ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tasks in the steel industry. In this paper, we propose a deep learning-based model to classify six of the most common steel strip surface defects using the NEU-CLS dataset. We investigate the effectiveness of two state-of-the-art CNN architectures (MobileNet-V2 and Xception) combined with the transfer learning approach. The proposed approach uses an ensemble of two pre-trained state-of-the-art Convolutional Neural Networks, which are MobileNet-V2 and Xception. To perform a comparative analysis of the proposed architectures, several evaluation metrics are adopted, including loss, accuracy, precision, recall, F1-score, and execution time. The experimental results show that the proposed deep ensemble learning approach provides higher performance achieving an accuracy of 99.72% compared to MobileNet-V2 (98.61%) and Xception (99.17%) while preserving fast execution time and small models' size.
Fast Run-Length Compression of Point Cloud Geometry

The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.
Efficacy of the environmentally sustainable microwave heating compared to biocide applications in the devitalization of phototrophic communities colonizing rock engravings of Valle Camonica, UNESCO world heritage site, Italy

The devitalization of lithobionts prior to their removal from engraved rocks is a common conservation practice periodically undertaken in rock art sites. In this study, we assessed in situ the efficacy of three traditional biocides and of an innovative microwave heating system, and compared different application protocols to devitalize foliose and crustose lichens and a cyanobacteria-dominated biofilm on the rock engravings of Valle Camonica (UNESCO site n.94, Italy). The analysis of their vitality and stress responses by monitoring chlorophyll a fluorescence parameters (Fv/Fm, F0, OJIP transient) showed that the common application of biocides by brush is rather ineffective, particularly in the case of the resistant crustose lichens. The heating of rock surfaces to 70 degrees C for a few minutes by the microwave system caused devitalization of lithobionts to a similar extent as the biocide application with cellulose poultice, which, however, introduced high amounts of chemicals in the environment. The microwave irradiation overcame any lithobiontic stress resistance and avoided useless or excessive spread of biocides, appearing a promising sustainable approach for the parallel conservation of rock art and its surrounding natural environment.
Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users' satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.
Phelan-McDermid and general anesthesia with different hypnotics

Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disease, caused by an autosomal dominant mutation due to the terminal deletion of 22q13, leading to a defect in the SHANK3 protein. We present the clinical case of a 12-year-old patient with this syndrome, who underwent three interventions that required general anesthesia. In none of them did she present intraoperative or postoperative complications.
Explicit Lower Bounds on Strong Quantum Simulation

We consider the problem of classical strong (amplitude-wise) simulation of n-qubit quantum circuits, and identify a subclass of simulators we call monotone. This subclass encompasses almost all prominent simulation techniques. We prove an unconditional (i.e. without relying on any complexity-theoretic assumptions) and explicit (n - 2)(2(n-3) - 1) lower bound on the running time of simulators within this subclass. Assuming the Strong Exponential Time Hypothesis (SETH), we further remark that a universal simulator computing any amplitude to precision 2(-n) /2 must take at least 2(n-o(n)) time. We then compare strong simulators to existing SAT solvers, and identify the time-complexity below which a strong simulator would improve on state-of-the-art general SAT solving. Finally, we investigate Clifford+T quantum circuits with t T-gates. Using the sparsification lemma, we identify a time complexity lower bound of 2(2.2451x10-8)t below which a strong simulator would improve on state-of-the-art 3-SAT solving. This also yields a conditional exponential lower bound on the growth of the stabilizer rank of magic states.
A Low-Cost Surveillance and Information System for Museum Using Visible Light Communication

This paper designed and implemented a low-cost surveillance and information system for museums with two new coding schemes using visible light communication. For security, we use a key in place of the clock in the Manchester coding. For the enhanced data transfer, we use the unbalanced time duration for data one and zero. The prototype system was built with two low-cost microcontrollers, an LED, and linear light sensors. With 1-m distance in a dark environment, the maximum data transfer rate was roughly 1.93 kbits/s with a 10 Phi white LED. Under the dark-to-modest ambient brightness of up to 120 lux, the white LEDs were performed well in terms of the transmission distance, compared with LEDs with different colors. The experiments with flashlights reveal that our system can easily detect the prohibited use of flashlight. Experiments with the mobile phones' flashlight also revealed that photographing with the flashlight would have a worse impact on arts than the continuous illumination. We also measured the distance between arts to avoid inevitable interference between illumination sources in neighbor.
Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation

Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.
Tau interactome and RNA binding proteins in neurodegenerative diseases

Pathological tau aggregation is a primary neuropathological feature of many neurodegenerative diseases. Intriguingly, despite the common presence of tau aggregates in these diseases the affected brain regions, clinical symptoms, and morphology, conformation, and isoform ratio present in tau aggregates varies widely. The tau-mediated disease mechanisms that drive neurodegenerative disease are still unknown. Tau interactome studies are critically important for understanding tauopathy. They reveal the interacting partners that define disease pathways, and the tau interactions present in neuropathological aggregates provide potential insight into the cellular environment and protein interactions present during pathological tau aggregation. Here we provide a combined analysis of 12 tau interactome studies of human brain tissue, human cell culture models and rodent models of disease. Together, these studies identified 2084 proteins that interact with tau in human tissue and 1152 proteins that interact with tau in rodent models of disease. Our combined analysis of the tau interactome revealed consistent enrichment of interactions between tau and proteins involved in RNA binding, ribosome, and proteasome function. Comparison of human and rodent tau interactome studies revealed substantial differences between the two species. We also performed a second analysis to identify the tau interacting proteins that are enriched in neurons containing granulovacuolar degeneration or neurofibrillary tangle pathology. These results revealed a timed dysregulation of tau interactions as pathology develops. RNA binding proteins, particularly HNRNPs, emerged as early disease-associated tau interactors and therefore may have an important role in driving tau pathology.
Ligand-Functional Groups Induced Tuning MOFs' 2D into 1D Pore Channels for Pipeline Natural Gas Purification

The solvothermal reactions of CoCl2 ⋅ 6H2 O, 3,5-pyridinedicarboxylic acid (H2 L) and isonicotinic acid (HL1 )/3-amino isonicotinic acid (HL2 )/3-chloro isonicotinic acid (HL3 ) successfully led to three tfz-d topological pillar-layer [Co4 (μ-F)2 (COO)6 (NC5 H4 )4 ] cluster-based MOFs, namely, [Co4 (μ-F)2 (L)2 (L1 )2 ⋅ 2DMA] ⋅ DMA ⋅ 2H2 O (SNNU-Bai76, SNNU-Bai=Shaanxi Normal University Bai's group), [Co4 (μ-F)2 (L)2 (L2 )2 ⋅ 2H2 O] ⋅ 2DMA ⋅ 2H2 O (SNNU-Bai77) and [Co4 (μ-F)2 (L)2 (L3 )2 ⋅ 2H2 O] ⋅ 2DMF ⋅ 2H2 O (SNNU-Bai78). With the 2D pore channels in SNNU-Bai76 and SNNU-Bai77 being tuned to the 1D pore channel in SNNU-Bai78, C3 H8 and C2 H6 adsorption uptakes are apparently improved and the IAST selectivities of C3 H8 /CH4 and C2 H6 /CH4 almost remain, which indicate that SNNU-Bai78 may be one potential separation material for the pipeline natural gas purification. These were further confirmed by the breakthrough experiments for the simulated pipeline natural gas (C3 H8 /C2 H6 /CH4 : 5/10/85 gas mixture) of three isostructural MOFs. Furthermore, GCMC simulations revealed that due to one of the pore channels blocked by Cl atoms in a couple of 3-chloro isonicotinic acid with the changed conformation as the pillar, the pore wall of the formed 1D pore channel in SNNU-Bai78 may interact with the adsorbed C3 H8 or C2 H6 molecule more strongly, for which more atoms of framework at the new adsorption site will interact with the adsorbed gas molecule by more intermolecular interactions. This was also evidenced by the increased binding energies, being consistent with the tuning of adsorption enthalpies for C3 H8 and C2 H6 gas molecules, and the reduced C3 H8 and C2 H6 gas diffusion coefficients in SNNU-Bai78. Very interestingly, this work is the first example of finely tuning the pore connectivity of MOFs toward strengthened host-guest interactions for the gas adsorption and separation.
MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled.
An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons.
Aberrant Topological Properties of Brain Functional Network in Children with Obstructive Sleep Apnea Derived from Resting-State fMRI

