ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Unconventional and Dynamically Anisotropic Thermal Conductivity in Compressed Flexible Graphene Foams

Although a variety of methods to predict the effective thermal conductivity of porous foams have been proposed, the response of such materials under dynamic compressive loading has generally not been considered. Understanding the dynamic thermal behavior will widen the potential applications of porous foams and provide insights into methods of modifying material properties to achieve desired performance. Previous experimental work on the thermal conductivity of a flexible graphene composite under compression showed intriguing behavior: the cross-plane thermal conductivity remained approximately constant with increasing compression, despite the increasing mass density. In this work, we use molecular dynamics (MD) simulations and finite element analysis to study the variation in both the cross-plane and in-plane thermal conductivities by compressing isotropic graphene foams. We have found that, interestingly, the cross-plane thermal conductivity decreases with compression while the in-plane thermal conductivity increases; hence, the dynamic thermal transport of the graphene foam becomes anisotropic with a significant anisotropy ratio. Such observations cannot be explained by the conventional effective medium theory, which describes the increase of thermal conductivity to be proportional to mass density. Thus, we propose a model that can describe such anisotropic effective thermal conductivity of highly porous open-cell media during compression. The model is validated against the MD simulations as well as a larger-scale finite element simulation of an open-cell foam geometry.
Tight binding enantiomers of pre-clinical drug candidates

MTDIA is a picomolar transition state analogue inhibitor of human methylthioadenosine phosphorylase and a femtomolar inhibitor of Escherichia coli methylthioadenosine nucleosidase. MTDIA has proven to be a non-toxic, orally available pre-clinical drug candidate with remarkable anti-tumour activity against a variety of human cancers in mouse xenografts. The structurally similar compound MTDIH is a potent inhibitor of human and malarial purine nucleoside phosphorylase (PNP) as well as the newly discovered enzyme, methylthioinosine phosphorylase, isolated from Pseudomonas aeruginosa. Since the enantiomers of some pharmaceuticals have revealed surprising biological activities, the enantiomers of MTDIH and MTDIA, compounds 1 and 2, respectively, were prepared and their enzyme binding properties studied. Despite binding less tightly to their target enzymes than their enantiomers compounds 1 and 2 are nanomolar inhibitors.
Improving LiDAR compression efficiency on small packets

Several high-quality methods for compressing LiDAR data stored in the LAS format have evolved in recent years. They offer good compression for large datasets, but are less efficient on small data packets, which are needed in web applications. This problem is focused on by analysing two state-of-the-art implementations for large LAS datasets and then proposing improvements suitable for small data packets.
Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications

Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87 & x0025; accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications.
Sampling as a Baseline Optimizer for Search-Based Software Engineering

Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method "SWAY", short for "the sampling way". This paper compares SWAY versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. For these models, the experiments of this paper show that SWAY is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations. Considering the simplicity and effectiveness of SWAY, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.
Exosomes as Potential Functional Nanomaterials for Tissue Engineering

Exosomes are cell-derived extracellular vesicles of 40-160 nm diameter, which carry numerous biomolecules and transmit information between cells. They are used as functional nanomaterials with great potential in biomedical areas, such as active agents and delivery systems for advanced drug delivery and disease therapy. In recent years, potential applications of exosomes in tissue engineering have attracted significant attention, and some critical progress has been made. This review gives a complete picture of exosomes and their applications in the regeneration of various tissues, such as the central nervous systems, kidney, bone, cartilage, heart, and endodontium. Approaches employed for modifying exosomes to equip them with excellent targeting capacity are summarized. Furthermore, current concerns and future outlook of exosomes in tissue engineering are discussed.
The Use of Online CB-ART Interventions in the Context of COVID-19: Enhancing Salutogenic Coping

Community crises require the provision of short-term reflective intervention methods to help service users identify stressors, and access and intensify their adaptive coping. Here, we demonstrate the use of a single-session online cognitive behavioral- and art-based (CB-ART) intervention within the context of the COVID-19 pandemic. In this method, the individual draws three images: his/her COVID-19-related stress, his/her perceived resources, and an integration of stress and resources. This method provided a reflective space in which individuals could identify their experienced stressors, acknowledge their coping resources, and integrate these two elements within the context of the current pandemic. In this article, we use illustrative examples from a study implemented during the first national lockdown in Israel and present a tool that can be easily implemented by mental-health professionals in ongoing community crises. The aims of this intervention were to co-create knowledge with service users, access their self-defined needs and strengths, and enhance their coping by enabling them to view stress and coping as part of the salutogenic continuum.
Evaluating the Integrated Methadone and Anti-Retroviral Therapy Strategy in Tanzania Using the RE-AIM Framework

