ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sustainable chemical technologies in production of clean fuels from fossil fuels

Some aspects of the present and the possible future role of sustainable chemical technologies in the production of clean liquid and gaseous fuels from fossil fuels are discussed. The state-of-the-art and the vision of possible sources and alternative routes which may lead to clean fuels from fossil fuels due to the progress in crude oil, natural gas and coal processing are briefly presented. The possible future role of the Fischer-Tropsch synthesis, methanol synthesis, dimethylether synthesis, and a group of methanol transformation processes is also discussed.
Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling

We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/- 0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
A critical review on microbial ecology in the novel biological nitrogen removal process: Dynamic balance of complex functional microbes for nitrogen removal

The novel biological nitrogen removal process has been extensively studied for its high nitrogen removal efficiency, energy efficiency, and greenness. A successful novel biological nitrogen removal process has a stable microecological equilibrium and benign interactions between the various functional bacteria. However, changes in the external environment can easily disrupt the dynamic balance of the microecology and affect the activity of functional bacteria in the novel biological nitrogen removal process. Therefore, this review focuses on the microecology in existing the novel biological nitrogen removal process, including the growth characteristics of functional microorganisms and their interactions, together with the effects of different influencing factors on the evolution of microbial communities. This provides ideas for achieving a stable dynamic balance of the microecology in a novel biological nitrogen removal process. Furthermore, to investigate deeply the mechanisms of microbial interactions in novel biological nitrogen removal process, this review also focuses on the influence of quorum sensing (QS) systems on nitrogen removal microbes, regulated by which bacteria secrete acyl homoserine lactones (AHLs) as signaling molecules to regulate microbial ecology in the novel biological nitrogen removal process. However, the mechanisms of action of AHLs on the regulation of functional bacteria have not been fully determined and the composition of QS system circuits requires further investigation. Meanwhile, it is necessary to further apply molecular analysis techniques and the theory of systems ecology in the future to enhance the exploration of microbial species and ecological niches, providing a deeper scientific basis for the development of a novel biological nitrogen removal process.
Antitumor efficacy of extracellular complexes with gadolinium in Binary Radiotherapy

In this report the efficacy of extracellular pharmaceutical Gd-DTPA in Binary Radiotherapy was studied. The study was carried out in mice bearing transplantable adenocarcinoma Ca755 using X-ray based contrast enhanced radiotherapy as a practical implementation of Binary Radiotherapy. It was shown that intravenous administration of 0.3 ml of 0.5 M water solution of Gd-DTPA followed by X-irradiation at a dose of 10 Gy provides T/C%=10±3% and leads to complete tumor regression in 25% of mice.
Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.
A comparison of radionuclide dispersion model performances for the Baltic Sea and Fukushima releases in the Pacific Ocean

State-of-the art dispersion models were applied to simulate Cs-137 dispersion from Chernobyl nuclear power plant disaster fallout in the Baltic Sea and from Fukushima Daiichi nuclear plant releases in the Pacific Ocean after the 2011 Tsunami. Models were of different nature, from box to full three-dimensional models, and included water/sediment interactions. Agreement between models was very good in the Baltic. In the case of Fukushima, results from models could be considered to be in acceptable agreement only after a model harmonization process consisting of using exactly the same forcing (water circulation and parameters) in all models.
Adaptive Pairing Reversible Watermarking

This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013. An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced. This adaptive approach provides an increased number of pixel pairs where both pixels are embedded and decreases the number of shifted pixels. The adaptive pairwise reversible watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.
KEY FACTORS IN SUCCESSFUL TEACHING OF SPACE ENVIRONMENT DESIGN UNDER INHERITANCE AND INNOVATION - TAKING THE CASE OF LACQUER ARTS TEACHING

The occurrence of Covid-19 pandemic had made it impossible for students to produce lacquer arts works from the school workshops. To address this issue and to respond to the appeal of Suspending Classes, but not Learning, Internet was made handy to set up situational teaching for spatial environment design. In this way, path for online teaching is explored, and creation passion of students are stimulated, challenges of courses exigency are resolved and great teaching effects had been accomplished. Taking the lacquer teaching forces in Beijing as the study subjects, this research get to know weights of each factor and its influences over teaching of space environment design. In a randomly manner, the subjects are handed out questionnaires. A total of 250 questionnaires are handed out and 183 ones are collected back, with a recovery rate of 83%. Through participation of space environment design teaching, it broke down the restrictions in time and space. Interests of learning on students were simulated, thinking approaches of creativity was widened, and the creation foundation was made solid, achieving the goal of enriching inheritance channels for lacquer arts inheritance.
Knowledge distillation methods for efficient unsupervised adaptation across multiple domains

Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-world applications. For instance, in person re-identification, videos are captured over a distributed set of cameras with non-overlapping viewpoints. The shift between the source (e.g. lab setting) and target (e.g. cameras) domains may lead to a significant decline in recognition accuracy. Additionally, state-of-the-art CNNs may not be suitable for such real-time applications given their computational requirements. Although several techniques have recently been proposed to address domain shift problems through unsupervised domain adaptation (UDA), or to accelerate/compress CNNs through knowledge distillation (KD), we seek to simultaneously adapt and compress CNNs to generalize well across multiple target domains. In this paper, we propose a progressive KD approach for unsupervised single target DA (STDA) and multi-target DA (MTDA) of CNNs. Our method for KD-STDA adapts a CNN to a single target domain by distilling from a larger teacher CNN, trained on both target and source domain data in order to maintain its consistency with a common representation. This method is extended to address MTDA problems, where multiple teachers are used to distill multiple target domain knowledge to a common student CNN. A different target domain is assigned to each teacher model for UDA, and they alternatively distill their knowledge to the student model to preserve specificity of each target, instead of directly combining the knowledge from each teacher using fusion methods. Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets. It is also compared against stateof-the-art methods for MTDA on Digits, Office31, and OfficeHome. In both settings ? KD-STDA and KD-MTDA ? results indicate that our approach can achieve the highest level of accuracy across target domains, while requiring a comparable or lower CNN complexity. ? 2021 Published by Elsevier B.V.
LIGHT DESIGN IN RUSSIAN UNIVERSITIES: WHAT AND HOW TO TEACH?

Light design as a prominent and self-sufficient branch of design has appeared and developed abroad and is a rather new part of Russian design practice. Along with solving practical problems, it is time to think on the aspects of training of light design specialists in Russian design universities. Engineering and technical, and partially artistic aspects in light design education, have international nature to a great extent, but the new specialty has been acquiring its own distinctions in Russia, which are mainly related to aesthetical problems of the profession and specific understanding of the cultural and historical context. These national distinctions have first appeared in practice and theoretical studies and then they began being translated into the educational sphere, turning into authorial education programmes and techniques and predetermining selection of design problems and their implementation methods. Light design implies training of interdisciplinary specialists, which requires a comprehensive approach to development of the educational process including innovative-technological as well as humanitarian sections, forming an unconventional model of the educational process. The Environment Design sub-department of Moscow State Stroganov Academy of Design and Applied Arts, the oldest Russian art and industry school, has gained a rich experience in artistic design in the field of light design, which comprises all leading professional trends. The methods of conventional artistic design, computer modelling, and animation skills as well as the knowledge included in the new discipline "The Basics of Light Design" have allowed developing of term and graduation projects having consideration for specific practical requirements and aiming at their future implementation.
Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of- the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.
Deep Neural Networks for Chronological Age Estimation From OPG Images

Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are proposed. The first (DANet) consists of a sequential Convolutional Neural Network (CNN) path to predict the age, while the second (DASNet) adds a second CNN path to predict the sex and uses sex-specific features with the aim of improving the age prediction performance. Both methods were tested on a set of 2289 OPG images of subjects from 4.5 to 89.2 years old, where both bad radiological quality images and images showing conditioning dental characteristics were not discarded. The results showed that the DASNet outperforms the DANet in every aspect, reducing the median Error (E) and the median Absolute Error (AE) by about 4 months in the entire database. When evaluating the DASNet in the reduced datasets, the AE values decrease as the real age of the subjects decreases, until reaching a median of about 8 months in the subjects younger than 15. The DASNet method was also compared to the state-of-the-art manual age estimation methods, showing significantly less over- or under-estimation problems. Consequently, we conclude that the DASNet can be used to automatically predict the chronological age of a subject accurately, especially in young subjects with developing dentitions.
Spray-Dried Photonic Balls with a Disordered/Ordered Hybrid Structure for Shear-Stress Indication

