ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

Stroke is a cardiovascular disease with high mortality and long-term disability in the world. Normal functioning of the brain is dependent on the adequate supply of oxygen and nutrients to the brain complex network through the blood vessels. Stroke, occasionally a hemorrhagic stroke, ischemia or other blood vessel dysfunctions can affect patients during a cerebrovascular incident. Structurally, the left and the right carotid arteries, and the right and the left vertebral arteries are responsible for supplying blood to the brain, scalp and the face. However, a number of impairment in the function of the frontal lobes may occur as a result of any decrease in the flow of the blood through one of the internal carotid arteries. Such impairment commonly results in numbness, weakness or paralysis. Recently, the concepts of brain's wiring representation, the connectome, was introduced. However, construction and visualization of such brain network requires tremendous computation. Consequently, previously proposed approaches have been identified with common problems of high memory consumption and slow execution. Furthermore, interactivity in the previously proposed frameworks for brain network is also an outstanding issue. This study proposes an accelerated approach for brain connectomic visualization based on graph theory paradigm using compute unified device architecture, extending the previously proposed SurLens Visualization and computer aided hepatocellular carcinoma frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, USA. Significantly, our proposed framework is able to generate and extract points and edges of datasets, displays nodes and edges in the datasets in form of a network and clearly maps data volume to the corresponding brain surface. Moreover, with the framework, surfaces of the dataset were simultaneously displayed with the nodes and the edges. The framework is very efficient in providing greater interactivity as a way of representing the nodes and the edges intuitively, all achieved at a considerably interactive speed for instantaneous mapping of the datasets' features. Uniquely, the connectomic algorithm performed remarkably fast with normal hardware requirement specifications.
Cross-Modality Contrastive Learning for Hyperspectral Image Classification

Deep learning has attracted much attention in the field of hyperspectral image classification recently, due to its powerful representation and generalization abilities. Most of the current deep learning models are trained in a supervised manner, which requires large amounts of labeled samples to achieve the state-of-the-art performance. Unfortunately, pixel-level labeling in hyperspectral imageries is difficult, time-consuming, and human-dependent. To address this issue, we propose an unsupervised feature learning model using multimodal data, hyperspectral, and light detection and ranging (LiDAR) in particular. It takes advantage of the relationship between hyperspectral and LiDAR data to extract features, without using any label information. After that, we design a dual fine-tuning strategy to transfer the extracted features for hyperspectral image classification with small numbers of training samples. Such a strategy is able to explore not only the semantic information but also the intrinsic structure information of training samples. In order to test the performance of our proposed model, we conduct comprehensive experiments on three hyperspectral and LiDAR datasets. Experimental results show that our proposed model can achieve better performance than several state-of-the-art deep learning models.
Nonlinear Regression with Logistic Product Basis Networks

We introduce a novel general regression model that is based on a linear combination of a new set of non-local basis functions that forms an effective feature space. We propose a training algorithm that learns all the model parameters simultaneously and offer an initialization scheme for parameters of the basis functions. We show through several experiments that the proposed method offers better coverage for high-dimensional space compared to local Gaussian basis functions and provides competitive performance in comparison to other state-of-the-art regression methods.
CRITERIA FOR COMPREHENSIVE EVALUATION OF THE QUALITY OF A CITY'S ARTIFICIAL LIGHT MEDIUM

One of the professional creative tasks of a light designer is the creation of light medium of a city meeting certain artistic and functional features. The article determines criteria for a comprehensive evaluation of the visual quality of an artificial light medium of a city and proposes a new criterion: "design properties of light medium of a city".
WHY IT IS NECESSARY TO REVISE THE STANDARDS OF EXHIBITION LIGHTING

In the modern world, one of the main functions of museums is to organize the preservation of pieces of art and arrange their presentation to museum visitors. Since the modern exhibition is based on the artificial lighting, it is necessary to properly arrange this lighting; otherwise, it can negatively affect the safety of museum pieces. The article sets out the views on the criteria of professional lighting of works of art, as it is always a compromise between the custodians and the lighting engineers. The authors also attempt to disclose the processes of organizing museum lighting and give a generalized description of the standards and rules, which serve as a basis to realize this lighting. The main reasons for the need to rethink these standards and rules (and even to revise them), in connection with the emergence of new LED sources, have been outlined.
Asymmetric cost aggregation network for efficient stereo matching

