ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Deep Relational Reasoning for the Prediction of Language Impairment and Postoperative Seizure Outcome Using Preoperative DWI Connectome Data of Children With Focal Epilepsy

Prolonged seizures in children with focal epilepsy (FE) may impair language functions and often reoccur after surgical intervention. This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the "dilated CNN + RN", a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of the dilated CNN + RN was evaluated using whole brain connectome data from 51 FE children. It was found that when compared with other state-of-the-art algorithms, the dilated CNN + RN led to an average improvement of 90.2% and 97.3% in predicting expressive and receptive language scores, and 2.2% and 4% improvement in classifying seizure freedom and seizure recurrence, respectively. These improvements were independent of the prefixed connectome densities. Also, the dilated CNN + RN could provide an explainable artificial intelligence (AI) model by computing gradient-based regression/classification activation maps. This mapping analysis revealed left superior-medial frontal cortex, bilateral hippocampi, and cerebellum as crucial hubs, facilitating important connections that were most predictive of language function and seizure refractoriness after surgery.
Current trends in the epidemiology of malaria

Malaria is one of the worlds most important infectious diseases, occurring in many tropical and subtropical countries. The causative agent is a parasitic protozoan of the genus Plasmodium, transmitted to humans by infected mosquitoes. More than 200 million people get malaria every year worldwide, and hundreds of thousands of them, mostly children under 5 years of age, die of it. Thanks to prevention programmes implemented by various organisations headed by the World Health Organisation (WHO) with the aim of eliminating malaria, cases have been declining in recent years. However, particularly in African countries, malaria continues to be a health and economic issue.
Better Regulation of End-Of-Life Care: A Call For A Holistic Approach

Existing regulation of end-of-life care is flawed. Problems include poorly-designed laws, policies, ethical codes, training, and funding programs, which often are neither effective nor helpful in guiding decision-making. This leads to adverse outcomes for patients, families, health professionals, and the health system as a whole. A key factor contributing to the harms of current regulation is a siloed approach to regulating end-of-life care. Existing approaches to regulation, and research into how that regulation could be improved, have tended to focus on a single regulatory instrument (e.g., just law or just ethical codes). As a result, there has been a failure to capture holistically the various forces that guide end-of-life care. This article proposes a response to address this, identifying "regulatory space" theory as a candidate to provide the much-needed holistic insight into improving regulation of end-of-life care. The article concludes with practical implications of this approach for regulators and researchers.
Characterization and dating of San rock art in the Metolong catchment, Lesotho: A preliminary investigation of technological and stylistic changes

Recent research on Later Stone Age (LSA) San rock art in southern Africa has unveiled some of the paint recipes the artists employed. However, these discoveries still need to be linked to human activities in or near the rock shelters where the paintings were made. In this paper, we report characterization and dating results from the catchment of the Metolong Dam, Phuthiatsana Valley, Lesotho. A total of 92 rock painting samples, six grindstones with traces of colouring materials from an excavated context, and 17 potential raw colouring materials were studied. We identified three previously unreported ingredients used by the artists: manganese oxides, calcined bones, and soot. Grindstones are stained with the same raw materials that the painters used. We propose that one of them may have served to prepare the red pigment used to make a human figure and a bichrome eland at Ha Makotoko, but direct links remain difficult to establish with certainty. The potential colouring materials in the valley are red clays, white clays (kaolinite and illite-or-montmorillonite), and gypsum, three compounds used as paints by the artists. Tests conducted to verify their suitability for paintings show these materials may have been ground, but settling (after pre-grinding) offers a quicker and easier way to obtain a fine powder as observed in the paints. Finally, 12 AMS dates provide an initial framework for studying the changing use of paint recipes in the Phuthiatsana Valley over time. Charcoal appears to have been employed over a period of at least 3000 years and carbon black for at least 2000 years, with soot seemingly used only before 2000 cal. BP. This study is currently the largest characterization and dating study of LSA rock art in southern Africa and shows the potential that such combined investigations offer for linking excavated and parietal components of the region's huntergatherer archaeological record.
Contextual deconvolution network for semantic segmentation

