ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Influence of Polymer Characteristics on the Self-Assembly of Polymer-Grafted Metal-Organic Framework Particles

Polymer-grafted metal-organic frameworks (MOFs) can combine the properties of MOFs and polymers into a single, matrix-free composite material. Herein, we examine polymer-grafted MOF particles (using UiO-66 as a model system) to examine how the molecular weight, grafting density, and chemical functionality of the polymer graft affects the preparation of free-standing self-assembled MOF monolayers (SAMMs). The physical properties of the monolayers are influenced by the choice of polymer, and robust, flexible monolayers were achieved more readily with poly(methyl acrylate) when compared to poly(methyl methacrylate) or poly(benzyl methacrylate). Molecular dynamics simulations were carried out to provide insights into the orientation and ordering of MOFs in the monolayers with respect to MOF size, graft length, and hydrophobicity. The relationship between molecular weight and graft density of the polymer brush was investigated and related to polymer brush conformation, offering design rules for further optimizations to balance mechanical strength, MOF weight fraction, and processability for this class of hybrid materials.
Estimation of functional diversity and species traits from ecological monitoring data

The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data are harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence, should be reflected indirectly in its spatiotemporal distribution. Thus, it may be possible to reconstruct these traits from a sufficiently extensive monitoring dataset. Here, we use diffusion maps, a deterministic and de facto parameter-free analysis method, to reconstruct a proxy representation of the species' traits directly from monitoring data and use it to estimate functional diversity. We demonstrate this approach with both simulated data and real-world phytoplankton monitoring data from the Baltic Sea. We anticipate that wider application of this approach to existing data could greatly advance the analysis of changes in functional biodiversity.
An Automatic Learning-Based Framework for Robust Nucleus Segmentation

Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus segmentation is a prerequisite for various quantitative analyses including automatic morphological feature computation. However, it remains to be a challenging problem due to the complex nature of histopathology images. In this paper, we propose a learning-based framework for robust and automatic nucleus segmentation with shape preservation. Given a nucleus image, it begins with a deep convolutional neural network (CNN) model to generate a probability map, on which an iterative region merging approach is performed for shape initializations. Next, a novel segmentation algorithm is exploited to separate individual nuclei combining a robust selection-based sparse shape model and a local repulsive deformable model. One of the significant benefits of the proposed framework is that it is applicable to different staining histopathology images. Due to the feature learning characteristic of the deep CNN and the high level shape prior modeling, the proposed method is general enough to perform well across multiple scenarios. We have tested the proposed algorithm on three large-scale pathology image datasets using a range of different tissue and stain preparations, and the comparative experiments with recent state of the arts demonstrate the superior performance of the proposed approach.
Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR

In recent years, end-to-end approaches to automatic speech recognition have received considerable attention as they are much faster in terms of preparing resources. However, conventional multistage approaches, which rely on a pipeline of training hidden Markov models (HMM)-GMM models and tree-building steps still give the state-of-the-art results on most databases. In this study, we investigate flat-start one-stage training of neural networks using lattice-free maximum mutual information (LF-MMI) objective function with HMM for large vocabulary continuous speech recognition. We thoroughly look into different issues that arise in such a setup and propose a standalone system, which achieves word error rates (WER) comparable with that of the state-of-the-art multi-stage systems while being much faster to prepare. We propose to use full biphones to enable flat-start context-dependent (CD) modeling and show through experiments that our CD modeling approach can be almost as effective as regular tree-based CD modeling. We show that our flat-start LF-MMI setup together with this tree-free CD modeling technique achieves 10 to 25 % relative WER reduction compared to other end-to-end methods on well-known databases. The improvements are larger for smaller databases.
BioThreads: A Novel VLIW-Based Chip Multiprocessor for Accelerating Biomedical Image Processing Applications