Optical microscale shear-stress indicator particles are of interest for the in situ recording of localized forces, e.g., during 3D printing or smart skins in robotic applications. Recently developed particle systems are based on optical responses enabled by integrated organic dyes. They thus suffer from potential chemical instability and cross-sensitivities toward humidity or temperature. These drawbacks can be circumvented using photonic balls as shear-stress indicator particles, which employ structural color as the element to record forces. Here, such photonic balls are prepared from silica and iron oxide nanoparticles via the scalable and fast spray-drying technique. Process parameters to create photonic balls with a disordered core and an ordered particle structure toward the exterior of the supraparticles are reported. This hybrid disordered-ordered structure is responsible for a color loss of the indicator particles during shear-stress application because of irreversible structural destruction. By adjusting the primary silica particle sizes, nearly all colors of the visible spectrum can be achieved and the sensitivity of the response to shear stress can be adjusted.
Complete mitochondrial genome of Parastichopus californicus (Aspidochirotida: Stichopodidae)

In this study, we first determined and described the complete mitogenome sequence of Parastichopus californicus, which was 16 727 bp in length. The mitochondrial genome had the canonical mitochondrial gene content, including 13 protein-coding genes, 2 rRNA genes and 22 tRNA genes. The overall base composition of the heavy-strand was 31.5% A, 18 % G, 20.6% C and 29.9% T, with a high A + T content of 61.4%. ML phylogenetic tree indicated that P. californicus and P. nigripunctatus were clustered in one branch belonging to the genus Parastichopus. This conclusion was identical to the former result by the methods of morphological taxonomy.
An Entropic Optimal Transport loss for learning deep neural networks under label noise in remote sensing images

Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a challenging and tedious task in most scenarios of remote sensing image analysis, thus cheap surrogate procedures are employed to label the dataset. Training deep neural networks on such datasets with inaccurate labels easily overfits to the noisy training labels and degrades the performance of the classification tasks drastically. To mitigate this effect, we propose an original solution with entropic optimal transportation. It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples. We empirically demonstrate on several remote sensing datasets, where both scene and pixel-based hyperspectral images are considered for classification. Our method proves to be highly tolerant to significant amounts of label noise and achieves favorable results against state-of-the-art methods.
Characterisation of particulate matter in the Royal Museum of Fine Arts, Antwerp, Belgium

Aerosol samples were collected during two campaigns in February and July 1999 both inside and outside the Koninklijk Museum voor Schone Kunsten (KMSK, Royal Museum of Fine Arts) in Antwerp. Bulk aerosol concentrations, as well as the composition of the individual particles, were determined. The influence of the outdoor aerosol was clearly visible. In winter, restoration and construction works constituted an additional indoor source of Ca-rich and Ca-Si particles. Along with sea salt, these were the main particle types identified in this season. In summer, S-rich particles were most frequent. The summer abundances of Ca-rich particles remained low, even though the museum is situated in a limestone building. Moreover, dry deposition samples were collected in order to determine what amount of particles could actually be deposited onto the works of art. (C) 2002 Elsevier Science Ltd. All rights reserved.
Subunit composition, molecular environment, and activation of native TRPC channels encoded by their interactomes

In the mammalian brain TRPC channels, a family of Ca2+-permeable cation channels, are involved in a variety of processes from neuronal growth and synapse formation to transmitter release, synaptic transmission and plasticity. The molecular appearance and operation of native TRPC channels, however, remained poorly understood. Here, we used high-resolution proteomics to show that TRPC channels in the rodent brain are macro-molecular complexes of more than 1 MDa in size that result from the co-assembly of the tetrameric channel core with an ensemble of interacting proteins (interactome). The core(s) of TRPC1-, C4-, and C5-containing channels are mostly heteromers with defined stoichiometries for each subtype, whereas TRPC3, C6, and C7 preferentially form homomers. In addition, TRPC1/C4/C5 channels may co-assemble with the metabotropic glutamate receptor mGluR1, thus guaranteeing both specificity and reliability of channel activation via the phospholipase-Ca2+ pathway. Our results unveil the subunit composition of native TRPC channels and resolve the molecular details underlying their activation.
Integrative biohydrogen- and biomethane-producing bioprocesses for comprehensive production of biohythane

The production of biohythane, a combination of energy-dense hydrogen and methane, from the anaerobic digestion of low-cost organic wastes has attracted attention as a potential candidate for the transition to a sustainable circular economy. Substantial research has been initiated to upscale the process engineering to establish a hythane-based economy by addressing major challenges associated with the process and product upgrading. This review provides an overview of the feasibility of biohythane production in various anaerobic digestion systems (single-stage, dual-stage) and possible technologies to upgrade biohythane to hydrogen-enriched renewable natural gas. The main goal of this review is to promote research in biohythane production technology by outlining critical needs, including meta-omics and metabolic engineering approaches for the advancements in biohythane production technology.