ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.
Geochemical analysis of the painted panels at the "Genyornis" rock art site, Arnhem Land, Australia

The so-called "Genyornis" rockshelter site on the Arnhem Land plateau, northern Australia, features a painting of a large bird that some archaeologists and paleontologists have suggested could be an image of the megafaunal species Genyornis newtoni, until recently widely thought to have become extinct some 45,000 years ago. However, a recent archaeologicalegeomorphological study has concluded that the rock surface that contains the large, enigmatic bird painting only became exposed during overhang collapse between 13,739-13,976 cal BP, so the rock art on this panel can only be more recent than this age. It is, therefore, most unlikely to represent a Genyornis bird. Using a range of analytical techniques, here we report on the geochemistry (microstructure and chemistry) of the sequence of microlayers that makes up the "surface" of the rock at the "Genyornis" site. Our aim is to provide insights into the composition, preparation, source, application, weathering and preservation of pigment used in the making of the paintings, and to clarify the superimpositional order and relationships between the enigmatic large bird painting originally thought to possibly be of a Genyornis, and other nearby images. Micro-stratigraphic superimpositions show that the earliest painting was of a large anthropomorph, followed sometime later by the simultaneous painting of the large enigmatic ("Genyornis") bird image together with a barbed spear image embedded in it. This significantly alters the perception of the paintings on the wall of this rockshelter, with relegation of the anthropomorph to the background, followed by the image of a speared large bird. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.
Crafting Sustainability: Handcraft in Contemporary Art and Cultural Sustainability in the Finnish Lapland

Crafting sustainability is discussed here with respect to the dimensions of handcraft traditions in contemporary art for promoting cultural sustainability in the Scandinavian North. Aspects of decolonization, cultural revitalisation, and intergenerational dialogue form an integral part of the negotiations around the need for cultural survival and renewal for a more sustainable future. These dimensions should also be considered in the development of the current education of art teachers. Learning traditional skills and applying them in contemporary art constitute an influential method when striving for cultural sustainability. This study examines three handcraft-based contemporary art cases through art-based action research conducted in the Finnish and the Swedish Lapland. The results show that handcraft-based contemporary art practices with place-specific intergenerational and intercultural approaches create an open space for dialogue where the values and the perceptions on cultural heritage can be negotiated.
Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region-and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics. (C) 2015 SPIE and IS&T
Conductive Iodine-Doped Red Phosphorus Enabled Dendrite-Free Lithium Deposition on MXene Matrix

The highest theoretical capacity and lowest redox potential of lithium metal make lithium-based batteries the "holy grail" of the next-generation batteries. However, the uncontrollable dendrite growth and infinite volume change of lithium seriously hinder the real-world implementation of lithium-based batteries. Herein, a flexible MXene@iodine-doped red phosphorus (MXene@RP) paper with iodine-doped red phosphorous particles evenly distributed on the surface and interlayer of MXene matrix is designed by a simple vapor condensation reduction approach. The MXene@RP paper can be used as an efficient matrix to enable dendrite-free lithium deposition. On the one hand, the iodine doping alleviates the low conductivity shortcoming of red phosphorus, making it facilitate homogeneous lithium nucleation, thus promoting uniform lithium deposition and suppressing dendrite growth. On the other hand, the unique layered structure of conductive MXene paper provides ion transport channels and free spaces for lithium loading, alleviating the volume change induced structural damage. As a result, the MXene@RP paper with preloaded lithium exhibits long-term cycling stability. Particularly, a full cell based on Li-MXene@RP anode can maintain 81.4% of the initial capacity after 600 cycles at 4 C. The MXene@RP-based anode increases the potential applications of MXene and provides a guide for the design of dendrite-free lithium hosts.
"I miss my school!": Examining primary and secondary school students' social distancing and emotional experiences during the Covid-19 pandemic

With the rapid spread of Covid-19, countries around the world implemented strict protocols ordering schools to close. As a result, educational institutions were forced to establish a new form of schooling by implementing emergency remote education. Learning from home during the Covid-19 pandemic brought numerous changes, challenges, and stressors to students' daily lives. In this context, major concerns have been raised based on the reports of students' negative experiences resulting from social distancing and isolation. Given the impact of Covid-19 on many aspects of students' lives, in particular their social and school experiences, research that provides insights into the consequences of this health crisis for students' well-being has become important. This study aims to explore students' experiences of social distancing and its relation to their negative emotional experiences during Germany's first Covid-19-related school closure. Findings indicate that both primary and secondary students missed their friends and classmates and that primary school students perceived higher levels of social distancing. However, a linear regression analysis indicated that the older the students were, the more negatively affected they were by social distancing. The implications of the study's results and further lines of research are discussed.
Can Nature-Based Solutions (NBSs) for Stress Recovery in Green Hotels Affect Re-Patronage Intention?

