Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography
Abstract: The auscultation of heart sounds has been for decades a fundamental diagnostic tool in clinical practice. Higher effectiveness can be achieved by recording the corresponding biomedical signal, namely the phonocardiographic signal, and processing it by means of traditional signal processing techniques. An unavoidable processing step is the heart sound segmentation, which is still a challenging task from a technical viewpoint—a limitation of state-of-the-art approaches is the unavailability of trustworthy techniques for the detection of heart sound components. The aim of this work is to design a reliable algorithm for the identification and the classification of heart sounds’ main components. The proposed methodology was tested on a sample population of 24 healthy subjects over 10-min-long simultaneous electrocardiographic and phonocardiographic recordings and it was found capable of correctly detecting and classifying an average of 99.2% of the heart sounds along with their components. Moreover, the delay of each component with respect to the corresponding R-wave peak and the delay among the components of the same heart sound were computed: the resulting experimental values are coherent with what is expected from the literature and what was obtained by other studies.
Extracted statement — Accuracy: 99.2
Research field: Atomic, Molecular and Optical Physics
Title: Ultraviolet/vacuum-ultraviolet emission from a high power magnetron sputtering plasma with an aluminum target
Abstract: We report the in situ measurement of the ultraviolet/vacuum-ultraviolet (UV/VUV) emission from a plasma produced by high power impulse magnetron sputtering with aluminum target, using argon as background gas. The UV/VUV detection system is based upon the quantification of the re-emitted fluorescence from a sodium salicylate layer that is placed in a housing inside the vacuum chamber, at 11 cm from the center of the cathode. The detector is equipped with filters that allow for differentiating various spectral regions, and with a front collimating tube that provides a spatial resolution ≈ 0.5 cm. Using various views of the plasma, the measured absolutely calibrated photon rates enable to calculate emissivities and irradiances based on a model of the ionization region. We present results that demonstrate that Al++ ions are responsible for most of the VUV irradiance. We also discuss the photoelectric emission due to irradiances on the target ~ 2×1018 s-1 cm-2 produced by high energy photons from resonance lines of Ar+.
Extracted statement — Pressure: 0.5
Research field: Bioinformatics
Title: Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks
Abstract: Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.
Extracted statement — AUC: 0.941
Research field: Bioinformatics
Title: Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks
Abstract: Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.
Extracted statement — Classifier accuracy: 86.2
Research field: Bioinformatics
Title: Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network
Abstract: Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms.
Extracted statement — Classifier accuracy: 88.25
Research field: Bioinformatics
Title: Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks
Abstract: Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.
Extracted statement — F1-score: 88.6
Research field: Coating and Surface Technology
Title: Clay-Based Nanocomposite Coating for Flexible Optoelectronics Applying Commercial Polymers
Abstract: Transparency, flexibility, and especially ultralow oxygen (OTR) and water vapor (WVTR) transmission rates are the key issues to be addressed for packaging of flexible organic photovoltaics and organic light-emitting diodes. Concomitant optimization of all essential features is still a big challenge. Here we present a thin (1.5 μm), highly transparent, and at the same time flexible nanocomposite coating with an exceptionally low OTR and WVTR (1.0 × 10(-2) cm(3) m(-2) day(-1) bar(-1) and <0.05 g m(-2) day(-1) at 50% RH, respectively). A commercially available polyurethane (Desmodur N 3600 and Desmophen 670 BA, Bayer MaterialScience AG) was filled with a delaminated synthetic layered silicate exhibiting huge aspect ratios of about 25,000. Functional films were prepared by simple doctor-blading a suspension of the matrix and the organophilized clay. This preparation procedure is technically benign, is easy to scale up, and may readily be applied for encapsulation of sensitive flexible electronics.
