ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Making Use of Auditory Models for Better Mimicking of Normal Hearing Processes With Cochlear Implants: The SAM Coding Strategy

Mimicking the human ear on the basis of auditory models has become a viable approach in many applications by now. However, only a few attempts have been made to extend the scope of physiological ear models to be employed in cochlear implants (CI). Contemporary CI systems rely on much simpler filter banks and simulate the natural signal processing of a healthy cochlea to only a very limited extent. When looking at rehabilitation outcomes, current systems seem to have reached their peak potential, which signals the need for better algorithms and/or technologies. In this paper, we present a novel sound processing strategy, SAM (Stimulation based on Auditory Modeling), that is based on neurophysiological models of the human ear and can be employed in auditory prostheses. It incorporates active cochlear filtering (basilar membrane and outer hair cells) along with the mechanoelectrical transduction of the inner hair cells, so that several psychoacoustic phenomena are accounted for inherently. Although possible, current implementation does not make use of parallel stimulation of the electrodes, which matches state-of-the-art CI hardware. This paper elaborates on SAM's signal processing and provides a computational evaluation of the strategy. Results show that aspects of normal cochlear processing that are missing in common strategies can be replicated by SAM. This is supposed to improve overall CI user performance, which we have at least partly proven in a pilot study with implantees.
Patterns of reported infection and reinfection of SARS-CoV-2 in England

One of the key features of any infectious disease is whether infection generates long-lasting immunity or whether repeated reinfection is common. In the former, the long-term dynamics are driven by the birth of susceptible individuals while in the latter the dynamics are governed by the speed of waning immunity. Between these two extremes a range of scenarios is possible. During the early waves of SARS-CoV-2, the underlying paradigm was for long-lasting immunity, but more recent data and in particular the 2022 Omicron waves have shown that reinfection can be relatively common. Here we investigate reported SARS-CoV-2 cases in England, partitioning the data into four main waves, and consider the temporal distribution of first and second reports of infection. We show that a simple low-dimensional statistical model of random (but scaled) reinfection captures much of the observed dynamics, with the value of this scaling, k, providing information of underlying epidemiological patterns. We conclude that there is considerable heterogeneity in risk of reporting reinfection by wave, age-group and location. The high levels of reinfection in the Omicron wave (we estimate that 18% of all Omicron cases had been previously infected, although not necessarily previously reported infection) point to reinfection events dominating future COVID-19 dynamics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
Blunt dopamine transmission due to decreased GDNF in the PFC evokes cognitive impairment in Parkinson's disease

Studies have found that the absence of glial cell line-derived neurotrophic factor may be the primary risk factor for Parkinson's disease. However, there have not been any studies conducted on the potential relationship between glial cell line-derived neurotrophic factor and cognitive performance in Parkinson's disease. We first performed a retrospective case-control study at the Affiliated Hospital of Xuzhou Medical University between September 2018 and January 2020 and found that a decreased serum level of glial cell line-derived neurotrophic factor was a risk factor for cognitive disorders in patients with Parkinson's disease. We then established a mouse model of Parkinson's disease induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and analyzed the potential relationships among glial cell line-derived neurotrophic factor in the prefrontal cortex, dopamine transmission, and cognitive function. Our results showed that decreased glial cell line-derived neurotrophic factor in the prefrontal cortex weakened dopamine release and transmission by upregulating the presynaptic membrane expression of the dopamine transporter, which led to the loss and primitivization of dendritic spines of pyramidal neurons and cognitive impairment. In addition, magnetic resonance imaging data showed that the long-term lack of glial cell line-derived neurotrophic factor reduced the connectivity between the prefrontal cortex and other brain regions, and exogenous glial cell line-derived neurotrophic factor significantly improved this connectivity. These findings suggested that decreased glial cell line-derived neurotrophic factor in the prefrontal cortex leads to neuroplastic degeneration at the level of synaptic connections and circuits, which results in cognitive impairment in patients with Parkinson's disease.
Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of more renewable energy sources into the power grid. To address the limited resolution and costs of methods based on numerical weather predictions (NWP), we take PV production data as main input for forecasting. Since PV power is affected by weather and cloud dynamics, we model spatio-temporal correlations between production data by representing PV systems as nodes of a dynamic graph and embedding production data, geographical information and clear-sky irradiance as signals on that graph. We introduce a new temporal-spatial multi -windows graph attention network (TSM-GAT) for predicting future PV power production. TSM-GAT can adapt to the dynamics of the problem, by learning different graphs over time. It consists of temporal attention with an overlapping-window mechanism that finds the temporal correlations and spatial attention with a multi-window mechanism, which captures different dynamical spatio-temporal correlations for different parts of the forecasting horizon. Thus, it is possible to interpret which PV stations have the most influence when making a prediction for short-, medium-and long-term intra-day forecasts. TSM-GAT outperforms multi-site state-of-the-art models for four to six hours ahead predictions, with average NRMSE 12.4% and 10.5% on a real and synthetic dataset, respectively. Furthermore, it outperforms state-of-the-art models that use NWP as inputs for up to five hours ahead predictions. TSM-GAT yields predicted signals with a closer shape to ground truth than state-of-the-art models, which indicates that it is better at capturing cloud motion and may lead to better generalization capabilities.
Performance Improvement in Geographic Routing for Vehicular Ad Hoc Networks

Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular Ad Hoc Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV). Most of these protocols suffer from the problem of elevated one-hop link disconnection, high end-to-end delay and low throughput even at normal vehicle speed in high vehicle density environment. This paper proposes a Geographic Distance Routing protocol based on Segment vehicle, Link quality and Degree of connectivity (SLD-GEDIR). The protocol selects a reliable NHV using the criteria segment vehicles, one-hop link quality and degree of connectivity. The proposed protocol has been simulated in NS-2 and its performance has been compared with the state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR. The empirical results clearly reveal that SLD-GEDIR has lower link disconnection and end-to-end delay, and higher throughput as compared to the state-of-the-art protocols. It should be noted that the performance of the proposed protocol is preserved irrespective of vehicle density and speed.
Isolation and Characterization of Fetal Liver Hematopoietic Stem Cells

Hematopoietic stem cells (HSCs) are responsible for the generation and maintenance of pools of multipotent precursors that ultimately give rise to all fully differentiated blood and immune cells. Proper identification and isolation of HSCs for functional analysis has greatly facilitated our understanding of both normal and abnormal adult hematopoiesis. Whereas adult hematopoiesis in mice and humans is driven by quiescent HSCs that reside almost exclusively within the bone marrow (BM), developmental hematopoiesis is characterized by a series of transient progenitors driving waves of increasingly mature hematopoietic cell production that occur across multiple anatomical sites. These waves of hematopoietic cell production are also responsible for the generation of distinct immune cell populations during development that persist into adulthood and contribute uniquely to adult immunity. Therefore, methods to properly isolate and characterize fetal progenitors with high purity across development become increasingly important not only for defining developmental hematopoietic pathways, but also for understanding the contribution of developmental hematopoiesis to the immune system. Here, we describe and discuss methods and considerations for the isolation and characterization of HSCs from the fetal liver, the primary hematopoietic organ during fetal development.
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited durations. Recently, in computer vision and natural language processing, systems pretrained on large-scale datasets have generalized well to several tasks. However, there is limited research on pretraining systems on large-scale datasets for audio pattern recognition. In this paper, we propose pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature. Our best PANN system achieves a state-of-the-art mean average precision (mAP) of 0.439 on AudioSet tagging, outperforming the best previous system of 0.392. We transfer PANNs to six audio pattern recognition tasks, and demonstrate state-of-the-art performance in several of those tasks. We have released the source code and pretrained models of PANNs: https://github.com/qiuqiangkong/audioset_tagging_cnn.
Signs associated with figurative representations Aurignacian. Examples from Grotte Chauvet and the Swabian Jura

Signs in Upper Palaeolithic art provide an important criterion of assessing cultural affinities or differences. The Swabian Jura in southern Germany and Grotte Chauvet in the Ardeche region in southern France - both inscribed in the list of the UNESCO world heritage - supply the most important examples of Aurignacian art known so far. Of a particular interest in this respect are characteristic types of signs which can be associated with depictions of animals. This paper examines the distinctive ways in which this association is realized in these two regions, including the question which type of signs was employed. Despite of the small data base and the lack of sophisticated statistical tests, this study may help to provide first hints to the question if the Aurignacian groups of the Ardeche and the Swabian Jura, whose areas are linked by important river systems, used similar or different sets of signs and in which animal contexts these signs were used. These results may give indications to what extent these groups were part of the same or different cultural entities and subsequently shared feelings of a common ethnicity and belief-world or not. (c) 2018 Elsevier Ltd and INQUA. All rights reserved.
Approximate Sparse Multinomial Logistic Regression for Classification

We propose a new learning rule for sparse multinomial logistic regression (SMLR). The new rule is the generalization of the one proposed in the pioneering work by Krishnapuram et al. In our proposed method, the parameters of SMLR are iteratively estimated from log-posterior by using some approximations. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The estimated parameters are tested on the pixel-based classification of hyperspectral images. The experimental results on real hyperspectral images show that the classification accuracy of proposed method is also better than those of the state-of-the-art methods.
Teamwork in the performing arts