We discuss BioThreads, a novel, configurable, extensible system-on-chip multiprocessor and its use in accelerating biomedical signal processing applications such as imaging photoplethysmography (IPPG). BioThreads is derived from the LE1 open-source VLIW chip multiprocessor and efficiently handles instruction, data and thread-level parallelism. In addition, it supports a novel mechanism for the dynamic creation, and allocation of software threads to uncommitted processor cores by implementing key POSIX Threads primitives directly in hardware, as custom instructions. In this study, the BioThreads core is used to accelerate the calculation of the oxygen saturation map of living tissue in an experimental setup consisting of a high speed image acquisition system, connected to an FPGA board and to a host system. Results demonstrate near-linear acceleration of the core kernels of the target blood perfusion assessment with increasing number of hardware threads. The BioThreads processor was implemented on both standard-cell and FPGA technologies; in the first case and for an issue width of two, full real-time performance is achieved with 4 cores whereas on a mid-range Xilinx Virtex6 device this is achieved with 10 dual-issue cores. An 8-core LE1 VLIW FPGA prototype of the system achieved 240 times faster execution time than the scalar Microblaze processor demonstrating the scalability of the proposed solution to a state-of-the-art FPGA vendor provided soft CPU core.
3-D object retrieval using topic model

In the last few years, extensive effort has been spent to develop better performed 3-D object retrieval methods. View-based methods have attracted a significant amount of attention, not only because their state-of-art performance, but also they merely require some of a 3-D object's 2-D view images. However, most recent approaches only deal with the images' primordial-extracted features and ignore their hidden relationships. Considering these latent characters, a visual-topic-model 3-D object retrieval approach is introduced in this paper. In this framework, dense scale invariant feature transform(dense-SIFT) descriptors are extracted from a set of views of each 3-D object, and all the dense-SIFT descriptors are grouped into bag-of-word features using k-means clustering. Then, the topic distribution of a 3-D object is generated via latent dirichlet allocation (LDA) given its bag-of-word features. Gibbs sampling is applied in the learning and inference processing of LDA. We conduct experiments on the Princeton Shape Benchmark (PSB) and National Taiwan University 3D model database (NTU), and the experimental results demonstrate that the proposed method can achieve better retrieval effectiveness than the state-of-the-art methods under several standard evaluation measures.
A plant-based mutant huntingtin model-driven discovery of impaired expression of GTPCH and DHFR

Pathophysiology associated with Huntington's disease (HD) has been studied extensively in various cell and animal models since the 1993 discovery of the mutant huntingtin (mHtt) with abnormally expanded polyglutamine (polyQ) tracts as the causative factor. However, the sequence of early pathophysiological events leading to HD still remains elusive. To gain new insights into the early polyQ-induced pathogenic events, we expressed Htt exon1 (Httex1) with a normal (21), or an extended (42 or 63) number of polyQ in tobacco plants. Here, we show that transgenic plants accumulated Httex1 proteins with corresponding polyQ tracts, and mHttex1 induced protein aggregation and affected plant growth, especially root and root hair development, in a polyQ length-dependent manner. Quantitative proteomic analysis of young roots from severely affected Httex1Q63 and unaffected Httex1Q21 plants showed that the most reduced protein by polyQ63 is a GTP cyclohydrolase I (GTPCH) along with many of its related one-carbon (C1) metabolic pathway enzymes. GTPCH is a key enzyme involved in folate biosynthesis in plants and tetrahydrobiopterin (BH4) biosynthesis in mammals. Validating studies in 4-week-old R6/2 HD mice expressing a mHttex1 showed reduced levels of GTPCH and dihydrofolate reductase (DHFR, a key folate utilization/alternate BH4 biosynthesis enzyme), and impaired C1 and BH4 metabolism. Our findings from mHttex1 plants and mice reveal impaired expressions of GTPCH and DHFR and may contribute to a better understanding of mHtt-altered C1 and BH4 metabolism, and their roles in the pathogenesis of HD.
DYANOM-Dykstra's projection based atomic norm solver

In this paper, we propose a fast monotonic algorithm named DYANOM to find the optimal minimizer of the atomic norm minimization problem (ANM), which has extensive applications ranging from spectral estimation, direction of arrival estimation to system identification. DYANOM is faster as well as more accurate than the state-of-the-art methods and enjoys monotonicity property. We present numerical simulations in the context of frequency estimation and compare its numerical performance with the stateof-the-art methods. (c) 2020 Elsevier B.V. All rights reserved.
Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning

Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and state-of-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets.
Comparison of three techniques for analysis of data from an Aerosol Time-of-Flight Mass Spectrometer

The Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) is one of few instruments able to measure the size and mass spectra of individual airborne particles with high temporal resolution. Data analysis is challenging and in the present study, we apply three different techniques (PMF. ART-2a and K-means) to a regional ATOFMS dataset collected at Harwell, UK. For the first time, Positive Matrix Factorization (PMF) was directly applied to single particle mass spectra as opposed to clusters already generated by the other methods. The analysis was performed on a total of 56,898 single particle mass spectra allowing the extraction of 10 factors, their temporal trends and size distributions, named CNO-COOH (cyanide, oxidized organic nitrogen and carboxylic acids), SUL (sulphate), NH4-OOA (ammonium and oxidized organic aerosol), NaCl, EC+ (elemental carbon positive fragments), OC-Arom (aromatic organic carbon), EC- (elemental carbon negative fragments), K (potassium), NIT (nitrate) and OC-CHNO (organic nitrogen). The 10 factor solution from single particle PMF analysis explained 45% of variance of the total dataset, but the factors are well defined from a chemical point of view. Different EC and OC components were separated: fresh EC (factor EC) from aged EC (factor EC+) and different organic families (factors NH4-OOA, OC-Arom, OC CHNO and CNO-COOH). A comparison was conducted between PMF, K-means cluster analysis and the ART-2a artificial neural network. K-means and ART-2a give broadly overlapping results (with 9 clusters, each describing the full composition of a particle type), while PMF, by effecting spectral deconvolution, was able to extract and separate the different chemical species contributing to particles, but loses some information on internal mixing. Relationships were also examined between the estimated volumes of ATOFMS PMF factors and species concentrations measured independently by GRAEGOR and AMS instruments, showing generally moderate to strong correlations. (C) 2012 Elsevier Ltd. All rights reserved.
Analysis Operator Learning and its Application to Image Reconstruction

Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this paper, we present an algorithm for learning an analysis operator from training images. Our method is based on l(p)-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constraints. Moreover, we compare our approach to state-of-the-art methods for image denoising, inpainting, and single image super-resolution. Our numerical results show competitive performance of our general approach in all presented applications compared to the specialized state-of-the-art techniques.
Pharmacological evaluation of newly synthesized benzimidazole derivative for anti-Alzheimer potential

Backgound: Alzheimer disease (AD) is a disastrous disease characterized by accretion of amyloid-beta plaques, neurofibrillary tangles inducing oxidative stress, loss of neuronal functions and continuous progression of cognitive impairment leading to severe dementia.Material and Methods: The newly synthesized benzimidazole derivative 4-chloro-3-(2-phenyl-1H-benzimidazole-1-sulfonyl) benzoic acid (CB) was evaluated for its anti-Alzheimer activity using in silico, in vivo, in vitro and molecular techniques (ELISA, WB & IHC).Results: In-silico studies revealed that CB has atomic contact energy values of -3.9 to -8.9 kcal/mol against selected targets. In vitro assay showed that CB caused acetylcholinesterase (AChE) and diphenyl-1-picrylhydrazyl inhibition. In-vivo findings revealed improvement in dementia as observed in the morris water maze test and Ymaze test. Amyloid-beta disaggregation, increased level of anti-oxidants, decreased expressions of inflammatory markers and enhanced cellular architecture were found in the cortex and hippocampus of treated rats in the histopathological examination, immunohistochemistry analysis, enzyme-linked immunosorbent assay and western blot analysis.Conclusions: This study revealed that CB possess different binding affinities with the Alzheimer-related targets and it possess anti-Alzheimer activity, mediated via AChE and amyloid-beta inhibition, anti-oxidant and anti-inflammatory pathways.
Improving the Performance and Energy Efficiency of GPGPU Computing through Integrated Adaptive Cache Management