Extracted statement — Coating thickness, micrometers: 1.5
Research field: Earth Sciences
Title: Classification of Iowa wetlands using an airborne hyperspectral image: a comparison of the spectral angle mapper classifier and an object-oriented approach
Abstract: Wetlands mapping using multispectral imagery from Landsat multispectral scanner (MSS) and thematic mapper (TM) and Système pour l'observation de la Terre (SPOT) does not in general provide high classification accuracies because of poor spectral and spatial resolutions. This study tests the feasibility of using high-resolution hyperspectral imagery to map wetlands in Iowa with two nontraditional classification techniques: the spectral angle mapper (SAM) method and a new nonparametric object-oriented (OO) classification. The software programs used were ENVI and eCognition. Accuracies of these classified images were assessed by using the information collected through a field survey with a global positioning system and high-resolution color infrared images. Wetlands were identified more accurately with the OO method (overall accuracy 92.3%) than with SAM (63.53%). This paper also discusses the limitations of these classification techniques for wetlands, as well as discussing future directions for study.
Extracted statement — SAM Accuracy (%): 63.53
Research field: Earth Sciences
Title: Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image
Abstract: Hyperspectral Imaging (HSI) is used to provide a wealth of information which can be used to address a variety of problems in different applications. The main requirement in all applications is the classification of HSI data. In this paper, supervised HSI classification algorithms are used to extract agriculture areas that specialize in wheat growing and get a classified image. In particular, Parallelepiped and Spectral Angel Mapper (SAM) algorithms are used. They are implemented by a software tool used to analyse and process geospatial images that is an Environment of Visualizing Images (ENVI). They are applied on Al-Kharj, Saudi Arabia as the study area. The overall accuracy after applying the algorithms on the image of the study area for SAM classification was 66.67%, and 33.33% for Parallelepiped classification. Therefore, SAM algorithm has provided a better a study area image classification.
Extracted statement — SAM Accuracy (%): 66.67
Research field: Earth Sciences
Title: Raman spectroscopy of the borosilicate mineral ferroaxinite
Abstract: Raman spectroscopy, complemented by infrared spectroscopy has been used to characterise the ferroaxinite minerals of theoretical formula Ca2Fe2+Al2BSi4O15(OH), a ferrous aluminium borosilicate. The Raman spectra are complex but are subdivided into sections based upon the vibrating units. The Raman spectra are interpreted in terms of the addition of borate and silicate spectra. Three characteristic bands of ferroaxinite are observed at 1082, 1056 and 1025 cm-1 and are attributed to BO4 stretching vibrations. Bands at 1003, 991, 980 and 963 cm-1 are assigned to SiO4 stretching vibrations. Bands are found in these positions for each of the ferroaxinites studied. No Raman bands were found above 1100 cm-1 showing that ferroaxinites contain only tetrahedral boron. The hydroxyl stretching region of ferroaxinites is characterised by a single Raman band between 3368 and 3376 cm-1, the position of which is sample dependent. Bands for ferroaxinite at 678, 643, 618, 609, 588, 572, 546 cm-1 may be attributed to the ν4 bending modes and the three bands at 484, 444 and 428 cm-1 may be attributed to the ν2 bending modes of the (SiO4)2-.
Extracted statement — ferroaxinite (OH): 3376
Research field: Earth Sciences
Title: Raman spectroscopy of the joaquinite minerals
Abstract: Selected joaquinite minerals have been studied by Raman spectroscopy. The minerals are categorised into two groups depending upon whether bands occur in the 3250 to 3450 cm−1 region and in the 3450 to 3600 cm−1 region, or in the latter region only. The first set of bands is attributed to water stretching vibrations and the second set to OH stretching bands. In the literature, X-ray diffraction could not identify the presence of OH units in the structure of joaquinite. Raman spectroscopy proves that the joaquinite mineral group contains OH units in their structure, and in some cases both water and OH units. A series of bands at 1123, 1062, 1031, 971, 912 and 892 cm−1 are assigned to SiO stretching vibrations. Bands above 1000 cm−1 are attributable to the νas modes of the (SiO4)4− and (Si2O7)6− units. Bands that are observed at 738, around 700, 682 and around 668, 621 and 602 cm−1 are attributed to OSiO bending modes. The patterns do not appear to match the published infrared spectral patterns of either (SiO4)4− or (Si2O7)6− units. The reason is attributed to the actual formulation of the joaquinite mineral, in which significant amounts of Ti or Nb and Fe are found. Copyright © 2007 John Wiley & Sons, Ltd.
