ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Dating Iberian prehistoric rock art: Methods, sampling, data, limits and interpretations

Rock art dating has been one of the major challenges since its discovery and recognition. The methods have evolved through the last century, beginning with the study of superpositions and style until to the application of numeric methods since the 1990s. The aim of this paper is to evaluate and publish an up-to-date database of all of the numerical dates currently available for Iberian prehistoric rock art sites. For this purpose, the manuscript reviews all the methods applied so far to Iberian rock art discussing the limits, the sampling involved, and the problems affecting the results. After that, we present and discuss the most relevant results related to each cultural graphic tradition (Palaeolithic, Levantine, Schematic and Megalithic rock art) assessing their value and limitations. Finally, we reflect on the future of rock art dating: unfortunately most of the motifs are not dateble in numeric terms, meaning we still have to combine traditional with numerical methods; but also, we need to keep working on the problems affecting these methods to be able to create a more reliable chronological framework of use to address other issues such as group mobility, cultural networks, and reutilisation of symbolic elements, to name a few.
Automatic portrait oil painter: joint domain stylization for portrait images

Everyone has the dream of being in the center of famous art paintings, admired by numerous future generations. However, the dream came true at a huge cost of the painter's commission in old days. In our paper, another practical choice is provided for everyone to achieve that dream - an automatic portrait oil painter transferring some artistic styles from one single reference painting. To address this issue, we propose a joint-domain image stylization approach, particularly for portrait oil paintings. From the view of artistic appreciation, we analyze an amount of oil painting art works and summarize three critical factors to depict the figure, i.e. color, structure and texture. Based on this point, we separate and represent an artistic work into these three domains. Then, considering their intrinsic properties and following an art creation route, we propose the corresponding approaches to jointly model and transfer the features in these domains. First, a swatch-based color adjustment is proposed to recolor the tone of the input image based on semantic regions corresponding to the references. Second, the main structures of the input image is maintained by sparse reconstruction. Third, a coarse-to-fine texture synthesis is used to enhance the detail oil painting patterns. Extensive experimental results demonstrate that the proposed method achieves desirable results compared with state-of-the-art methods in not only transferring the styles from references but also keeping consistent contents with the given portrait.
Machine learning in lung transplantation: Where are we?

Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.
Oxidative stress and DNA damage in earthworms induced by methyl tertiary-butyl ether in natural soils

Adverse effects of methyl tertiary-butyl ether (MTBE) have been noticed at different trophic levels by international researchers. However, there was unclear evidence about its effects on oxidative stress and DNA damage in earthworms. In this study, earthworms were cultivated in various doses of MTBE (0.0 mg/kg, 10.0 mg/kg, 30.0 mg/kg, and 60.0 mg/kg) contaminated agricultural soil for 7 days, 14 days, 21 days, and 28 days, respectively. The result showed that the reactive oxygen species (ROS) content of earthworms significantly increased in MTBE treatment groups compared to the control group. In MTBE treatment groups, the activities of superoxide dismutase, catalase, peroxidase, and glutathione S-transferase were significantly activated at the exposure of 7 days, which increased by 36.3-78.9%, 51.8-97.3%, 36.5-61.9%, and 12.0-54.8%, respectively. Then, the activities of these defense enzymes showed various changes following the changes in exposure times and MTBE concentrations. Especially in the 60.0 mg kg-1 group, both antioxidant enzymes and GST were still significantly activated at the exposure of 14 days and then significantly inhibited at the exposure of 28 days. The analysis of olive tail moment showed significant DNA damage in the 10.0 mg kg-1 group at the exposure of 28 days, and this damage in 30.0 mg/kg and 60.0 mg/kg groups was found at the exposure of 7 days. This result was consistent with the malondialdehyde accumulation in earthworms. Additionally, the analysis of IBRv2 showed the effects of MTBE treatments on earthworms in dose- and time-dependent manners. This study helps better to understand the effects of MTBE on soil invertebrate animals and provide theoretical support for soil protection in governing MTBE application.
Comparative analysis of building insulation material properties and performance