Hardware caches are widely employed in GPGPUs to achieve higher performance and energy efficiency. Incorporating hardware caches in GPGPUs, however, does not immediately guarantee enhanced performance and energy efficiency due to high cache contention and thrashing. To address the inefficiency of GPGPU caches, various adaptive techniques (e.g., warp limiting) have been proposed. However, relatively little work has been done in the context of creating an architectural framework that tightly integrates adaptive cache management techniques and investigating their effectiveness and interaction. To bridge this gap, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. IACM integrates the state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1 and 61.9 percent on average, respectively), achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4 percent at maximum and 7.7 percent on average), and delivers significant performance and energy-efficiency gains over the baseline GPGPU architecture enhanced with advanced architectural technologies.
Ultra-fast, High-Bandwidth Coherent cw THz Spectrometer for Non-destructive Testing

Continuous wave THz (cw THz) systems define the state-of-the-art in terms of spectral resolution in THz spectroscopy. Hitherto, acquisition of broadband spectra in a cw THz system was always connected with slow operation. Therefore, high update rate applications like inline process monitoring and non-destructive testing are served by time domain spectroscopy (TDS) systems. However, no fundamental restriction prevents cw THz technology from achieving faster update rates and be competitive in this field. In this paper, we present a fully fiber-coupled cw THz spectrometer. Its sweep speed is two orders of magnitude higher compared to commercial state-of-the-art systems and reaches a record performance of 24 spectra per second with a bandwidth of more than 2THz. In the single-shot mode, the same system reaches a peak dynamic range of 67dB and exceeds a value of 100dB with averaging of 7min, which is among the highest values ever reported. The frequency steps can be as low as 40MHz. Due to the fully homodyne detection, each spectrum contains full amplitude and phase information. This demonstration of THz-spectroscopy at video-rate is an essential step towards applying cw THz systems in non-destructive, in line testing.
RNA interference (RNAi)-based therapeutics for treatment of rare neurologic diseases

Advances in genome sequencing have greatly facilitated the identification of genomic variants underlying rare neurodevelopmental and neurodegenerative disorders. Understanding the fundamental causes of rare monogenic disorders has made gene therapy a possible treatment approach for these conditions. RNA interference (RNAi) technologies such as small interfering RNA (siRNA), microRNA (miRNA), and short hairpin RNA (shRNA), and other oligonucleotide-based modalities such as antisense oligonucleotides (ASOs) are being developed as potential therapeutic approaches for manipulating expression of the genes that cause a variety of neurological diseases. Here, we offer a brief review of the mechanism of action of these RNAi approaches; provide deeper discussion of the advantages, challenges, and specific considerations related to the development of RNAi therapeutics for neurological disease; and highlight examples of rare neurological diseases for which RNAi therapeutics hold great promise.
Investigation of murine host sex as a biological variable in epithelial barrier function and muscle contractility in human intestinal organoids

Intestinal failure (IF) occurs when intestinal surface area or function is not sufficient to support digestion and nutrient absorption. Human intestinal organoid (HIO)-derived tissue-engineered intestine is a potential cure for IF. Research to date has demonstrated successful HIO transplantation (tHIO) into mice with significant in vivo maturation. An area lacking in the literature is exploration of murine host sex as a biological variable (SABV) in tHIO function. In this study, we investigate murine host SABV in tHIO epithelial barrier function and muscle contractility. HIOs were generated in vitro and transplanted into nonobese diabetic, severe combined immunodeficiency gamma chain deficient male and female mice. tHIOs were harvested after 8-12 weeks in vivo. Reverse transcriptase polymerase chain reaction and immunohistochemistry were conducted to compare tight junctions and contractility-related markers in tHIOs. An Ussing chamber and contractility apparatus were used to evaluate tHIO epithelial barrier and muscle contractile function, respectively. The expression and morphology of tight junction and contractility-related markers from tHIOs in male and female murine hosts is not significantly different. Epithelial barrier function as measured by transepithelial resistance, short circuit current, and fluorescein isothiocyanate-dextran permeability is no different in tHIOs from male and female hosts, although these results may be limited by HIO epithelial immaturity and a short flux time. Muscle contractility as measured by total contractile activity, amplitude, frequency, and tension is not significantly different in tHIOs from male and female hosts. The data suggest that murine host sex may not be a significant biological variable influencing tHIO function, specifically epithelial barrier maintenance and muscle contractility, though limitations exist in our model.