ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
[Effects of microencapsulated and heme iron supplementation on the recovery of hemoglobin levels in iron-depleted rats]

Introduction: one of the causes of nutritional anemia is that the amount of iron absorbed is insufficient to meet the body's needs. Objective: to determine the effects of microencapsulated and heminic iron supplementation to increase hemoglobin levels and body weight in iron-depleted rats. Methods: a randomized experimental study was designed. Four study groups were formed. Control group (GC), experimental group (GE1) (MC microencapsulated iron supplementation), experimental group 2 (GE2) (heminic iron supplementation) and experimental group 3 (GE3) (MC + heminic iron supplementation). A dry powdered diet containing all normal nutrients except iron was fed to the four groups for 15 days (three times a day). Weight, length and hemoglobin (Hb) were evaluated at pre- and post-test under similar conditions. Results: in Hb, no significant differences were observed in the CG (p = 0.225), despite a -9.6 % decrease in the post-test. GE1 significantly increased hemoglobin (14.3 %, Hb 2.1 g/dl) and body weight (21.6 %, 25.8 g) (p < 0.05) in the post-test. Similarly, GE2 significantly increased hemoglobin (14.5 %, Hb 2.1 g/dl) and body weight (44.5 %, 52.3 g) (p < 0.05). However, in GE3, despite significantly increasing weight (30.2 %, 35.2 g), the increase in hemoglobin levels was similar to what occurred in GE1 and GE2 groups (increasing by 14.5 % and Hb 2.2 g/dl). There were no significant differences in rat length between pre- and post-test in the four groups. Conclusion: these results suggest that heme iron together with quinoa and cañihua flour could be exploited as a new safe and efficient iron supplement compared to microencapsulated iron, given its higher iron bioavailability and its ability to increase body weight.
Echocardiography Segmentation With Enforced Temporal Consistency

Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints. Our method is a post-processing that takes as input segmented echocardiographic sequences produced by any state-of-the-art method and processes it in two steps to (i) identify spatio-temporal inconsistencies according to the overall dynamics of the cardiac sequence and (ii) correct the inconsistencies. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes, where we can both detect and fix anomalies. We tested our framework on 98 full-cycle sequences from the CAMUS dataset, which are available alongside this paper. Our temporal regularization method not only improves the accuracy of the segmentation across the whole sequences, but also enforces temporal and anatomical consistency.
The Trend of Metaverse and Augmented & Virtual Reality Extending to the Healthcare System

There is no escaping Internet's favorite buzzword for 2022: The Metaverse. Everyone is talking about it, but only a few know what it is or how it works. One can look at the Metaverse as a 3D model of the Internet where it is possible to spend your reality parallel to the virtual world. In broad terms, Metaverse can be explained as a virtual space, graphically rich, leaning towards verisimilitude where people can do all sorts of things they do in real-life such as shop, play, socialize, and party. The pandemic has accelerated innovations in the digital age. Looking beyond revolutions in telehealth, payments, remote monitoring, and secure data-sharing are other essential innovations in the fields of artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and blockchain technology. The Metaverse is still in its nascent stage and evolving continuously, having a huge potential in health care to combine the technologies of AI, AR/VR, web 3.0, Internet of medical devices, and quantum computing, along with robotics to give a new direction to healthcare systems. From improving surgical precision to therapeutic usage and more, the Metaverse can bring significant changes to the industry.
Similarity-based Demosaicing Algorithm using Unified High-Frequency Map

This paper introduces an innovative demosaicing algorithm for color filter array (CFA). Conventional demosaicing algorithms usually detect edges in horizontal-, vertical- or omni-direction, and apply a directional filtering along the edge direction. It is found that these algorithms do not properly work for diagonal edges. We, then, invent a similarity based filtering using unified high-frequency (UHF) map. This new technique enables flexible filtering masks so as to be able to deal with edges of any direction. It is confirmed through experiments that the proposed algorithm outperforms state-of-the-art algorithms in terms of PSNR and subjective quality. In addition, the proposed algorithm requires fewer resources than the state-of-the-art algorithms.
A Comparison Between Direct Telehealth and In-Person Methods of Teaching Expressive Labels to Children Diagnosed With Autism Spectrum Disorder

Recent behavior analytic research has demonstrated that the provision of applied behavior analytic services via direct telehealth can be an effective teaching modality for some learners with autism spectrum disorder (ASD). Historically, teaching procedures based on applied behavior analysis (ABA), including discrete trial teaching (DTT), have been provided and evaluated via in-person delivery. This study sought to compare the implementation of DTT via direct telehealth to DTT implemented in-person within and across participants. Specifically, this study evaluated the two delivery modalities in terms of skill acquisition, maintenance, efficiency, and learner responding during teaching sessions. Results of an adapted alternating treatments design nested into a multiple baseline design demonstrated that all three participants diagnosed with ASD met the mastery criteria for the expressive labels taught. Areas of future research, participant prerequisite skills, and clinical implications will be discussed in the context of these results.
Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault

Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available.
Updated insight into the role of Th2-associated immunity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple organs involvement, abundant autoantibodies, complement activation, and immune complexes depositions. By regulating inflammation and immune homeostasis, cytokines have been well documented to participate in the pathogenesis of SLE. A number of studies have shown that T helper 2 (Th2)-associated immunity plays an important role in autoimmune diseases, including SLE. Key molecules underlying Th2-related immunity are expected to serve as promising targets for the diagnosis and targeted treatment of SLE. Current progress in SLE pathogenesis and biological treatment strategies has been reviewed, focusing on the latest development in Th2-associated immunity.
Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization

Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: 1) the noise is multiplied by (rather than added to) the original image; 2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: 1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; 2) using variable splitting to obtain an equivalent constrained problem; and 3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance.
Statistical Shape Model-Based Femur Kinematics From Biplane Fluoroscopy

Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 +/- 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5 degrees precision).
Sufu- and Spop-mediated regulation of Gli2 is essential for the control of mammalian cochlear hair cell differentiation

Development of mammalian auditory epithelium, the organ of Corti, requires precise control of both cell cycle withdrawal and differentiation. Sensory progenitors (prosensory cells) in the cochlear apex exit the cell cycle first but differentiate last. Sonic hedgehog (Shh) signaling is required for the spatiotemporal regulation of prosensory cell differentiation, but the underlying mechanisms remain unclear. Here, we show that suppressor of fused (Sufu), a negative regulator of Shh signaling, is essential for controlling the timing and progression of hair cell (HC) differentiation. Removal of Sufu leads to abnormal Atoh1 expression and a severe delay of HC differentiation due to elevated Gli2 mRNA expression. Later in development, HC differentiation defects are restored in the Sufu mutant by the action of speckle-type PDZ protein (Spop), which promotes Gli2 protein degradation. Deletion of both Sufu and Spop results in robust Gli2 activation, exacerbating HC differentiation defects. We further demonstrate that Gli2 inhibits HC differentiation through maintaining the progenitor state of Sox2+ prosensory cells. Along the basal-apical axis of the developing cochlea, the Sox2 expression level is higher in the progenitor cells than in differentiating cells and is down-regulated from base to apex as differentiation proceeds. The dynamic spatiotemporal change of Sox2 expression levels is controlled by Shh signaling through Gli2. Together, our results reveal key functions of Gli2 in sustaining the progenitor state, thereby preventing HC differentiation and in turn governing the basal-apical progression of HC differentiation in the cochlea.
Dual-level attention-aware network for temporal emotion segmentation

Human emotions are known to always have four phases in the temporal domain: neutral, onset, apex, and offset. This has been demonstrated to be of great benefit for emotion recognition. Therefore, temporal segmentation has attracted considerable research interest. Although state-of-the-art techniques use recurrent neural networks to highly increase the performance, they ignore the relevance of each frame (time step) of a video, and they do not consider the changing contribution of different features when fusing them. We propose a framework called dual-level attention-aware bidirectional grated recurrent unit, which integrates ideas from attention models to discover the most important frames and features for improving temporal segmentation. Specifically, it applies attention mechanisms at two levels: frame and feature. A significant advantage is that the two-level attention weights provide a meaningful value to depict the importance of each frame and feature. The experiments demonstrated that the proposed framework outperforms state-of-the-art methods. (C) 2018 SPIE and IS&T
ADAPT: A Write Disturbance-Aware Programming Technique for Scaled Phase Change Memory

Phase change memory (PCM) is an emerging, resistance-based, nonvolatile memory. With a promising scaling potential, PCM can replace the existing charge-based memory technologies. A highly scaled PCM is prone to write disturbance (WD) because of a high-current RESET programming pulse. Exploiting the data-dependent nature of WD, encoding techniques have been proposed to reduce the frequency of WD-vulnerable data patterns. These techniques work along with a verify and correct (VnC) method to ensure memory reliability. However, the effectiveness of these techniques varies depending on the data patterns. Unlike the conventional methods, this article introduces a WD-aware programming technique to mitigate WD in PCM. The proposed method encodes the data based on the number of WD-vulnerable cells and the bit flips. By reducing the number of WD-vulnerable cells as well as the bit flips, the proposed method is more effective than the existing encoding techniques, in mitigating WD as well as improving the memory lifetime. Evaluation using various realistic workloads shows that the proposed method can reduce the average word-line WD errors by 62%, compared to the existing state of the art. With a reduced number of WD errors, the frequency of a VnC operation is also reduced. This leads to a reduction of 44% in the number of extra writes and 20% in the average write time. With reduction in the number of writes and the write time, instructions-per-cycle is improved by 9% and the write energy by 11% over the existing art. By reducing the number of bit flips compared to the previous state of the art, the proposed method improves the PCM memory lifetime by 13% to 33%, considering the asymmetry of SET and RESET operations in impacting the cell endurance.
Oxy-fuel combustion technology for cement production - State of the art research and technology development