To examine the difference in the topological properties of brain functional network between the children with obstructive sleep apnea (OSA) and healthy controls, and to explore the relationships between these properties and cognitive scores of OSA children. Twenty-four OSA children (6.5 ± 2.8 years, 15 males) and 26 healthy controls (8.0 ± 2.9 years, 11 males) underwent resting-state fMRI (rs-fMRI), based on which brain functional networks were constructed. We compared the global and regional topological properties of the network between OSA children and healthy controls. Partial correlation analysis was performed between topological properties and cognitive scores across OSA children. When comparing the OSA children with the healthy controls, lower full-scale intelligent quotient (FIQ) and verbal intelligent quotient (VIQ) were observed. Additionally, nodal degree centrality decreased in the bilateral anterior cingulate and paracingulate gyrus, but increased in the right middle frontal gyrus, the left fusiform gyrus, and the left supramarginal gyrus. Nodal efficiency decreased in the right precentral gyrus, and the bilateral anterior cingulate and paracingulate gyrus, but increased in the left fusiform gyrus. Nodal betweenness centrality increased in the dorsolateral part of the right superior frontal gyrus, the left fusiform gyrus, and the left supramarginal gyrus. Further, the nodal degree centrality in the left supramarginal gyrus was positively correlated with FIQ. In contrast, none of global topological properties showed difference between those two groups. The outcomes of OSA may impaired the regional topological properties of the brain functional network of OSA children, which may be potential neural mechanism underlying the cognitive declines of these patients.
Design, synthesis and anticancer evaluation of novel Se-NSAID hybrid molecules: Identification of a Se-indomethacin analog as a potential therapeutic for breast cancer

A total of twenty-five novel carboxylic acid, methylester, methylamide or cyano nonsteroidal anti-inflammatory drug (NSAID) derivatives incorporating Se in the chemical form of selenoester were reported. Twenty Se-NSAID analogs exhibited an increase in cytotoxic potency compared with parent NSAID scaffolds (aspirin, salicylic acid, naproxen, indomethacin and ketoprofen). Top five analogs were selected to further study their cytotoxicity in a larger panel of cancer cells and were also submitted to the DTP program of the NCI's panel of 60 cancer cell lines. Compounds 4a and 4d stood out with IC50 values below 10 μM in several cancer cells along with a selectivity index higher than 5 in breast cancer cells. Remarkably, analog 4d was found to inhibit cell growth notably in two breast cancer cell lines by inducing apoptosis, and to be metabolized to release the parent NSAID along with the Se fragment. Taken together, our results show that Se-NSAID analog 4d could be a potential chemotherapeutic drug for breast cancer.
Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography

White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. The proposed networks showed promising results, indicating they may significantly improve the efficiency of pre-surgical tractography mapping, without compromising its quality.
Can lateral mobility be restored along a highly domesticated low-energy gravel-bed river?

Fluvial engineering works such as weirs, rip-rap, groynes, and dykes have constrained for decades and more the lateral mobility of rivers, one of the key drivers of aquatic and riparian diversity. Preserving or restoring a sufficient space for river mobility has therefore become a major river management focus. Because the success and relevance of management actions are conditioned by the level of energy and sediment supply of rivers, such actions are generally considered unsuitable for low-energy rivers. However, some low-energy rivers have numerous ancient engineering works along their length, especially bank protections, suggesting a potential capacity for bed migration. In this context, it is essential to determine to what extent planform dynamics is disturbed, and if lateral mobility can be restored. Herein, a case study was done on a 146 km stretch of the low-energy meandering gravel-bed Cher River (France). The goal of the study was to estimate the remnant shifting capacity, identify the factors controlling the location and intensity of lateral erosion, determine the potential for preserving and restoring lateral mobility, and examine management measures that could be implemented to this end. For that, field surveys, analysis of existing databases, aerial photographs, and laser imaging detection and ranging digital elevation model (LiDAR DEM) data were combined. The study revealed a strong longitudinal fragmentation of the river, with most of it laterally constrained due to the presence of anthropogenic structures such as bank protections, former gravel pits in the alluvial plain, bridges, and weirs. The river is now composed of a string of constrained and unconstrained reaches, and the space available for river shifting has been dramatically reduced. Due to these fluvial engineering works and anthropogenic legacies, the potential for lateral movement of the riverbed, and, therefore, diversification of riparian and aquatic habitats, is limited. Furthermore, lateral mobility could be preserved or restored only for very short sections of the river. It is therefore highly unlikely that good ecological status could be achieved on the entire river corridor through removal of bank protections. Nevertheless, a possible solution could be combining bank protection removals with a series of gravel augmentations close to each other.