ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Genome report: chromosome-level draft assemblies of the snow leopard, African leopard, and tiger (Panthera uncia, Panthera pardus pardus, and Panthera tigris)

The big cats (genus Panthera) represent some of the most popular and charismatic species on the planet. Although some reference genomes are available for this clade, few are at the chromosome level, inhibiting high-resolution genomic studies. We assembled genomes from 3 members of the genus, the tiger (Panthera tigris), the snow leopard (Panthera uncia), and the African leopard (Panthera pardus pardus), at chromosome or near-chromosome level. We used a combination of short- and long-read technologies, as well as proximity ligation data from Hi-C technology, to achieve high continuity and contiguity for each individual. We hope that these genomes will aid in further evolutionary and conservation research of this iconic group of mammals.
The Factory of Blood Production: Hematopoietic Stem Cells

Hematopoietic stem cells (HSCs) support the lifelong production of hundreds of billions of blood cells per day. This unique, incredible ability of HSCs also creates an incredible therapeutic potential for patients. To advance this potential, effective methods to study HSCs are continually evolving. This chapter summarizes the variety of protocols and techniques covered in this book used to evaluate HSCs - modification, characterization, interaction with their niche, and in vivo function.
In Situ Separator Modification with an N-Rich Conjugated Microporous Polymer for the Effective Suppression of Polysulfide Shuttle and Li Dendrite Growth

Lithium-sulfur (Li-S) batteries are very promising high-energy-density electrochemical energy storage devices, but suffer from serious Li polysulfide (LiPS) shuttle and uncontrollable Li dendrite growth. Here, we show in situ polyolefin separator modification with an N-rich conjugated microporous polymer (NCMP) for advanced Li-S battery. In situ polymerization generates an ultrathin NCMP coating on the whole external surface and the internal surface of the separator, which is substantially different from the conventional approaches with thick coatings only on the external surface. The NCMP coating with abundant N-containing groups (-NH2 and -N═), uniform nanopores (12.294 Å), and π-conjugated structure can simultaneously inhibit LiPS shuttle and regulate uniform nucleation and growth of Li dendrites. Consequently, the NCMP-based separator endows the Li-S battery with significantly enhanced cycling stability at high S loading (5.4 mg cm-2), lean electrolyte (E/S = 6.3 μL mg-1), and limited Li excess (50 μm).
A Genome-Wide CRISPR-Cas9 Loss-of-Function Screening to Identify Host Restriction Factors Modulating Oncolytic Virotherapy

Oncolytic virotherapy represents an efficient immunotherapeutic approach for cancer treatment. Oncolytic viruses (OVs) promote antitumor responses through tumor-selective cell lysis and immune system activation. However, some tumor cell lines and primary tumors display resistance to therapy. Here we describe a protocol to identify novel host factors responsible for tumor resistance to oncolysis using an unbiased genome-wide CRISPR-Cas9 loss-of-function screening. Cas9-expressing tumor cells are transduced with a library of pooled single-guide RNA (sgRNA)-expressing lentiviruses that target all human genes to obtain a cell population where each cell is knocked out for a single gene. Upon OV infection, resistant cells survive, while sensitive cells die. The relative abundance of each genome-integrated sgRNA is measured by next-generation sequencing (NGS) in resistant and control cells. This protocol is amenable to uncover host factors involved in the resistance to different OVs in multiple tumor models.
Energy- and time-efficient matrix multiplication on FPGAs

We develop new algorithms and architectures for matrix multiplication on configurable devices. These have reduced energy dissipation and latency compared with the state-of-the-art field-programmable gate array (FPGA)-based designs. By profiling well-known designs, we identify "energy hot spots," which are responsible for most of the energy dissipation. Based on this, we develop algorithms and architectures that offer tradeoffs among the number of I/O ports, the number of registers, and the number of PEs. To avoid time-consuming low-level simulations for energy profiling and performance prediction of many alternate designs, we derive functions to represent the impact of algorithm design choices on the system-wide energy dissipation, area, and latency. These functions are used to either optimize the energy performance or provide tradeoffs for a family of candidate algorithms and architectures. For selected designs, we perform extensive low-level simulations using state-of-the-art tools and target FPGA devices. We show a design space for matrix multiplication on FPGAs that results in tradeoffs; among energy, area, and latency. For example, our designs improve the energy performance of state-of-the-art FPGA-based designs by 29%-51% without any increase in the area-latency product. The latency of our designs is reduced one-third to one-fifteenth while area is increased 1.9-9.4 times. In terms of comprehensive metrics such as Energy-Area-Time, our designs exhibit superior performance compared with the state-of-the-art by 50%-79%.
A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