Extracted statement — Joaquinite (OH): 3600
Research field: Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Title: Identifying Canadian mosquito species through DNA barcodes
Abstract: Abstract A short fragment of mt DNA from the cytochrome c oxidase 1 (CO1) region was used to provide the first CO1 barcodes for 37 species of Canadian mosquitoes (Diptera: Culicidae) from the provinces Ontario and New Brunswick. Sequence variation was analysed in a 617‐bp fragment from the 5′ end of the CO1 region. Sequences of each mosquito species formed barcode clusters with tight cohesion that were usually clearly distinct from those of allied species. CO1 sequence divergences were, on average, nearly 20 times higher for congeneric species than for members of a species; divergences between congeneric species averaged 10.4% (range 0.2–17.2%), whereas those for conspecific individuals averaged 0.5% (range 0.0–3.9%).
Extracted statement — nearest neighbor distance lower limit: 0.2
Research field: Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Title: Evaluation of DNA barcoding and identification of new haplomorphs in Canadian deerflies and horseflies
Abstract: This paper reports the first tests of the suitability of the standardized mitochondrial cytochrome c oxidase subunit I (COI) barcoding system for the identification of Canadian deerflies and horseflies. Two additional mitochondrial molecular markers were used to determine whether unambiguous species recognition in tabanids can be achieved. Our 332 Canadian tabanid samples yielded 650 sequences from five genera and 42 species. Standard COI barcodes demonstrated a strong A + T bias (mean 68.1%), especially at third codon positions (mean 93.0%). Our preliminary test of this system showed that the standard COI barcode worked well for Canadian Tabanidae: the target DNA can be easily recovered from small amounts of insect tissue and aligned for all tabanid taxa. Each tabanid species possessed distinctive sets of COI haplotypes which discriminated well among species. Average conspecific Kimura two‐parameter (K2P) divergence (0.49%) was 12 times lower than the average divergence within species. Both the neighbour‐joining and the Bayesian methods produced trees with identical monophyletic species groups. Two species, Chrysops dawsoni Philip and Chrysops montanus Osten Sacken (Diptera: Tabanidae), showed relatively deep intraspecific sequence divergences (∼10 times the average) for all three mitochondrial gene regions analysed. We suggest provisional differentiation of Ch. montanus into two haplotypes, namely, Ch. montanus haplomorph 1 and Ch. montanus haplomorph 2, both defined by their molecular sequences and by newly discovered differences in structural features near their ocelli.
Extracted statement — Maximum intraspecific distances averaged: 0.49
Research field: Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Title: Identifying Canadian mosquito species through DNA barcodes
Abstract: Abstract A short fragment of mt DNA from the cytochrome c oxidase 1 (CO1) region was used to provide the first CO1 barcodes for 37 species of Canadian mosquitoes (Diptera: Culicidae) from the provinces Ontario and New Brunswick. Sequence variation was analysed in a 617‐bp fragment from the 5′ end of the CO1 region. Sequences of each mosquito species formed barcode clusters with tight cohesion that were usually clearly distinct from those of allied species. CO1 sequence divergences were, on average, nearly 20 times higher for congeneric species than for members of a species; divergences between congeneric species averaged 10.4% (range 0.2–17.2%), whereas those for conspecific individuals averaged 0.5% (range 0.0–3.9%).