In this paper, we propose a Contextual Deconvolution Network (CDN) and focus on context association in decoder network. Specifically, in upsampling path, we introduce two types of contextual modules to model the interdependencies of features in channel and spatial dimensions respectively. The channel contextual module captures image-level semantic information by aggregating the feature maps across spatial dimensions, and clarifies global ambiguity of features. Meanwhile, the spatial contextual module obtains patch-level semantic context by learning a spatial weight map, and enhance the feature discrimination. We embed the two contextual modules into individual components of the decoder network, thus improving the representation power and gaining more precise segment results. Thorough evaluations are performed on four challenging datasets, i.e., PASCAL VOC 2012, ADE20K, PASCAL-Context and Cityscapes dataset. Our approach achieves competitive performance with state-of-the-art models on PASCAL VOC 2012, ADE20K and Cityscapes dataset, and new state-of-the-art performance on PASCAL-Context dataset. (C) 2019 Published by Elsevier Ltd.
Benchmarking state-of-the-art imbalanced data learning approaches for credit scoring

The goal of credit scoring is to identify abnormalities, aiding decision making and maintaining the order of financial transactions. Due to the small number of default records, one inevitably faces a class imbalance problem when handling financial data. The class imbalance problem has received a lot of attention because of the eco-nomic loss that can occur when one fails to accurately identify default samples. To solve this problem, there are various classic and mature approaches to learning imbalanced data, including resampling approaches, cost -sensitive strategies, and so on. Especially in recent years, generative adversarial networks (GANs) have attrac-ted the attention of researchers to explore these networks' effects as imbalanced data learning tools. However, no attention has been paid to the systematic scoring and comparison of these traditional and state-of-the-art imbalanced data learning approaches in relation to credit scoring. Therefore, choosing several related data -sets, we compare the performance of the traditional approaches and GANs in solving the class imbalance problem of credit scoring; at the same time, with the help of benchmark analysis, we provide some suggestions for relevant research.
Fast and Fully Automatic Left Ventricular Segmentation and Tracking in Echocardiography Using Shape-Based B-Spline Explicit Active Surfaces

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81 +/- 0.59 and 1.98 +/- 0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.
A Novel Nonparametric Maximum Likelihood Estimator for Probability Density Functions

Parametric maximum likelihood (ML) estimators of probability density functions (pdfs) are widely used today because they are efficient to compute and have several nice properties such as consistency, fast convergence rates, and asymptotic normality. However, data is often complex making parametrization of the pdf difficult, and nonparametric estimation is required. Popular nonparametric methods, such as kernel density estimation (KDE), produce consistent estimators but are not ML and have slower convergence rates than parametric ML estimators. Further, these nonparametric methods do not share the other desirable properties of parametric ML estimators. This paper introduces a nonparametric ML estimator that assumes that the square-root of the underlying pdf is band-limited (BL) and hence "smooth". The BLML estimator is computed and shown to be consistent. Although convergence rates are not theoretically derived, the BLML estimator exhibits faster convergence rates than state-of-the-art nonparametric methods in simulations. Further, algorithms to compute the BLML estimator with lesser computational complexity than that of KDE methods are presented. The efficacy of the BLML estimator is shown by applying it to (i) density tail estimation and (ii) density estimation of complex neuronal receptive fields where it outperforms state-of-the-art methods used in neuroscience.
Dating ancient paintings of Mogao Grottoes using deeply learnt visual codes