Our research framework in this paper investigated natural-based solutions (NBSs) at green hotels. We employed attention restoration theory (ART) to test the mediating effect of perceived stress (PS), psychological wellness (PW), satisfaction (SA), and the moderating effect of health consciousness (HC) on re-patronage intentions (RI). Data were collected through a survey of 544 customers who frequently visited green hotels in Korea, and structural equation modeling (SEM) was used to test the research hypotheses. The findings generally supported the hypothesized associations of the study variables within our proposed theoretical framework (PS, PW, SF) in order of the mediating effect on RI and confirmed the moderating effect of HC. In addition, the study's results have important theoretical and practical implications for the environment. In the former case, our results demonstrate the application of ART and NBS by explaining the effect of the relationship among PS, PW, and SF on RI and confirm the mediating effect of the ART (PS, PW, SF) on RI, as demonstrated in previous studies. Moreover, in the latter case our results may encourage green hotels to participate in the prevention of environmental problems.
Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.
The complete mitochondrial genome and phylogenic analysis of Pseudobagrus vachelli

The complete mitochondrial genome of Pseudobagrus vachelli has been sequenced. The mitochondrial genome is 16 529 bp in length, with the base composition of 31.61% A, 26.88% T, 26.55% C, and 14.96% G, containing 2 ribosomal RNA genes, 13 protein-coding genes, 22 transfer RNA genes and a major non-coding control region (D-loop region). The gene order and orientation are similar with some typical fish species. The data will provide useful molecular information for phylogenetic studies concerning P. vachelli and its related species.
Modeling genetic benefits and financial costs of integrating biobanking into the conservation breeding of managed marsupials

Managed breeding programs are an important tool in marsupial conservation efforts but may be costly and have adverse genetic effects in unavoidably small captive colonies. Biobanking and assisted reproductive technologies (ARTs) could help overcome these challenges, but further demonstration of their potential is required to improve uptake. We used genetic and economic models to examine whether supplementing hypothetical captive populations of dibblers (Parantechinus apicalis) and numbats (Myrmecobius fasciatus) with biobanked founder sperm through ARTs could reduce inbreeding, lower required colony sizes, and reduce program costs. We also asked practitioners of the black-footed ferret (Mustela nigripes) captive recovery program to complete a questionnaire to examine the resources and model species research pathways required to develop an optimized biobanking protocol in the black-footed ferret. We used data from this questionnaire to devise similar costed research pathways for Australian marsupials. With biobanking and assisted reproduction, inbreeding was reduced on average by between 80% and 98%, colony sizes were on average 99% smaller, and program costs were 69- to 83-fold lower. Integrating biobanking made long-standing captive genetic retention targets possible in marsupials (90% source population heterozygosity for a minimum of 100 years) within realistic cost frameworks. Lessons from the use of biobanking technology that contributed to the recovery of the black-footed ferret include the importance of adequate research funding (US$4.2 million), extensive partnerships that provide access to facilities and equipment, colony animals, appropriate research model species, and professional and technical staff required to address knowledge gaps to deliver an optimized biobanking protocol. Applied research investment of A$133 million across marsupial research pathways could deliver biobanking protocols for 15 of Australia's most at-risk marsupial species and 7 model species. The technical expertise and ex situ facilities exist to emulate the success of the black-footed ferret recovery program in threatened marsupials using these research pathways. All that is needed now for significant and cost-effective conservation gains is greater investment by policy makers in marsupial ARTs.
Collective Intelligence Using 5G: Concepts, Applications, and Challenges in Sociotechnical Environments

Distributed intelligence is a well-known approach for optimizing interactions among numerous smart devices that interconnect and operate together as Internet of Things (IoT) systems. A modern form of human-machine collective intelligence emerges when humans interact with IoT systems in sociotechnical environments such as smart homes. Fifth-generation (5G) communication networks are designed for high-speed reliable wireless connectivity and expected to boost IoT and (distributed) collective intelligence by revolutionizing human-device-human interactions. In this paper, we contribute a comprehensive review of state-of-the-art sociotechnical environments that exhibit collective intelligence, supported by 5G-enabled IoT. We discuss the latest developments in 5G and their implications for collective intelligence. Further, we explain the key challenges for using 5G to support collective intelligence, e.g., data processing, security, and radio resource management. Finally, we describe four practical applications of collective intelligence to sociotechnical environments-road traffic control, unmanned aerial vehicles, electrical load demand response, and augmented democracy.
How to Represent Paintings: A Painting Classification Using Artistic Comments

The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then train an art graph convolutional network (ArtGCN) on the entire corpus. The nodes, which include the words and documents in the topological graph are initialized using a one-hot representation; then, the embeddings are learned jointly for both words and documents, supervised by the known-class training labels of the paintings. Through extensive experiments on different classification tasks using different input sources, we demonstrate that the proposed methods achieve state-of-art performance. In addition, ArtGCN can learn word and painting embeddings, and we find that they have a major role in describing the labels and retrieval paintings, respectively.
Phased arrays - Part II: Implementations, applications, and future trends

In Part I of this paper, we presented the basic architectures and theory for passive and active phased arrays. Here, we review array implementation, state-of-the-art applications, and identify future trends in phased-array technology.
Identification and validation of potential genotoxic impurities, 1,3-dichloro-2-propanol, and 2,3-dichloro-1-propanol, at subtle levels in a bile acid sequestrant, colesevelam hydrochloride, using hyphenated GC-MS technique