Oxy-fuel combustion stands as a promising carbon capture technology to significantly reduce CO2 emissions from industrial combustion processes. Due to a different process layout compared to power industry as well as different boundary conditions further investigations and demonstration activities are required to develop the oxy-fuel cement process to maturity. This paper presents an overview on research activities and current state-of-the-art on the development of oxy-fuel combustion applied to the cement process. Oxy-firing concepts for cement plants are introduced under two process configurations. Modifications regarding plant lay-out and key components as well as operational implications are discussed. Relevant research projects focusing on the application of the oxy-fuel technology in the cement industry are presented and finally fields, in which further research is required, are identified. (C) 2015 Elsevier Ltd. All rights reserved.
Ultrasonic‑assisted molten salt hydrates pretreated Eucheuma cottonii residues as a greener precursor for third-generation L-lactic acid production

This study aims to establish an efficient pretreatment method that facilitates the conversion of sugars from macroalgae wastes, Eucheuma cottonii residues (ECRs) during hydrolysis and subsequently enhances L-lactic acid (L-LA) production. Hence, ultrasonic-assisted molten salt hydrates (UMSHs) pretreatment was proposed to enhance the accessibility of ECRs to hydrolyze into glucose through dilute acid hydrolysis (DAH). The obtained hydrolysates were employed as the substrate in producing L-LA by separate hydrolysis and fermentation (SHF). The maximum glucose yield (97.75%) was achieved using UMSHs pretreated ECRs with 40wt% ZnCl2 at 80 °C for 2 h and followed with DAH. The optimum glucose to L-LA yield obtained for SHF was 90.08% using 5% (w/w) inoculum cell densities of B. coagulans ATCC 7050 with yeast extract (YE). A comparable performance (89.65%) was obtained using a nutrient combination (lipid-extracted Chlorella vulgaris residues (CVRs), vitamin B3, and vitamin B5) as a partial alternative for YE.
Transcriptional Profiling of Pseudomonas aeruginosa Infections

Pseudomonas aeruginosa is an opportunistic pathogen that causes life-devastating acute as well as chronic biofilm-associated infections with limited treatment options. Its success is largely due to its remarkable adaptability. P. aeruginosa uses different long- and short-term adaptive mechanisms to increase its fitness, both at the population level through genetic diversification and at the individual cell level by adapting gene expression. These adapted gene expression profiles can be fixed by the accumulation of patho-adaptive mutations. The latter are often found in transcriptional regulators and lead to rewiring of the regulatory network to promote survival at the infected host site. In this chapter, we review recent developments in transcriptional profiling and explain how these provide new insights into the establishment and maintenance of P. aeruginosa infections. We illustrate what can be learned from the application of advanced RNA-seq technology, such as ex vivo RNA-seq, host-pathogen crosstalk (dual RNA-seq), or recording of transcriptional heterogeneity within a bacterial population (single-cell RNA-seq). In addition, we discuss how large transcriptome datasets from a variety of clinical isolates can be used to gain an expanded understanding of bacterial adaptation during the infection process. Global genotype-phenotype correlation studies provide a unique opportunity to discover new evolutionary pathways of infection-related phenotypes and led to the discovery of different strategies of the pathogen P. aeruginosa to build a biofilm. Insights gained from large-scale, multi-layered functional -omics approaches will continue to contribute to a more comprehensive understanding of P. aeruginosa adaptation to the host habitat and promises to pave the way for novel strategies to combat recalcitrant infections.
Deep Learning Architectures for Navigation Using Forward Looking Sonar Images

This paper investigates the use of supervised Deep Learning (DL) networks to process sonar images for underwater navigation. State-of-the-art DL techniques for micro-navigation using sequences of optical images have been adapted to work with sonar images. Specifically, the DL networks estimate the Forward-Looking Sonar (FLS) motion in three degrees of freedom corresponding to x- and y-translation and rotation around z-axis. The state-of-the-art DL architectures and a proposed new architecture are investigated for motion estimation. They are trained using images generated by a FLS simulator. The data sets are made using pairs of consecutive images associated with labels that represent the motion of the sonar platform between images. The results show the effectiveness of using the DL architectures, which can provide millimeter accuracy for translation motion and below 0.1 degrees for rotation motion between two consecutive sonar images. Examples of trajectory estimation and mosaic building using simulated and real sonar images are also presented.