Cost aggregation is crucial to the accuracy of stereo matching. A reasonable cost aggregation algorithm should aggregate costs within homogeneous regions where pixels have the same or similar disparities. Otherwise, the estimated disparity map is prone to the well-known edge-fattening issue and the problem of losing fine structures. However, current state-of-the-art convolutional neural networks (CNNs) mainly do cost aggregation with square-kernel convolutional layers that learn to adjust their kernel elements to make the actual receptive fields of the aggregated costs adapt to homogeneous regions with various shapes. This is a mechanism that easily leads to the above issues due to the translation equivalence and content-insensitivity properties of CNNs. To tackle these problems, a novel densely connected asymmetric convolution block (Dense-ACB) based on asymmetric convolutions is proposed to explicitly construct receptive fields with various shapes, which effectively alleviates the issues caused by mismatching shapes of receptive fields and homogeneous regions. The proposed Dense-ACB brings new insight to CNN-based stereo matching networks. Based on the proposed cost aggregation method, an efficient and effective stereo matching network is built, which not only achieves competitive overall accuracy compared with state-of-the-art models but also effectively alleviates the edge-fattening problem and preserves fine structures.
Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer

Various image pre-processing tasks in optical coherence tomography (OCT) systems involve reversing degradation effects (e.g. deblurring). Current deblurring research mainly focuses on how to build suitable degradation models using deconvolution operators. However, model-based solutions may not work well in many scenarios. To solve this problem, the authors propose a non-model architecture, called a deep convolutional neural network, to address parameter-free situations. The proposed solution employs a deep learning strategy to bridge the gap between traditional model-based methods and neural network architectures. Experiments on retinal OCT images demonstrate that the proposed approach achieves superior performance compared with the state-of-the-art model-based OCT deblurring methods.
Diagnostic and interventional EUS in hepatology: An updated review

EUS has become an increasingly used diagnostic and therapeutic modality in the armamentarium of endoscopists. With ever-expanding indications, EUS is being used in patients with liver disease, for both diagnosis and therapy. EUS is playing an important role in providing additional important information to that provided by cross-sectional imaging modalities such as computerized tomography and magnetic resonance imaging. Domains of therapy that were largely restricted to interventional radiologists have become accessible to endosonologists. From liver biopsy and sampling of liver lesions to ablative therapy for liver lesions and vascular interventions for varices, there is increased use of EUS in patients with liver disease. In this review, we discuss the various diagnostic and therapeutic applications of EUS in patients with various liver diseases.
Efficient Registration of High-Resolution Feature Enhanced Point Clouds

We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.
PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection

Plant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem.
The Contribution of Cultural Heritage Owned by Local Health Authorities in the Humanization of Care: The Point of View of Top Management

After the COVID-19 pandemic, reforms in healthcare systems have the purpose to fully recover the relationship of healthcare organizations with their patients. For centuries, art was used throughout Europe in the healthcare context for its power to engage and support patients in their illnesses. This approach can be rediscovered by utilizing the cultural heritage owned by Local Health Authorities. In this context, tradition, art, innovation, and care coexist. This study aims to investigate the interest in developing projects for the humanization of care by the top management of Italian Local Health Authorities, in particular exploiting their cultural heritage. The evaluation of the proposal was conducted using semi-structured interviews with the top management of two Local Health Authorities, in which the Santa Maria Nuova hospital in Florence and the Santo Spirito in Sassia Hospital in Rome are located, as the two selected cases for this study. The interviewees welcomed the proposal to develop humanization of care projects involving the use of their cultural heritage. Moreover, they expressed their desire to invest human, economic, and structural resources in the development of these initiatives. The implementation of humanization of care projects using cultural heritage owned by Local Health Authorities is useful to apply specific policies to enhance the governance of the cultural heritage according to the health mission. On the other hand, it permits the search for additional or ad hoc resources. Finally, it is possible to humanize and improve patients' experience while increasing awareness among the health workforce and trainees.
Toward visual microprocessors