Potential genotoxic impurities (PGI) and N-nitrosamine impurities in active pharmaceutical ingredients (APIs) and their determination at low levels are substantial challenges for cholesterol-lowering agents in recent years. Herein we developed a robust, reliable, rapid, accurate and validated technique of gas chromatography equipped with a mass spectrometer (GC-MS) for quantifying subtle levels of 1,3-dichloro-2-propanol (PGI-I) and 2,3-dichloro-1-propanol (PGI-II) in colesevelam hydrochloride drug substance (bile acid sequestrant). The separation of colesevelam hydrochloride, PGI-I and PGI-II was executed with chromatographic technique using a capillary column, DB-624 measuring with 30 m × 0.32 mm × 1.8 μm specification of 6% cyanopropylphenyl-94% dimethylpolysiloxane copolymer and helium carrier gas. This developed technique gave a good intensity peak without any interference and extra masses at the retention times of 11.17 min for PGI-I and 11.59 min for PGI-II, which was adequate, with mass spectra (m/z) of 79 and 62, respectively. The method's sensitivity and linearity are demonstrated by its detection and quantification limits at subtle levels with correlation coefficients of 0.9965 for PGI-I and 0.9910 for PGI-II. The determination is mainly focused on improving sensitivity with the limits of detection and quantitation far below the specifications, which can support tighter limits. This results in a cost-effective and easily adoptable methodology having precise and accurate results in colesevelam hydrochloride API at subtle levels.
Multilayer X-ray optics

The principles, state of the art, and problems of multilayer X-ray optics are analysed. Among its applications, the projection X-ray lithography and mirrors for a repetitively pulsed capillary-discharge X-ray laser are considered.
Phospholipase Dε enhances Braasca napus growth and seed production in response to nitrogen availability

Phospholipase D (PLD), which hydrolyses phospholipids to produce phosphatidic acid, has been implicated in plant response to macronutrient availability in Arabidopsis. This study investigated the effect of increased PLDε expression on nitrogen utilization in Brassica napus to explore the application of PLDε manipulation to crop improvement. In addition, changes in membrane lipid species in response to nitrogen availability were determined in the oil seed crop. Multiple PLDε over expression (PLDε-OE) lines displayed enhanced biomass accumulation under nitrogen-deficient and nitrogen-replete conditions. PLDε-OE plants in the field produced more seeds than wild-type plants but have no impact on seed oil content. Compared with wild-type plants, PLDε-OE plants were enhanced in nitrate transporter expression, uptake and reduction, whereas the activity of nitrite reductase was higher under nitrogen-depleted, but not at nitrogen-replete conditions. The level of nitrogen altered membrane glycerolipid metabolism, with greater impacts on young than mature leaves. The data indicate increased expression of PLDε has the potential to improve crop plant growth and production under nitrogen-depleted and nitrogen-replete conditions.
Effects of multi-kinase inhibitors on the activity of cytochrome P450 2J2

1. Cytochrome P450 2J2 (CYP2J2) shows high expression in extrahepatic tissues, including the heart and kidney and in tumours. Inhibition of CYP2J2 has attracted attention for cancer treatment because it metabolises arachidonic acid (AA) to epoxyeicosatrienoic acid (EET), which inhibits apoptosis and promotes tumour growth. Multi-kinase inhibitor (MKI) is a molecular-targeted drug with antitumor activities. This study aimed to clarify the inhibitory effects of MKIs on CYP2J2 activity. We also investigated whether MKIs affected CYP2J2-catalysed EET formation from AA.2. Twenty MKIs showed different inhibitory potencies against astemizole O-demethylation in CYP2J2. In particular, apatinib, motesanib, and vatalanib strongly inhibited astemizole O-demethylation. These three MKIs exhibited competitive inhibition with inhibition constant (Ki) values of 9.3, 15.4, and 65.0 nM, respectively. Apatinib, motesanib, and vatalanib also inhibited CYP2J2-catalysed 14,15-EET formation from AA.3. In simulations of docking to CYP2J2, the U energy values of apatinib, motesanib, and vatalanib were low, and measured -84.5, -69.9, and -52.3 kcal/mol, respectively.4. In conclusion, apatinib, motesanib, and vatalanib strongly inhibited CYP2J2 activity, suggesting that the effects of a given CYP2J2 substrate may be altered upon the administration of these MKIs.
PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network

The autonomous car must recognize the driving environment quickly for safe driving. As the Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast semantic segmentation of LiDAR point cloud, which is the point-wise classification of the point cloud within the sensor framerate, has attracted attention in recognition of the driving environment. Although the voxel and fusion-based semantic segmentation models are the state-of-the-art model in point cloud semantic segmentation recently, their real-time performance suffer from high computational load due to high voxel resolution. In this paper, we propose the fast voxel-based semantic segmentation model using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model is designed to outperform at both high and low voxel resolution using point convolution-based feature extraction. Moreover, the proposed model accelerates the feature propagation using 3D sparse convolution after the feature extraction. The experimental results demonstrate that the proposed model outperforms the state-of-the-art real-time models in semantic segmentation of SemanticKITTI and