Building envelope insulation is crucial for an energy-efficient and comfortable indoor environment because the envelope accounts for 50-60% of total heat gain/loss in a building. Previous studies mostly used lifecycle cost as the criteria to select the optimum insulation materials with little or no consideration of embodied energy, emission, and summer overheating potential. This study presents a comparative analysis of building insulation materials properties (thermal, hygroscopic, acoustic, reaction to fire, environmental, and cost) and their performance in different climate zones and proposed an optimization framework. Insulation materials can be primarily categorized as conventional, state-of-the-art and sustainable. State-of-the-art insulation materials have the lowest thermal conductivity value amongst the three insulation types. However, their life cycle cost is higher compared to the other types. Sustainable insulation materials could be useful to delay and minimize indoor peak temperature and reduce overheating risk during the hot summer period. The analysis also showed that building walls with comparatively lower thermal resistance are more cost-effective for the cooling dominated region, but walls with higher thermal resistance are more cost-effective in heating-dominated regions. However, highly insulated and airtight houses may also lead to increased overheating risk and peak cooling demand during a hot summer period. In addition, hygroscopic, acoustic, and fire retardancy properties of insulation materials are critical to control indoor relative humidity in a humid region, to maintain a minimum noise level in a zone, and to reduce fire destruction. Hence, the optimization should include four criteria 1) Energy, 2) Environment, 3) Economic, and 4) Comfort.
Prenatal evaluation of atelencephaly

Atelencephaly is a rare lethal congenital brain malformation characterized by underdevelopment of the prosencephalon and is often accompanied by the facial features seen in some cases of holoprosencephaly, such as cyclopia. We report a case of atelencephaly in the fetus with characteristic ultrasound findings. In addition, we report the findings on fetal MRI, which have not been previously described in the literature.
Gangliosides in Neurodegenerative Diseases

The main purpose of this chapter is to summarize the chief findings on ganglioside changes/interactions with some of the neurodegenerative disorders. For the latter we have focused on three diseases that have seen especially intensive study in that regard: Parkinson's, Alzheimer's, and Huntington's diseases. Parkinson's disease (PD) has received the most intensive study with revelation of systemic deficiency of GM1 in brain and all peripheral tissues that have been analyzed to date; this pointed to GM1 replacement as a promising therapy which proved only partially successful when tried for reasons that are discussed. Huntington's disease resembles PD in also manifesting GM1 deficiency, which did, however, respond to GM1 replacement therapy - apparently due to GM1 being administered directly into the brain. Alzheimer's disease was more complex in relation to gangliosides, with b-series (GD1b, GT1b) apparently depressed along with a-series. GM1 administered in brain appeared to induce improvement, but in a limited number of patients. We summarize studies showing why GM1 is of critical importance in neuronal function, and we also briefly point to a few additional neurological disorders in which one or more ganglioside changes have been implicated.
Study on categorization of factors affecting smallholder dairy production in Siltie Zone, Southern Ethiopia, applying multivariate analysis approaches

This study aims to categorize smallholder dairy farming systems through multivariate analysis. Nonlinear categorical principal component analysis (NLPCA) was used to lessen 35 variables into 4 sets of uncorrelated components. These four categories are environment-genetic interaction, management, hygiene, and genetic-related factors. Besides, within the two-step cluster analysis, a variable cluster membership was created that identified which family belonged to which cluster. For comparison purpose, hierarchical cluster analysis was used. A two-step cluster analysis results showed that most farms (41.50% and 31.90% in peri-urban and urban areas, respectively, had been in cluster 1 at the same time as most farms (66.70%) of urban areas had been in cluster 2. Overall, most (31.00% and 34.00%) of farms have been in clusters 1 and 2, respectively. Most farms in peri-urban areas had been challenged with scarcity of feed, mastitis, and animal sickness than farms within the urban and rural farming systems. Forming farmer groups as a cooperative to supply offerings together with feed processing devices, artificial insemination, and health services is usually recommended to triumph over such hassle. In addition, on account that it is primarily based on a couple of criteria, the category system evolved in this study depicts a lot better dairy farming systems with admiration to the variety of the components and the relative contribution of each component to dairy farming than do the single-criterion classifications. Hence, the results of such classifications should be seen as a start line from which to efficiently compare the modern extension system and eventually design the high-quality-fit extension models for a heterogeneous populace of smallholder dairy farmers.
Appendiceal schwannoma - report of a case and literature review