This paper outlines motivations and models underlying the design of visual microprocessors based on the cellular neural network universal machine. We also overview the state of the art regarding the realization of these microprocessors in the form of very large-scale integration chips. Examples corresponding to measurements realized on these chips are enclosed for illustration purposes.
Atrioventricular septal defect with an absent or tiny ostium primum defect: a case series of three surgical cases

Although atrioventricular septal defects are categorized according to the anatomical atrioventricular orifice, the location of the intracardiac shunt in atrioventricular septal defects is important from a surgical perspective. Herein, we report three cases of atrioventricular septal defects with a small or no ostium primum defect. Case 1 (3-month-old girl) was diagnosed preoperatively with a ventricular septal defect, secundum atrial septal defect, and mitral valve cleft. After the operation, the diagnosis was corrected to an atrioventricular septal defect and was repaired completely. Case 2 (9-year-old girl) underwent pulmonary artery banding for a ventricular septal defect with a straddling mitral valve. After the experience with Case 1, we realized similarities between Cases 1 and 2. Therefore, we corrected the diagnosis to atrioventricular septal defect and achieved definitive repair. Based on these experiences, we accurately diagnosed Case 3 (3-month-old boy) with an atrioventricular septal defect. This variant is poorly known; however, proper morphological understanding is necessary to facilitate anatomical repair and prevent postoperative atrioventricular blocks. Some cases of this variant may be diagnosed as a ventricular septal defect with straddling mitral valve and are unable to receive definitive repair. The direction of the cleft, absence of atrioventricular valve offsetting, and trileaflet of the left atrioventricular valve all seem useful for making a diagnosis, and these can be easily confirmed by echocardiography.
[The effects of the deployment of artificial intelligence on the healthcare professions]

Understanding the effects of the spread of artificial intelligence and robotization on the healthcare professions must be free of prejudice. In this way, it will be possible to promote a real methodology for evaluating and supporting these transformations.
Environmental performance measurement in arts and cultural organisations: Exploring factors influencing organisational changes

Arts and Cultural Organisations (ACOs) have received significant attention over the last few years regarding their environmental performance. ACOs are often non-profit organisations, relying on government funding to implement various programmes to support societal development. Funding dependence can shift ACOs' focus from creating socio-cultural value to being more commercially driven. This paper explores factors influencing organisational changes in ACOs related to environmental performance measurement. Stakeholders in ACOs based in Nottingham, England, were interviewed and participated in a workshop to validate and collect addi-tional data. Our research uncovered five interrelated factors that influence organisational change: the role of funding bodies; local policies and networks; organisational culture and leadership; lack of resources; and building proprietary-tenant relationships. This paper contributes to understanding ACOs responses to measuring environmental performance and the challenges they face as they move from measuring to implementation. Implications are explored for how funding is allocated and understood in terms of moving beyond merely measuring the carbon footprint of activities. ACOs' funding dependence indicates a focus on carbon measure-ment, omitting a more holistic approach towards the environment and sustainability.
Tensor Factorization and Attention-Based CNN-LSTM Deep-Learning Architecture for Improved Classification of Missing Physiological Sensors Data