Large-scale 3D shape retrieval has become an important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale comprehensive and sketch-based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large-scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain-specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query-by-Model or Query-by-Sketch 3D shape retrieval methods and to solicit state-of-the-art approaches, we perform a more comprehensive comparison of twenty-six (eighteen originally participating algorithms and eight additional state-of-the-art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites (C) 2014 Elsevier Inc. All rights reserved.
Learning Mesh Representations via Binary Space Partitioning Tree Networks

Polygonal meshes are ubiquitous, but have only played a relatively minor role in the deep learning revolution. State-of-the-art neural generative models for 3D shapes learn implicit functions and generate meshes via expensive iso-surfacing. We overcome these challenges by employing a classical spatial data structure from computer graphics, Binary Space Partitioning (BSP), to facilitate 3D learning. The core operation of BSP involves recursive subdivision of 3D space to obtain convex sets. By exploiting this property, we devise BSP-Net, a network that learns to represent a 3D shape via convex decomposition without supervision. The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built over a set of planes, where the planes and convexes are both defined by learned network weights. BSP-Net directly outputs polygonal meshes from the inferred convexes. The generated meshes are watertight, compact (i.e., low-poly), and well suited to represent sharp geometry. We show that the reconstruction quality by BSP-Net is competitive with those from state-of-the-art methods while using much fewer primitives.We also explore variations to BSP-Net including using a more generic decoder for reconstruction, more general primitives than planes, as well as training a generative model with variational auto-encoders.
An Improved Weighted Nuclear Norm Minimization Method for Image Denoising

Patch-based low rank matrix approximation has shown great potential in image denoising. Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising performance. For each local patch in an image, the WNNM method groups nonlocal similar patches by block matching to formulate a low-rank matrix. However, the WNNM often chooses irrelevant patches such that it may lose fine details of the image, resulting in undesirable artifacts in the final reconstruction. In this regards, this paper aims to provide a denoising algorithm which further improves the performance of the WNNM method. For this purpose, we develop a new nonlocal similarity measure by exploiting both pixel intensities and gradients and present a filter that enhances edge information in a patch to improve the performance of low rank approximation. The experimental results on widely used test images demonstrate that the proposed denoising algorithm performs better than other state-of-the-art denoising algorithms in terms of PSNR and SSIM indices as well as visual quality.
Bilateral atraumatic femoral neck fractures resulting from transient osteoporosis of the hip

A woman in her mid-30s presented to the orthopaedic team, unable to mobilise, shortly after her caesarean section. On questioning, she reported 10 weeks of atraumatic right hip pain. A radiograph revealed a displaced right subcapital neck of femur fracture. An MRI confirmed this, as well as identifying a minimally displaced left subcapital neck of femur fracture. She underwent a right total hip replacement and internal fixation of the left hip. A dual energy X-ray absorptiometry (DEXA) scan showed severe osteoporosis, and a diagnosis of transient osteoporosis of the hip was made. She was seen by the bone metabolism team and given calcium and vitamin D medication. Although atraumatic hip fractures are rare in young patients, disproportionate or persisting hip pain in pregnant patients should raise the index of suspicion and prompt further investigation in the form of an MRI. This will allow timely management of hip fractures and improve patient outcomes.
Development of a Linear Piezoelectric Microactuator Inspired by the Hollowing Art

Microactuators have been playing crucial roles in micromachine technologies because of their small sizes and light weights. The piezoelectric actuation is one of the most important driving methods to perform the motion output with miniature structures. The rotary piezoelectric microactuators are relatively mature, whereas the linear ones need to be further developed. Inspired by the hollowing art, a novel linear piezoelectric microactuator with miniature dimensions of 7.4 mm x 7.4 mm x 10 mm is proposed, which is designed to be an integrated hollow structure without a guide rail and can achieve bidirectional standing-wave drive. The bidirectional motions are achieved by the same first-order bending vibrations of two pairs of thin-walled beams, respectively, which are conducive to the consistency of the forward and backward motions indicate that the maximum velocity, resolution, and maximum thrust force achieved in the forward motion are 19.06 mm/s, 0.3 mu m, and 17.45 mN, respectively, whereas those in the backward motion are 16.92 mm/s, 0.45 mu m, and 15.28 mN, respectively. The design inspired by the hollowing art has the potential to be an effective and alternative solution for the development of linear piezoelectric microactuators.
State of the art of advanced solar control devices for buildings