Appendiceal tumours encompass a wide spectrum of differential diagnoses and frequently present with clinical features of appendicitis. We report the case of a 43-year-old woman who presented with epigastric pain, dyspepsia and bloating. An atypical right para-iliac mass was detected on abdominal ultrasound, and computed tomography (CT) identified an appendiceal tumour. The tumour subtype remained indeterminate following Gallium-68 Dotatate positron emission tomography (PET); however, an appendiceal neuroendocrine tumour was suspected. Surgical resection with laparoscopic en bloc appendicectomy and limited caecectomy was performed, and histopathological assessment confirmed an appendiceal schwannoma. The report is followed by a review of the literature. To our knowledge, there have been fourteen reported cases of appendiceal schwannoma. The preoperative diagnosis can be challenging and appendiceal schwannoma had not been suspected in any of the reported cases, while a suspected diagnosis of neuroendocrine tumour or gastrointestinal stromal tumour was common. Definitive diagnosis requires immunohistochemical assessment and S100 is the hallmark. No personal or family history of underlying neurofibromatosis (NF) type 1 or type 2 has been reported to date. As for other gastrointestinal schwannomas, complete surgical resection is the recommended treatment for appendiceal schwannoma. Following this, despite lack of long-term follow-up, no cases of recurrence have been reported thus far.
Cobalt Cyclopentadienyl-Phosphine Dinitrogen Complexes

By applying the potassium salts of cyclopentadienyl-phosphine ligands LK to CoCl2 , the corresponding cobalt chlorides (1, LCoII Cl) were prepared. By reducing complexes 1 with KHBEt3 under a N2 atmosphere, bridging end-on complexes, LCoI -N2 -CoI L (2 a and 2 b), were successfully obtained. 15 N2 -labeled [15 N2 ]-2 a was prepared under 15 N2 /14 N2 exchange in THF solution. LCoI -N2 -CoI L complex 2 a could react with P4 molecules to release N2 and generate a Co-P4 -Co moiety 4. Further reduction of complex 2 b led to cleavage of a P-C bond in the cyclopentadienyl-phosphine ligand to provide novel μ-PCy2 -bridged Co0 -N2 complex 5. DFT calculations confirmed the experimental observations.
Reducing the question burden of patient reported outcome measures using Bayesian networks

Patient Reported Outcome Measures (PROMs) are questionnaires completed by patients about aspects of their health status. They are a vital part of learning health systems as they are the primary source of information about important outcomes that are best assessed by patients such as pain, disability, anxiety and depression. The volume of questions can easily become burdensome. Previous techniques reduced this burden by dynamically selecting questions from question item banks which are specifically built for different latent constructs being measured. These techniques analyzed the information function between each question in the item bank and the measured construct based on item response theory then used this information function to dynamically select questions by computerized adaptive testing. Here we extend those ideas by using Bayesian Networks (BNs) to enable Computerized Adaptive Testing (CAT) for efficient and accurate question selection on widely-used existing PROMs. BNs offer more comprehensive probabilistic models of the connections between different PROM questions, allowing the use of information theoretic techniques to select the most informative questions. We tested our methods using five clinical PROM datasets, demonstrating that answering a small subset of questions selected with CAT has similar predictions and error to answering all questions in the PROM BN. Our results show that answering 30% - 75% questions selected with CAT had an average area under the receiver operating characteristic curve (AUC) of 0.92 (min: 0.8 - max: 0.98) for predicting the measured constructs. BNs outperformed alternative CAT approaches with a 5% (min: 0.01% - max: 9%) average increase in the accuracy of predicting the responses to unanswered question items.
Large-scale document image retrieval and classification with runlength histograms and binary embeddings

We present a new document image descriptor based on multi-scale runlength histograms. This descriptor does not rely on layout analysis and can be computed efficiently. We show how this descriptor can achieve state-of-the-art results on two very different public datasets in classification and retrieval tasks. Moreover, we show how we can compress and binarize these descriptors to make them suitable for large-scale applications. We can achieve state-of-the-art results in classification using binary descriptors of as few as 16-64 bits. (C) 2012 Elsevier Ltd. All rights reserved.
Large-Scale Malicious Software Classification With Fuzzified Features and Boosted Fuzzy Random Forest

Classification of malicious software, especially in a very large dataset, is a challenging task for machine intelligence. Malware can have highly diversified features, each of which has highly heterogeneous distributions. These factors increase the difficulties for traditional data analytic approaches to deal with them. Although deep learning based methods have reported good classification performance, the deep models usually lack interpretability and are fragile under adversarial attacks. To solve these problems, fuzzy systems have become a competitive candidate in malware analysis. In this article, a new fuzzy-based approach is proposed for malware classification. We focused on portable executable files in the Windows platform and analyzed the distributions of static features and content-oriented features. Fuzzification was used to reduce the ubiquitous impact of noise and outliers in a very large dataset. Finally, a novel boosted classifier consisted of fuzzy decision trees and support vector machine is proposed to perform the malware classification. By using fuzzy decision trees, the inner structure of the classifier can be readily interpreted as discriminative rules, whereas the novel boosting strategy provides state-of-the-art classification performance. Extensive experimental results showed that our method significantly outperformed several state-of-the-art classifiers.
Attention Residual Learning for Skin Lesion Classification