Cultural heritage is the asset of all the peoples of the world. The preservation and inheritance of cultural heritage is conducive to the progress of human civilization. In northwestern China, there is a world heritage site - Mogao Grottoes - that has a plenty of mural paintings showing the historical cultures of ancient China. To study these historical cultures, one critical procedure is to date the mural paintings, i.e., determining the era when they were created. Until now, most mural paintings at Mogao Grottoes have been dated by directly referring to the mural texts or historical documents. However, some are still left with creation-era undetermined due to the lack of reference materials. Considering that the drawing style of mural paintings was changing along the history and the drawing style can be learned and quantified through painting data, we formulate the problem of mural-painting dating into a problem of drawing-style classification. In fact, drawing styles can be expressed not only in color or curvature, but also in some unknown forms - the forms that have not been observed. To this end, besides sophisticated color and shape descriptors, a deep convolution neural network is designed to encode the implicit drawing styles. 3860 mural paintings collected from 194 different grottoes with determined creation-era labels are used to train the classification model and build the dating method. In experiments, the proposed dating method is applied to seven mural paintings which were previously dated with controversies, and the exciting new dating results are approved by the Dunhuang experts.
Characterization of the use-wear and residues resulting from limestone working. Experimental approach to the parietal art of La Vina rock shelter (La Manzaneda, Asturias, Spain)

The Palaeolithic rock engravings that are located along the Nal ' on river basin in Northern Spain (central area of Asturias) have been studied from various perspectives (morphology, depth, style, manual range), but no use-wear studies on the stone tools used to produce such engravings have ever been undertaken. This paper aims to explore a new approach to this type of incisions based on use-wear analysis of experimental lithic tools used to engrave limestone blocks and slabs. Our results show that the use-wear traces generated by engraving limestone are welldeveloped and can be defined with specific criteria. The principal objective of this study was to provide the first experimental reference collection of use-wear resulting from engraving limestone using flint and quartzite experimental tools to compare with the traces that appear on tools in the archaeological record in contexts with parietal and portable art and, more specifically, to add a new approach to the multidisciplinary study of the La Vina rock shelter.
A Data-Driven Framework for Inter-Frequency Handover Failure Prediction and Mitigation

With 5G already deployed, challenges related to handover exacerbate due to the dense base station deployment operating on a motley of frequencies. In this paper, we present and evaluate a novel data-driven solution, to reduce inter-frequency handover failures (HOFs), hereafter referred to as TORIS (Transmit Power Tuning-based Handover Success Rate Improvement Scheme). TORIS is designed by developing and integrating two sub-solutions. First sub-solution consists of an Artificial Intelligence (AI)-based model to predict inter-frequency HOFs. In this model, we achieve higher than the state-of-the-art accuracy by leveraging two approaches. First, we devise a novel feature set by exploiting domain knowledge gathered from extensive drive test data analysis. Second, we exploit an extensive set of data augmentation techniques to address the class imbalance in training the HOF prediction model. The data augmentation techniques include Chow-Liu Bayesian Network and Generative Adversarial Network further improved by focusing the sampling only on the borderline. We also compare the performance of state-of-the-art AI models for predicting HOFs with and without augmented data. Results show that AdaBoost yields best performance for predicting HOFs. The second sub-solution is a heuristic scheme to tune the transmit (Tx) power of serving and target cells. Unlike the state-of-the-art approaches for HOF reduction that tune cell individual offset, TORIS targets the main cause of HOFs i.e., poor signal quality and propagation condition, by proactively varying the Tx power of the cells whenever a HOF is anticipated. Results show that TORIS outperforms the state-of-the-art HOF reduction solution and yields 40%-75% reduction in HOFs.
Imaging of Bone Marrow Involvement in Lymphoma: State of the Art and Future Directions

Accurate detection of bone marrow involvement in patients with lymphoma is of crucial importance because of the prognostic and therapeutic consequences. Bone marrow trephine biopsy (BMB) is currently regarded as the method of choice for the evaluation of the bone marrow in lymphoma, but it is invasive, has a risk of complications, and lacks sufficient sensitivity due to the possibility of sampling errors. Bone marrow imaging, if accurate, may (partially) replace BMBand/or may improve the sensitivity of BMB by guiding the biopsy to the location that appears to be involved by lymphoma at imaging. In this scientific communication, general concepts of bone marrow imaging, state-of-the-art imaging modalities, and future imaging strategies for the assessment of the bone marrow in lymphoma will be reviewed and discussed.
A meta-analysis on morphological, physiological and biochemical responses of plants with PGPR inoculation under drought stress

