ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Single- and Mutiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes

In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The electrostatic energy minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi-shell schemes, called generalized EEM (GEEM). GEEM has been successfully used in the human connectome project. However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called spherical code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called iterative maximum overlap construction (IMOC), an 1-Opt greedy method, a mixed integer linear programming method, and a constrained non-linear optimization method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state-of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool.
Application of fuzzy ART neural network to intelligent gas sensor arrays

In this paper a new approach to gas identification using Fuzzy Adaptive Resonance Theory (ART) neural network and polymer coated surface acoustic wave (SAW) sensors array is reported. This method provides fast learning capability in the training mode and accurate gas classification in the sniffing mode. The method can be easily extended to gas mixtures as well as other sensor technologies. Since the Fuzzy ART neural network is self organizing classifier trained in unsupervised mode, it avoids the drawbacks associated with static feed forward neural networks.
Water-Triggered Chemical Transformation of Perovskite Nanocrystals

Recently emerged lead-halide perovskite nanocrystals (PNCs) are promising optoelectronic material due to their easy solution processability, wide range of color tunability, as well as very high photoluminescence quantum yield. Despite their significant success in lab-scale optoelectronic applications, the long-term stability becomes the main issue, hindering them towards commercialization. The highly ionic nature of such lead-halide structure makes them extremely unstable in water and air. But a very few groups have taken the advantage of such nature of the crystal structure for water-triggered chemical transformation towards shape, composition, and morphology controlled stable and bright PNCs, which are otherwise difficult to obtain by typical direct approach. Furthermore, using polymer as an encapsulating layer for the PNCs, water-soluble stable PNCs have been prepared. In this review, the recent progress on the water-hexane interface chemistry towards chemical transformation to produce several PNCs is described. Such method not only ensure to yield several shape-controlled perovskites nanocrystals, but also formation of perovskites in aqueous phase that show promising application towards bio-imaging.
AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.
Arithmetic in the signing brain: Differences and similarities in arithmetic processing between deaf signers and hearing non-signers

Deaf signers and hearing non-signers have previously been shown to recruit partially different brain regions during simple arithmetic. In light of the triple code model, the differences were interpreted as relating to stronger recruitment of the verbal system of numerical processing, that is, left angular and inferior frontal gyrus, in hearing non-signers, and of the quantity system of numerical processing, that is, right horizontal intraparietal sulcus, for deaf signers. The main aim of the present study was to better understand similarities and differences in the neural correlates supporting arithmetic in deaf compared to hearing individuals. Twenty-nine adult deaf signers and 29 hearing non-signers were enrolled in an functional magnetic resonance imaging study of simple and difficult subtraction and multiplication. Brain imaging data were analyzed using whole-brain analysis, region of interest analysis, and functional connectivity analysis. Although the groups were matched on age, gender, and nonverbal intelligence, the deaf group performed generally poorer than the hearing group in arithmetic. Nevertheless, we found generally similar networks to be involved for both groups, the only exception being the involvement of the left inferior frontal gyrus. This region was activated significantly stronger for the hearing compared to the deaf group but showed stronger functional connectivity with the left superior temporal gyrus in the deaf, compared to the hearing, group. These results lend no support to increased recruitment of the quantity system in deaf signers. Perhaps the reason for performance differences is to be found in other brain regions not included in the original triple code model.
ZoneDefense: A Fault-Tolerant Routing for 2-D Meshes Without Virtual Channels

Fault-tolerant routing is usually used to provide reliable on-chip communication for many-core processors. This paper focuses on a special class of algorithms that do not use virtual channels. One of the major challenges is to keep the network deadlock free in the presence of faults, especially those locating on network edges. State-of-the-art solutions address this problem by either disabling all nodes of the faulty network edges or including all faults into one faulty block. Therefore, a large number of fault-free nodes will be sacrificed. To address this problem, the proposed ZoneDefense routing not only includes faults into convex faulty blocks but also spreads the faulty blocks' position information in corresponding columns. The nodes, which know the position of faulty blocks, form the defense zones. Therefore, packets can find the faulty blocks and route around them in advance. Exploiting the defense zones, the proposed ZoneDefense routing could tolerate many more faults with significantly reduced sacrificed fault-free nodes compared with the state-of-the-art algorithms. Furthermore, the ZoneDefense routing does not degrade the network performance in the absence of faults, and could get similar performance as its counterparts in the presence of faults.
Ask the River: A Public Art and Placemaking Project