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-class variation, and the lack of the ability to focus on semantically meaningful lesion parts. To address these issues, we propose an attention residual learning convolutional neural network (ARL-CNN) model for skin lesion classification in dermoscopy images, which is composed of multiple ARL blocks, a global average pooling layer, and a classification layer. Each ARL block jointly uses the residual learning and a novel attention learning mechanisms to improve its ability for discriminative representation. Instead of using extra learnable layers, the proposed attention learning mechanism aims to exploit the intrinsic self-attention ability of DCNNs, i.e., using the feature maps learned by a high layer to generate the attention map for a low layer. We evaluated our ARL-CNN model on the ISIC-skin 2017 dataset. Our results indicate that the proposed ARL-CNN model can adaptively focus on the discriminative parts of skin lesions, and thus achieve the state-of-the-art performance in skin lesion classification.
Local Coordinate Concept Factorization for Image Representation

Learning sparse representation of high-dimensional data is a state-of-the-art method for modeling data. Matrix factorization-based techniques, such as nonnegative matrix factorization and concept factorization (CF), have shown great advantages in this area, especially useful for image representation. Both of them are linear learning problems and lead to a sparse representation of the images. However, the sparsity obtained by these methods does not always satisfy locality conditions. For example, the learned new basis vectors may be relatively far away from the original data. Thus, we may not be able to achieve the optimal performance when using the new representation for other learning tasks, such as classification and clustering. In this paper, we introduce a locality constraint into the traditional CF. By requiring the concepts (basis vectors) to be as close to the original data points as possible, each datum can be represented by a linear combination of only a few basis concepts. Thus, our method is able to achieve sparsity and locality simultaneously. We analyze the complexity of our novel algorithm and demonstrate the effectiveness in comparison with the state-of-the-art approaches through a set of evaluations based on real-world applications.
Through the Looking Glass: The Paradoxical Evolution of Targeted Temperature Management for Comatose Survivors of Cardiac Arrest

For the past two decades, targeted temperature management (TTM) has been a staple in the care of comatose survivors following cardiac arrest. However, recent clinical trials have failed to replicate the benefit seen in earlier studies, bringing into question the very existence of such clinical practice. In this review, we explore clinical scenarios within critical care that appeared to share a similar fate, but in actuality changed the landscape of practice in a modern world. Accordingly, clinicians may apply these lessons to the utilization of TTM among comatose survivors following cardiac arrest, potentially paving way for a re-framing of clinical care amidst an environment where current data appears upside down in comparison to past successes.
Sensory neural hearing loss in Behcet's disease successfully controlled with infliximab, case report and review of literature

Behcet's disease is a systemic autoimmune disorder occasionally associated with otological manifestations, including sensorineural hearing loss. We are reporting a case of Behcet's disease, which was complicated by sensorineural hearing loss and managed successfully with anti-TNF agent Infliximab.
Footedness for scratching itchy eyes in rodents

The neural bases of itchy eye transmission remain unclear compared with those involved in body itch. Here, we show in rodents that the gastrin-releasing peptide receptor (GRPR) of the trigeminal sensory system is involved in the transmission of itchy eyes. Interestingly, we further demonstrate a difference in scratching behaviour between the left and right hindfeet in rodents; histamine instillation into the conjunctival sac of both eyes revealed right-foot biased laterality in the scratching movements. Unilateral histamine instillation specifically induced neural activation in the ipsilateral sensory pathway, with no significant difference between the activations following left- and right-eye instillations. Thus, the behavioural laterality is presumably due to right-foot preference in rodents. Genetically modified rats with specific depletion of Grpr-expressing neurons in the trigeminal sensory nucleus caudalis of the medulla oblongata exhibited fewer and shorter histamine-induced scratching movements than controls and eliminated the footedness. These results taken together indicate that the Grpr-expressing neurons are required for the transmission of itch sensation from the eyes, but that foot preference is generated centrally. These findings could open up a new field of research on the mechanisms of the laterality in vertebrates and also offer new potential therapeutic approaches to refractory pruritic eye disorders.