This paper deals with the state of the art of advanced solar control devices for buildings, with the comparative evaluation of solar-control systems and with guidelines for the development of new solar control systems. It includes multifunctional systems with building integrated photovoltaic (BIPV) and/or building integrated solar thermal (BIST) energy conversion. In order to facilitate and to structure the understanding of solar control systems two multidimensional spaces are introduced: the design space and the evaluation space. The design space contains the design parameters which are to be selected by the designer when solar control devices are to be chosen for specific buildings or when new systems are to be developed, such us the color of the slat of a venetian blind or the fraction of holes (direct transmittance) of a fabric for a roller blind. The evaluation space contains the performance parameters or evaluation criteria, which indicate the design's ability to satisfy the functional and aesthetic requirements, such us passive solar gain control or visual comfort. All the design parameters and evaluation criteria are explained in detail in the paper. A chapter with examples of advanced solar control systems completes the overview of the state of the art of solar control systems. (C) 2017 The Author. Published by Elsevier Ltd.
Millimeter-Wave Imaging With Accelerated Super-Resolution Range Migration Algorithm

Millimeter-wave imaging systems are increasingly used in human screening at checkpoints, in linear or multi-input multi-output (MIMO) array modes. Rather than pressing intrinsic system requirements to improve image quality, it seems more economical to appeal to some specific approaches, for example, super-resolution (SR) techniques. In this paper, as a variation of SR, coherence factor (CF) is incorporated in the framework of the range migration algorithm (RMA), termed CF-RMA for short. As a critical part of CF, the double integral in the wavenumber domain is approximated by a single integral in the sense of the point-matching method, resulting in a particular formulation to carry on another independent RMA program. Being computationally efficient, the proposed method is shown to be effective in suppressing sidelobes and background noise. The resolution (as gauged by the width of the main lobe of the point spread function) is enhanced as well. A structural similarity (SSIM) index is employed to quantify the image quality enhancement with respect to state-of-the-art SR approaches. In terms of computation load, the new approach is shown to be much more efficient than state-of-the-art SR approaches.
Techno-economic comparison of membrane distillation and MVC in a zero liquid discharge application

Membrane distillation (MD) is a thermally driven membrane process for the separation of vapour from a liquid stream through a hydrophobic, microporous membrane. However, a commercial breakthrough on a large scale has not been achieved so far. Specific developments on MD technology are required to adapt the technology for applications in which its properties can potentially outshine state of the art technologies such as standard evaporation. In order to drive these developments in a focused manner, firstly it must be shown that MD can be economically attractive in comparison to state of the art systems. Thus, this work presents a technological design and economic analysis for AGMD and v-AGMD for application in a zero liquid discharge (ZLD) process chain and compares it to the costs of mechanical vapour compression (MVC) for the same application. The results show that MD can potentially be similar to 40% more cost effective than MVC for a system capacity of 100 m(3)/day feed water, and up to similar to 75% more cost effective if the MD is driven with free waste heat.
Litter Detection with Deep Learning: A Comparative Study

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.
Medial Spheres for Shape Approximation

We study the problem of approximating a 3D solid with a union of overlapping spheres. In comparison with a state-of-the-art approach, our method offers more than an order of magnitude speedup and achieves a tighter approximation in terms of volume difference with the original solid while using fewer spheres. The spheres generated by our method are internal and tangent to the solid's boundary, which permits an exact error analysis, fast updates under local feature size preserving deformation, and conservative dilation. We show that our dilated spheres offer superior time and error performance in approximate separation distance tests than the state-of-the-art method for sphere set approximation for the class of (sigma, theta)-fat solids. We envision that our sphere-based approximation will also prove useful for a range of other applications, including shape matching and shape segmentation.
Characterization of the Catalytic and Nucleotide Binding Properties of the α-Kinase Domain of Dictyostelium Myosin-II Heavy Chain Kinase A