One of the essential issues for efficient control of prosthesis is the accurate classification of target movements hidden in electroencephalography (EEG) and electromyography (EMG) signals. However, in the presence of missing data in acquired signals, the classification accuracy degrades significantly as the amount of missing data increases, reducing the control performance of the prosthesis. This research proposes a framework based on tensor (multidimensional array) factorization and attention-based convolutional neural network (CNN)-long short-term memory (LSTM) deep learning (DL) for recovering missing data and performing classification of target movements, respectively. To recover missing data in tensor factorization, Canonical/Polyadic Weighted OPTimization (CP-WOPT) is employed, and its performance is compared to state-of-the-art factorization methods, whereas the performance of CNN-LSTM-attention layer (Attn) is compared to state-of-the-art machine learning and DL classifiers. Results show that CNN-LSTM-Attn obtained the mean classification accuracy of 98%, 83%, and 90% on complete (0% missing data), partially complete (10% to 50% missing data), and tensor-recovered real-world EEG and EMG data, respectively, demonstrating the applicability of the proposed framework.
Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage requirement. Online decision tree learning algorithms have been devised to tackle this problem by concurrently training with incoming samples and providing inference results. However, even the most up-to-date online tree learning algorithms still suffer from either high memory usage or high computational intensity with dependency and long latency, making them challenging to implement in hardware. To overcome these difficulties, we introduce a new quantile-based algorithm to improve the induction of the Hoeffding tree, one of the state-of-the-art online learning models. The proposed algorithm is lightweight in terms of both memory and computational demand, while still maintaining high generalization ability. A series of optimization techniques dedicated to the proposed algorithm have been investigated from the hardware perspective, including coarse-grained and fine-grained parallelism, dynamic and memory-based resource sharing, pipelining with data forwarding. Following this, we present Hard-ODT, a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques. Performance and resource utilization are modeled for the complete learning system for early and fast analysis of the tradeoff between various design metrics. Finally, we propose a design flow in which the proposed learning system is applied to FPGA run-time power monitoring as a case study. Experimental results show that our proposed algorithm outperforms the state-of-the-art Hoeffding tree learning method, leading to 0.05% to 12.3% improvement in inference accuracy. Real implementation of the complete learning system on the FPGA demonstrates a 384x to 1581x speedup in execution time over the state-of-the-art design. The power modeling strategy with Hard-ODT achieves an average power prediction error within 4.93% of a commercial gate-level power estimation tool.
Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data

We propose a novel symmetry-based technique for scalable lossless compression of 3D medical image data. The proposed method employs the 2D integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub-bands by exploiting the anatomical symmetries typically present in structural medical images. A modified version of the embedded block coder with optimized truncation (EBCOT), tailored according to the characteristics of the data, encodes the residual data generated after prediction to provide resolution and quality scalability. Performance evaluations on a wide range of real 3D medical images show an average improvement of 15% in lossless compression ratios when compared to other state-of-the art lossless compression methods that also provide resolution and quality scalability including 3D-JPEG2000, JPEG2000, and H.264/AVC intra-coding.
Repression of SLC22A3 by the AR-V7/YAP1/TAZ axis in enzalutamide-resistant castration-resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive and fatal disease, with most patients succumbing within 1-2 years despite undergoing multiple treatments. Androgen-receptor (AR) inhibitors, including enzalutamide (ENZ), are used for the treatment of mCRPC; however, most patients develop resistance to ENZ. Herein, we propose that the repression of SLC22A3 by AR-V7/YAP1/TAZ conferred ENZ resistance in mCRPC. SLC22A3 expression is specifically downregulated in the ENZ-resistant C4-2B MDVR cells, and when YAP1/TAZ is hyperactivated by AR full-length or AR-V7, these proteins interact with DNMT1 to repress SLC22A3 expression. We observed low SLC22A3 expression and high levels of TAZ or YAP1 in mCRPC patient tissues harbouring AR-V7 and the opposite expression patterns in normal patient tissues. Our findings suggest a mechanism underlying ENZ resistance by providing evidence that the AR-V7/YAP1/TAZ axis represses SLC22A3, which could be a potential treatment target in prostate cancer.
Back to the past: Symbolism and archaeology in Altxerri B (Gipuzkoa, Northern Spain)

In a previous publication on Altxerri B Cave, we explained a chronological hypothesis which proposed that the graphic activity in the site dates to an early Aurignacian phase. This paper presents a complete study of the parietal ensemble, including descriptions of the graphic motifs and other anthropic evidence that has been documented. The number of figures identified in the only panel documented in previous studies has been increased considerably, while several previously unpublished panels in other parts of the cave are described. The iconographic and stylistic characteristics of the rock art, far from contradicting our first conclusions about the chronology, support these and link the art in Altxerri B with other European Early Upper Palaeolithic graphic ensembles. (C) 2015 Elsevier Ltd and INQUA. All rights reserved.