Plant growth-promoting rhizobacteria (PGPR) can help plants to resist drought stress. However, the mechanisms of how PGPR inoculation affect plant status under drought remain incompletely understood. We performed a meta-analysis of plant response to PGPR inoculation by compiling data from 57 PGPR-inoculation studies, including 2, 387 paired observations on morphological, physiological and biochemical parameters under drought and well-watered conditions. We compare the PGPR effect on plants performances among different groups of controls and treatments. Our results reveal that PGPR enables plants to restore themselves from drought-stressed to near a well-watered state, and that C4 plants recover better from drought stress than C3 plants. Furthermore, PGPR is more effective underdrought than well-watered conditions in increasing plant biomass, enhancing photosynthesis and inhibiting oxidant damage, and the responses of C4 plants to the PGPR effect was stronger than that of C3 plants under drought conditions. Additionally, PGPR belonging to different taxa and PGPR with different functional traits have varying degrees of drought-resistance effects on plants. These results are important to improve our understanding of the PGPR beneficial effects on enhanced drought-resistance of plants.
Extracting semantics from audiovisual content: The final frontier in multimedia retrieval

Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss, how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and also discuss various mechanisms for modeling concepts and context.
ICTs, growth, and environmental quality nexus: dynamic panel threshold regression

ICTs (information and communication technologies) have emerged as a potent new force. Digitalization, modernization, and automation of the manufacturing process are expected to facilitate ICT adoption, resulting in increased genuine environmental concerns. This research aims to examine the impact of ICTs on environmental quality and the relationship between ICTs, environmental quality, and economic growth. Dynamic panel threshold regression was employed, and the sample countries comprised 69 developing countries from 2010 to 2019. The threshold technique will identify the precise threshold value of ICTs and highlights the impacts of ICTs on the environmental quality nexus when above and below the threshold value in developing countries. Empirical evidence suggests that ICTs positively impact environmental quality (CO2) when above the ICTs threshold value. However, ICTs provide a positive but insignificant impact on environmental quality when below the ICTs threshold value of 4.699. Additionally, ICTs affect the economic growth and environmental quality nexus, with increasing economic growth resulting in a decrease in CO2 emissions in developing countries when ICTs are below the threshold value. Thus, the ICTs threshold value should be used to ensure that ICTs adoption promotes sustainable economic growth and resolves environmental degradation issues in developing nations.
An Investigation into Art Therapy Aided Health and Well-Being Research: A 75-Year Bibliometric Analysis

Considering the physical, and psychological impacts and challenges brought about the coronavirus disease 2019 (COVID-19), art therapy (AT) provides opportunities to promote human health and well-being. There are few systematic analysis studies in the fields of AT, which can provide content and direction for the potential value and impact of AT. Therefore, this paper aims to critically analyze the published work in the field of AT from the perspective of promoting health and well-being, and provides insights into current research status, hotspots, limitations, and future development trends of AT. This paper adopts a mixed method of quantitative and qualitative analysis including bibliometric analysis and keyword co-occurrence analysis. The results indicate that: (1) the current studies on AT are mostly related to research and therapeutic methods, types of AT, research populations and diseases, and evaluation of therapeutic effect of AT. The research method of AT mainly adopts qualitative research, among which creative arts therapy and group AT are common types of AT, and its main research populations are children, veterans, and adolescents. AT-aided diseases are trauma, depression, psychosis, dementia, and cancer. In addition, the therapeutic methods are mainly related to psychotherapy, drama, music, and dance/movement. Further, computer systems are an important evaluation tool in the research of AT; (2) the future development trend of AT-aided health and well-being based on research hotspots, could be focused on children, schizophrenia, well-being, mental health, palliative care, veterans, and the elderly within the context of addressing COVID-19 challenges; and (3) future AT-aided health and well-being could pay more attention to innovate and integrate the therapeutic methods of behavior, movement, and technology, such as virtual reality and remote supervision.