs fuzzy art and color texture characterization. The experimental results have also shown that the proposed method can obtain satisfactory results of agriculture products image segmentation, for the subsequent automatic inspection systems and computer vision systems.
A Miniaturized 0.78-mW/cm(2) Autonomous Thermoelectric Energy-Harvesting Platform for Biomedical Sensors

In order to use thermoelectric energy harvesters (TEHs) as a truly autonomous energy source for size-limited sensing applications, it is essential to improve the power conversion efficiency and energy density. This study presents a thin-film, arraybasedTEHwith a surface area of 0.83 cm(2). TheTEHautonomously supplies a power management IC fabricated in a 65-nm CMOS technology. The IC utilizes a single-inductor topology with integrated analog maximum power point tracking (MPPT), resulting in a 68% peak end-to-end efficiency (92% converter efficiency) and less than 20-ms MPPT. In an in-vivo test, a 645-mu Wregulated output power (effective 3.5 K of temperature gradient) was harvested from a rat implanted with our TEH, demonstrating true energy independence in a real environment while showing a 7.9 x improvement in regulated power density compared to the state-ofthe-art. The system showed autonomous operation down to 65-mV TEH input.
Deep CNN-Based Blind Image Quality Predictor

Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the stateof- the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to noreference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i. e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method-deep image quality assessor (DIQA)-separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
Prospects and challenges of multi-layer optical networks

This paper investigates the prospects and challenges of hierarchical optical path networks. The merits and issues of introducing higher order optical paths are elucidated. State of the art of the key enabling technologies are demonstrated including hierarchical optical cross-connect switch architectures, hierarchical optical path network design algorithms, a newly developed waveband filter, and waveband conversion technologies.
Chemo-enzymatic Synthesis of Pseudo-trisaccharide Aminoglycoside Antibiotics with Enhanced Nonsense Read-through Inducer Activity

Aminoglycosides (AGs) are broad-spectrum antibiotics used to treat bacterial infections. Over the last two decades, studies have reported the potential of AGs in the treatment of genetic disorders caused by nonsense mutations, owing to their ability to induce the ribosomes to read through these mutations and produce a full-length protein. However, the principal limitation in the clinical application of AGs arises from their high toxicity, including nephrotoxicity and ototoxicity. In this study, five novel pseudo-trisaccharide analogs were synthesized by chemo-enzymatic synthesis by acid hydrolysis of commercially available AGs, followed by an enzymatic reaction using recombinant substrate-flexible KanM2 glycosyltransferase. The relationships between their structures and biological activities, including the antibacterial, nephrotoxic, and nonsense readthrough inducer (NRI) activities, were investigated. The absence of 1-N-acylation, 3',4'-dideoxygenation, and post-glycosyl transfer modifications on the third sugar moiety of AGs diminishes their antibacterial activities. The 3',4'-dihydroxy and 6'-hydroxy moieties regulate the in vitro nephrotoxicity of AGs in mammalian cell lines. The 3',4'-dihydroxy and 6'-methyl scaffolds are indispensable for the ex vivo NRI activity of AGs. Based on the alleviated in vitro antibacterial properties and nephrotoxicity, and the highest ex vivo NRI activity among the five compounds, a kanamycin analog (6'-methyl-3''-deamino-3''-hydroxykanamycin C) was selected as a novel AG hit for further studies on human genetic disorders caused by premature transcriptional termination.
Spatio-temporal pulse propagation in nonlinear dispersive optical media

We discuss state-of-art approaches to modeling of propagation of ultrashort optical pulses in one and three spatial dimensions. We operate with the analytic signal formulation for the electric field rather than using the slowly varying envelope approximation, because the latter becomes questionable for few-cycle pulses. Suitable propagation models are naturally derived in terms of unidirectional approximation.