There are an estimated 50,000 people who inject drugs in Tanzania, with an HIV prevalence in this population of 42%. The Integrated Methadone and Anti-Retroviral Therapy (IMAT) strategy was developed to integrate HIV services into an opioid treatment program (OTP) in sub-Saharan Africa and increase anti-retroviral therapy (ART) initiation rates. In this paper, we evaluate the IMAT strategy using an implementation science framework to inform future care integration efforts in the region. IMAT centralized HIV services into an OTP clinic in Dar Es Salaam, Tanzania: HIV diagnosis, ART initiation, monitoring and follow up. A mixed-methods, concurrent design, was used for evaluation: quantitative programmatic data and semi-structured interviews with providers and clients addressed 4 out of 5 components of the RE-AIM framework: reach, effectiveness, adoption, implementation. Results showed high reach: 98% of HIV-positive clients received HIV services; effectiveness: 90-day ART initiation rate doubled, from 41% pre-IMAT to 87% post-IMAT (p < 0.001); proportion of HIV-positive eligible clients on ART increased from 71% pre-IMAT to 98% post-IMAT (p < 0.001). There was high adoption and implementation protocol fidelity. Qualitative results informed barriers and facilitators of RE-AIM components. In conclusion, we successfully integrated HIV care into an OTP clinic in sub-Saharan Africa with increased rates of ART initiation. The IMAT strategy represents an effective care integration model to improve HIV care delivery for OTP clients.
A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.
DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning

On-the-fly learning is unavoidable for applications that demand instantaneous deep neural network (DNN) training or where transferring data to the central system for training is costly. Hyperparameter optimization plays a significant role in the performance and reliability in deep learning. Many hyperparameter optimization algorithms have been developed for obtaining better validation accuracy in DNNs. Most state-of-the-art hyperparameter optimization techniques are computationally expensive due to the focus on validation accuracy. Therefore, they are unsuitable for on-the-fly learning applications that require faster computation on resource constraint devices (e.g., edge devices). In this paper, we develop a novel greedy approach-based hyperparameter optimization (GHO) algorithm enabling faster computing on edge devices for on-the-fly learning applications. In GHO, we obtain the validation accuracy locally for each of the hyperparameter configurations. The GHO algorithm optimizes each hyperparameter while keeping the others constant in order to converge to the locally optimal solution in the expectation that this choice will lead to a globally optimal solution. We perform an empirical study to compute the performance such as computation time and energy consumption of the GHO and compare it with two state-of-the-art hyperparameter optimization algorithms. We also deploy the GHO algorithm in an edge device to validate the performance of our algorithm. We perform post-training quantization on the GHO algorithm to reduce the inference time. Our GHO algorithm is more than 3x energy efficient and 2x faster than two state-of-the-art hyperparameter optimization techniques on both DNNs and datasets studied in this paper.
A Case of Sex Cord-Stromal Tumor Originating in the Retroperitoneal Space

A 54-year-old man was seen in the clinic with the chief complaint of epigastric pain radiating to the left groin region and a predominant postprandial abdominal discomfort. Upon examination, a painless round mass with reduced mobility was felt in the left flank during deep palpation of the abdomen. His past medical history was irrelevant. Ultrasound and IV contrast-enhanced CT scan confirmed the presence of a large tumor and an exploratory laparotomy for removal of the tumor was performed. The microscopic examination of the specimen confirmed the primary diagnosis of retroperitoneal tumor (RPT) and identified it as an extragonadal germ cell tumor with a vestigial origin, which is a rare type affecting the kidney and adrenal gland. Primitive RPTs are histologically classified as mesenchymal and neuroectodermal or vestigial. These histological types are rarely found in surgical practice and are challenging to diagnose and treat due to the peculiarities of the site of origin where they develop. RPTs are extremely rare and approximately 80% are malignant and detected lately during the disease's course, commonly discovered in advanced stages of local or systemic evolution. Currently, surgical intervention remains the only effective method of treating these tumors.
Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm

BACKGROUND. In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. OBJECTIVE. The purpose of this article is to compare the diagnostic performance of radiologists' overall impression, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), and a deep learning algorithm in differentiating benign and malignant thyroid nodules on ultrasound in children and young adults. METHODS. This retrospective study included 139 patients (median age 17.5 years; 119 female patients, 20 male patients) evaluated from January 1, 2004, to September 18, 2020, who were 21 years old and younger with a thyroid nodule on ultrasound with definitive pathologic results from fine-needle aspiration and/or surgical excision to serve as the reference standard. A single nodule per patient was selected, and one transverse and one longitudinal image each of the nodules were extracted for further evaluation. Three radiologists independently characterized nodules on the basis of their overall impression (benign vs malignant) and ACR TI-RADS. A previously developed deep learning algorithm determined for each nodule a likelihood of malignancy, which was used to derive a risk level. Sensitivities and specificities for malignancy were calculated. Agreement was assessed using Cohen kappa coefficients. RESULTS. For radiologists' overall impression, sensitivity ranged from 32.1% to 75.0% (mean, 58.3%; 95% CI, 49.2-67.3%), and specificity ranged from 63.8% to 93.9% (mean, 79.9%; 95% CI, 73.8-85.7%). For ACR TI-RADS, sensitivity ranged from 82.1% to 87.5% (mean, 85.1%; 95% CI, 77.3-92.1%), and specificity ranged from 47.0% to 54.2% (mean, 50.6%; 95% CI, 41.4-59.8%). The deep learning algorithm had a sensitivity of 87.5% (95% CI, 78.3-95.5%) and specificity of 36.1% (95% CI, 25.6-46.8%). Interobserver agreement among pairwise combinations of readers, expressed as kappa, for overall impression was 0.227-0.472 and for ACR TI-RADS was 0.597-0.643. CONCLUSION. Both ACR TI-RADS and the deep learning algorithm had higher sensitivity albeit lower specificity compared with overall impressions. The deep learning algorithm had similar sensitivity but lower specificity than ACR TI-RADS. Interobserver agreement was higher for ACR TI-RADS than for overall impressions. CLINICAL IMPACT. ACR TI-RADS and the deep learning algorithm may serve as potential alternative strategies for guiding decisions to perform fine-needle aspiration of thyroid nodules in children.
The Shigir idol in the context of early art in Eurasia

In 1890 the so called "Shigir Idol" was found in a peat bog and for a long time discussion on the dating of the wooden sculpture was going on. In the 1990s first conventional radiocarbon dates suggested a Mesolithic context, but a series of recent AMS-results date the object close to the beginning of the Holocene (c. 10,000 calBC). The surprisingly early date makes the find the earliest monumental wooden sculpture of the world. A direct parallel is not available and this hampers the interpretation and contextualization of the find. Here we discuss the find according to aspects such as wood working and the type of sculpture and decoration in the Late Palaeolithic to Early Mesolithic context of Eurasia. We can show that there is a long tradition of wood working since the Lower Palaeolithic and the very limited evidence of wooden objects from the Palaeolithic and Mesolithic is due to preservation conditions. Anthropomorphic figures are sometimes present in Late Glacial art and less anthropomorphic representations are available from the Early Mesolithic. The geometric patterns of the Shigir sculpture such as simple lines and zigzag-ornaments are common elements of Late Palaeolithic and Early Mesolithic decoration. In conclusion the different elements of the Shigir sculpture fit better to the record of Late Glacial to Early Mesolithic art in Eurasia than expected. We see this as a confirmation of the early date of the monumental sculpture. The figure demonstrates a complex expression of symbolic behavior and art of hunter-gatherers at that time. The only general parallel of monumental anthropomorphic figures from that time can be mentioned from the far distant Gobekli Tepe site in eastern Anatolia.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.
Energy and building aesthetics. Slovenian examples of good practice

The well-being of people, the industry and the economy depends on a safe, reliable, sustainable, affordable, efficient and economical use of energy. At the same time, energy-related emissions represent almost 80% of the total greenhouse gas emissions in the European Union (EU). Therefore the question of efficient energy use is one of the biggest challenges facing Europe and Slovenia in the coming decades. The building industry is responding to the energy and environmental crisis with a development of new low energy systems for the heating and cooling of the buildings. New technologies pose designers and architects new tasks and challenges, as well as opportunities for creating new, low energy and sustainable environment and buildings. With a multidisciplinary approach, featuring technical disciplines, as well as architecture as an art, it is possible to transform new technological arrangements and buildings into poetic living spaces for people. The possibilities of such an approach are illustrated by seven examples of low energy buildings in Slovenia. (C) 2015 Published by Elsevier B.V.
Robust key point descriptor for multi-spectral image matching

Histogram of collinear gradient-enhanced coding (HCGEC), a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of-the-art descriptors.