Intertwining people and place, Ask the River is a public art and placemaking project reconnecting the Brattleboro, Vermont community to the nearby Connecticut River. Ask the River is guided by the Abenaki understanding that people and place are one: we are the River, the River is us. Three artists and multiple community partners create public art for community-a riverscape of shimmering kinetic sculpture spanning the town's parking garage-and in community, in workshops using the hands-on, accessible and engaging process of cyanotype. The River is inspiration and guide. The purpose of Ask the River is for the Brattleboro community to explore seeing ourselves as a part of, not separate from, the River: to create pathways of reconnection, to enable us to hear its voice.
A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended.
A low-cost technique for biodiesel production in Ankistrodesmus sp. EHY by using harvested microalgal effluent

The present study aims to use Ankistrodesmus sp. EHY to develop a viable and economic lipid production strategy using recycling of harvested microalgal effluent. In comparison to the control, the highest lipid content (52.4 %) and productivity (250.72 mg L-1 d-1) were achieved when 40 % recycled medium was used. Consistent with the trend of lipid accumulation, the six key lipogenetic genes were upregulated, as well as reactive oxygen species (ROS), glutathione (GSH) and genes encoding antioxidant enzymes during cultivation in recycled medium. Moreover, the consumption of dissolved organic carbon (DOC) and the increased humic acid (HA) in the recycled medium might also be associated with lipid biosynthesis. The biodiesel parameters of alga biomass-derived lipids were fitted to the standard of commercial biodiesel. In conclusion, this study offers an economically viable strategy for microalgal biofuel production and wastewater treatment using recycling of harvested microalgal effluent.
Demonstration of bacterium-free very rapid reverse genetics system using mumps virus

The reverse genetics system is a very powerful tool for analyzing the molecular mechanisms of viral propagation and pathogenesis. However, full-length genome plasmid construction is highly time-consuming and laborious, and undesired mutations may be introduced by Escherichia coli. This study shows a very rapid E. coli-free method of full-genome construction using the mumps virus as an example. This method was able to reduce dramatically the time for full-genome construction, which was used very efficiently for virus rescue, from several days or more to ~2 days, with a similar accuracy and yield to the conventional method using E. coli/plasmid.
Polarization clustering of biological structures with Mueller matrix parameters

Mueller matrix imaging polarimetry (MMIP) is a promising technique for the characterization of biological tissues, including the classification of microstructures in pathological diagnosis. To expand the parameter space of Mueller matrix parameters, we propose new vector parameters (VPs) according to the Mueller matrix polar decomposition method. We measure invasive bladder cancer (IBC) with extensive necrosis and high-grade ductal carcinoma in situ (DCIS) with MMIP, and the regions of cancer cells and fibrotic stroma are classified with the VPs. Then the proposed and existing VPs are mapped on the Poincaré sphere with 3D visualization, and an indicator of spatial feature is defined based on the minimum enclosing sphere to evaluate the classification capability of the VPs. For both IBC and DCIS, the results show that the proposed VPs exhibit evident contrast between the regions of cancer cells and fibrotic stroma. This study broadens the fundamental Mueller matrix parameters and helps to improve the characterization ability of the MMIP technique.
A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva

Multiplexed detection of biomolecules is of great value in various fields, from disease diagnosis to food safety and environmental monitoring. However, accurate and multiplexed analyte detection is challenging to achieve in mixtures using a single device/material. In this paper, we demonstrate a machine learning (ML)-powered multimodal analytical device based on a single sensing material made of electrodeposited molybdenum polysulfide (eMoSx) on laser induced graphene (LIG) for multiplexed detection of tyrosine (TYR) and uric acid (UA) in sweat and saliva. Electrodeposition of MoSx shows an increased electrochemically active surface area (ECSA) and heterogeneous electron transfer rate constant, k0. Features are extracted from the electrochemical data in order to train ML models to predict the analyte concentration in the sample (both singly spiked and mixed samples). Different ML architectures are explored to optimize the sensing performance. The optimized ML-based multimodal analytical system offers a limit of detection (LOD) that is two orders of magnitude better than conventional approaches which rely on single peak analysis. A flexible and wearable sensor patch is also fabricated and validated on-body, achieving detection of UA and TYR in sweat over a wide concentration range. While the performance of the developed approach is demonstrated for detecting TYR and UA using eMoSx-LIG sensors, it is a general analytical methodology and can be extended to a variety of electrochemical sensors to enable accurate, reliable, and multiplexed sensing.
A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification

As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes.
Context awareness in biometric systems and methods: State of the art and future scenarios

In the last decade, research in biometrics has been focused on augmenting the algorithmic performance to address a growing range of applications, not limited to person authentication/recognition. The concept of context awareness emerged as a possible key-factor for both performance optimization and operational adaptation of the capture, extraction, matching and decision stages. This may be particularly effective for multi-biometrics systems. The knowledge of the context in which a task is being performed, may provide useful information to the system in several manners. For example, it may allow to adapt to a specific environmental condition, such as shadow or light exposure. On the other hand, it may be possible to select the best available algorithm, among a given set to address the task at hand, which best performs within the given context. This paper aims to provide an overall vision of the main contributions available so far in the field of context-aware biometric systems and methods. The survey is not confined to a particular biometric modality or processing stage, but rather spans the state of the art of several biometric modalities and approaches. A taxonomy of context-aware biometric systems and methods is also proposed, along with a comparison of their features, aims and performances. The analysis will be complemented with a critical discussion about the state of the art also suggesting some future application scenarios. (C) 2018 Elsevier B.V. All rights reserved.
A low-power current mode fuzzy-ART cell

This paper presents a very large scale integration (VLSI) implementation of a low-power current-mode fuzzy-adaptive resonance theory (ART) cell. The cell is based on a compact new current source multibit memory cell with online learning capability. A small prototype of the designed cell and its peripheral block has been fabricated in the AustriaMicroSystems (AMS)-0.35-mu m technology. The cell occupies a total area of 44 x 34 mu m(2) and consumes a maximum current of 22 nA.
Motion Tracking of the Carotid Artery Wall From Ultrasound Image Sequences: a Nonlinear State-Space Approach

The motion of the common carotid artery (CCA) wall has been established to be useful in early diagnosis of atherosclerotic disease. However, tracking the CCA wall motion from ultrasound images remains a challenging task. In this paper, a nonlinear state-space approach has been developed to track CCA wall motion from ultrasound sequences. In this approach, a nonlinear state-space equation with a time-variant control signal was constructed from a mathematical model of the dynamics of the CCA wall. Then, the unscented Kalman filter (UKF) was adopted to solve the nonlinear state transfer function in order to evolve the state of the target tissue, which involves estimation of the motion trajectory of the CCA wall from noisy ultrasound images. The performance of this approach has been validated on 30 simulated ultrasound sequences and a real ultrasound dataset of 103 subjects by comparing the motion tracking results obtained in this study to those of three state-of-the-art methods and of the manual tracing method performed by two experienced ultrasound physicians. The experimental results demonstrated that the proposed approach is highly correlated with (intra-class correlation coefficient >= 0.9948 for the longitudinal motion and >= 0.9966 for the radial motion) and well agrees (the 95% confidence interval width is 0.8871 mm for the longitudinal motion and 0.4159 mm for the radial motion) with the manual tracing method on real data and also exhibits high accuracy on simulated data (0.1161 similar to 0.1260 mm). These results appear to demonstrate the effectiveness of the proposed approach for motion tracking of the CCA wall.
Photo-Realistic Image Dehazing and Verifying Networks via Complementary Adversarial Learning

Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet & CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases.
Pansharpening Based on Deconvolution for Multiband Filter Estimation

The combination of a multispectral (MS) image and a panchromatic (PAN) image, the so-called pansharpening, allows to produce very appealing images that are useful both for visual interpretation and for feature extraction. The state-of-the-art multiresolution analysis pansharpening algorithms are based on the extraction of spatial details from the PAN image through image filters matched with the MS sensors' modulation transfer function. However, this knowledge is often poor due to measurement inaccuracies and/or its aging. Thus, deconvolution algorithms have been proposed to overcome this limitation. In this paper, we propose a multiband filter estimation (FE) approach to improve the solutions in the literature. The main idea in this paper is to exploit a preliminary pansharpened image to estimate the spatial filter used for detail extraction associated with each spectral band. We demonstrate that the proposed method outperforms the state-of-the-art FE approaches by employing data sets acquired by the IKONOS, the Quickbird, and the WorldVievv-3 sensors.