The α-kinases are a widely expressed family of serine/threonine protein kinases that exhibit no sequence identity with conventional eukaryotic protein kinases. In this report, we provide new information on the catalytic properties of the α-kinase domain of Dictyostelium myosin-II heavy chain kinase-A (termed A-CAT). Crystallization of A-CAT in the presence of MgATP yielded structures with AMP or adenosine in the catalytic cleft together with a phosphorylated Asp-766 residue. The results show that the β- and α-phosphoryl groups are transferred either directly or indirectly to the catalytically essential Asp-766. Biochemical assays confirmed that A-CAT hydrolyzed ATP, ADP, and AMP with kcat values of 1.9, 0.6, and 0.32 min(-1), respectively, and showed that A-CAT can use ADP to phosphorylate peptides and proteins. Binding assays using fluorescent 2'/3'-O-(N-methylanthraniloyl) analogs of ATP and ADP yielded Kd values for ATP, ADP, AMP, and adenosine of 20 ± 3, 60 ± 20, 160 ± 60, and 45 ± 15 μM, respectively. Site-directed mutagenesis showed that Glu-713, Leu-716, and Lys-645, all of which interact with the adenine base, were critical for nucleotide binding. Mutation of the highly conserved Gln-758, which chelates a nucleotide-associated Mg(2+) ion, eliminated catalytic activity, whereas loss of the highly conserved Lys-722 and Arg-592 decreased kcat values for kinase and ATPase activities by 3-6-fold. Mutation of Asp-663 impaired kinase activity to a much greater extent than ATPase, indicating a specific role in peptide substrate binding, whereas mutation of Gln-768 doubled ATPase activity, suggesting that it may act to exclude water from the active site.
A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have recently been proposed for automatic glaucoma detection based on fundus images. However, none of the existing approaches can efficiently remove high redundancy in fundus images for glaucoma detection, which may reduce the reliability and accuracy of glaucoma detection. To avoid this disadvantage, this paper proposes an attention-based convolutional neural network (CNN) for glaucoma detection, called AG-CNN. Specifically, we first establish a large-scale attention-based glaucoma (LAG) database, which includes 11 760 fundus images labeled as either positive glaucoma (4878) or negative glaucoma (6882). Among the 11 760 fundus images, the attention maps of 5824 images are further obtained from ophthalmologists through a simulated eye-tracking experiment. Then, a new structure of AG-CNN is designed, including an attention prediction subnet, a pathological area localization subnet, and a glaucoma classification subnet. The attention maps are predicted in the attention prediction subnet to highlight the salient regions for glaucoma detection, under a weakly supervised training manner. In contrast to other attention-based CNN methods, the features are also visualized as the localized pathological area, which are further added in our AG-CNN structure to enhance the glaucoma detection performance. Finally, the experiment results from testing over our LAG database and another public glaucoma database show that the proposed AG-CNN approach significantly advances the state-of-the-art in glaucoma detection.
A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction

This paper presents a preconditioned Krasnoselskii-Mann (KM) algorithm with an improved EM preconditioner (IEM-PKMA) for higher-order total variation (HOTV) regularized positron emission tomography (PET) image reconstruction. The PET reconstruction problem can be formulated as a three-term convex optimization model consisting of the Kullback-Leibler (KL) fidelity term, a nonsmooth penalty term, and a nonnegative constraint term which is also nonsmooth. We develop an efficient KM algorithm for solving this optimization problem based on a fixed-point characterization of its solution, with a preconditioner and a momentum technique for accelerating convergence. By combining the EM precondtioner, a thresholding, and a good inexpensive estimate of the solution, we propose an improved EM preconditioner that can not only accelerate convergence but also avoid the reconstructed image being "stuck at zero." Numerical results in this paper show that the proposed IEM-PKMA outperforms existing state-of-the-art algorithms including, the optimization transfer descent algorithm and the preconditioned L-BFGS-B algorithm for the differentiable smoothed anisotropic total variation regularized model, the preconditioned alternating projection algorithm, and the alternating direction method of multipliers for the nondifferentiable HOTV regularized model. Encouraging initial experiments using clinical data are presented.
Local oppugnant color space extrema patterns for content based natural and texture image retrieval

This paper proposes a novel feature descriptor called local oppugnant color space extrema patterns (LOCSEP) for image indexing retrieval. The existing directional local extrema pattern (DLEP) extracts the directional edge information based on local extrema in 0 degrees, 45 degrees, 90 degrees, and 135 degrees directions in an image. The proposed method integrates the concepts of color and texture features. First, the color image is converted into RGB (red, green and blue) and HSV (hue, saturation and value) color spaces. Then oppugnant color spaces, RV, GV, BV are used for the extract of oppugnant DLEP features (LOCSEP). The performance of the proposed method is tested by conducting two experiments on benchmark databases viz. Corel-1K and MIT VisTex databases. The performance of the proposed method is compared with the state-of-the-art methods for image retrieval and face recognition applications in terms of average retrieval precision (ARP) and average retrieval rate (ARR). The results after being investigated show a significant improvement in terms of their evaluation measures as compared to state-of-the-art methods on respective databases. (C) 2014 Elsevier GmbH. All rights reserved.