Extracted statement — Maximum intraspecific distances averaged: 0.5
Research field: Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Title: DNA barcodes reveal cryptic genetic diversity within the blackfly subgenus Trichodagmia Enderlein (Diptera: Simuliidae: Simulium) and related taxa in the New World
Abstract: <jats:p>In this paper we investigate the utility of the COI DNA barcoding region for species identification and for revealing hidden diversity within the subgenus Trichodagmia and related taxa in the New World. In total, 24 morphospecies within the current expanded taxonomic concept of Trichodagmia were analyzed. Three species in the subgenus Aspathia and 10 species in the subgenus Simulium s.str. were also included in the analysis because of their putative phylogenetic relationship with Trichodagmia. In the Neighbour Joining analysis tree (NJ) derived from the DNA barcodes most of the specimens grouped together according to species or species groups as recognized by other morphotaxonomic studies. The interspecific genetic divergence averaged 11.2% (range 2.8–19.5%), whereas intraspecific genetic divergence within morphologically distinct species averaged 0.5% (range 0–1.2%). Higher values of genetic divergence (3.2–3.7%) in species complexes suggest the presence of cryptic diversity. The existence of well defined groups within S. piperi, S. duodenicornium, S. canadense and S. rostratum indicate the possible presence of cryptic species within these taxa. Also, the suspected presence of a sibling species in S. tarsatum and S. paynei is supported. DNA barcodes also showed that specimens from species that were taxonomically difficult to delimit such as S. hippovorum, S. rubrithorax, S. paynei, and other related taxa (S. solarii), grouped together in the NJ analysis, confirming the validity of their species status. The recovery of partial barcodes from specimens in collections was time consuming and PCR success was low from specimens more than 10 years old. However, when a sequence was obtained, it provided good resolution for species identification. Larvae preserved in ‘weak’ Carnoy’s solution (9:1 ethanol:acetic acid) provided full DNA barcodes. Adding legs directly to the PCR mix from recently collected and preserved adults was an inexpensive, fast methodology to obtain full barcodes. In summary, DNA barcoding combined with a sound morphotaxonomic framework provides an effective approach for the delineation of species and for the discovery of hidden diversity in the subgenus Trichodagmia.</jats:p>
Extracted statement — Maximum intraspecific distances averaged: 0.5
Research field: Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Title: Barcoding the butterflies of southern South America: Species delimitation efficacy, cryptic diversity and geographic patterns of divergence
Abstract: Because the tropical regions of America harbor the highest concentration of butterfly species, its fauna has attracted considerable attention. Much less is known about the butterflies of southern South America, particularly Argentina, where over 1,200 species occur. To advance understanding of this fauna, we assembled a DNA barcode reference library for 417 butterfly species of Argentina, focusing on the Atlantic Forest, a biodiversity hotspot. We tested the efficacy of this library for specimen identification, used it to assess the frequency of cryptic species, and examined geographic patterns of genetic variation, making this study the first large-scale genetic assessment of the butterflies of southern South America. The average sequence divergence to the nearest neighbor (i.e. minimum interspecific distance) was 6.91%, ten times larger than the mean distance to the furthest conspecific (0.69%), with a clear barcode gap present in all but four of the species represented by two or more specimens. As a consequence, the DNA barcode library was extremely effective in the discrimination of these species, allowing a correct identification in more than 95% of the cases. Singletons (i.e. species represented by a single sequence) were also distinguishable in the gene trees since they all had unique DNA barcodes, divergent from those of the closest non-conspecific. The clustering algorithms implemented recognized from 416 to 444 barcode clusters, suggesting that the actual diversity of butterflies in Argentina is 3%–9% higher than currently recognized. Furthermore, our survey added three new records of butterflies for the country (Eurema agave, Mithras hannelore, Melanis hillapana). In summary, this study not only supported the utility of DNA barcoding for the identification of the butterfly species of Argentina, but also highlighted several cases of both deep intraspecific and shallow interspecific divergence that should be studied in more detail.
Extracted statement — Maximum intraspecific distances averaged: 0.69