This paper addresses the nature of teams and teamwork in the performing arts, including symphony, chamber orchestra, chorus, and jazz, as well as musical theater straight theatre, improv, ballet, and puppetry. The results of an interview study of performing arts leaders in these domains are reported. These results suggest an "ecology" of performance. The characteristics of this ecology strongly influence the nature and roles of teams, as well as how teams are created and supported. Potential relationships among the attributes of this characterization are discussed.
Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration

Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. This framework allows the robust registration of motion components over time without intensity variances. Existing sparse coding techniques recover a 3D image containing only contrast agents (named contrast enhancement component) from a manually labeled dictionary, whose column has the same size with the original 3D volume (3D-t mode). The high dimension of the recovery target (3D volume) and the indistinguishability between the unenhanced and enhanced images make accurate coding difficult. In this paper, we predefine an ideal time-intensity curve containing only contrast agents (named contrast agent curve) and recover it from the transpose dictionary (t-3D mode), whose column has been updated into the original time-intensity curves. The low dimension of the target (1D curve) and the significant intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure contrast agent curves. A "correlation-weighted"constraint is introduced for the selection of a coding subset with more contrast agent curves, leading to an efficient and accurate sparse recovery process. Then, the contrast enhancement component can be estimated by the solved sparse coefficients' map and the ideal curve and subtracted from the original DCE-MRI. Finally, we register the de-enhanced images and apply the obtained deformation fields for the original DCE-MRI to achieve the goal of motion correction. We conduct the experiments on both simulated and real liver DCE-MRI data. Compared with other state-of-the-art DCE-MRI registration methods, the experimental results show that our method achieves a better registration performance with less computational efficiency.
Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms

Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum Description Length) discretization algorithm. It achieves an overall accuracy of 97.92 percent for classifying eight major applications. Next we propose approaches to accelerate the classifier on FPGA (Field Programmable Gate Array) and multicore platforms. We optimize the original classifier by merging it with discretization. Our implementation of this optimized decision tree achieves 7500+Million Classifications Per Second (MCPS) on a state-of-the-art FPGA platform and 75-150 MCPS on two state-of-the-art multicore platforms. We also propose a divide and conquer approach to handle imbalanced decision trees. Our implementation of the divide-and-conquer approach achieves 10,000+MCPS on a state-of-the-art FPGA platform and 130-340 MCPS on two state-of-the-art multicore platforms. We conduct extensive experiments on both platforms for various application scenarios to compare the two approaches.
Association of Plasma Metabolites and Lipoproteins with Rh and ABO Blood Systems in Healthy Subjects

This study investigated the associations between the levels of 27 plasma metabolites, 114 lipoprotein parameters, determined using nuclear magnetic resonance spectroscopy, and the ABO blood groups and the Rhesus (Rh) blood system in a cohort of n = 840 Italian healthy blood donors of both sexes. We observed good multivariate discrimination between the metabolomic and lipoproteomic profiles of subjects with positive and negative Rh. In contrast, we did not observe significant discrimination for the ABO blood group pairwise comparisons, suggesting only slight metabolic differences between these group-specific metabolic profiles. We report univariate associations (P-value < 0.05) between the subfraction HDL1 related to Apo A1, the subfraction HDL2 related to cholesterol and phospholipids, and the particle number of LDL2 related to free cholesterol, cholesterol, phospholipids, and Apo B and the ABO blood groups; we observed association of the lipid main fraction LDL4 related to free cholesterol, triglycerides, and Apo B; creatine; the particle number of LDL5; the subfraction LDL5 related to Apo B; the particle number of LDL4; and the subfraction LDL4 related to Apo B with Rh blood factors. These results suggest blood group-dependent (re)shaping of lipoprotein metabolism in healthy subjects, which may provide relevant information to explain the differential susceptibility to certain diseases observed in different blood groups.
The Innovative Application of Visual Communication Design in Modern Art Design

In the context of the rapid development of economy and culture, people's requirements for material and culture are constantly increasing, and the relationship between graphic design and human life is also increasingly close. With the continuous development of Internet of Things technology, information exchange between people and things can be realized by using various sensing devices, and innovative modern art can be established. Therefore, the focus of culture has gradually shifted to the field of visualization. However, in the visual communication design, the product design method of "mainly two-dimensional plan and supplemented by three-dimensional model", on the one hand, cannot maximize the intuitive effect of three-dimensional modeling, thus affecting the efficiency of design; on the other hand, problems such as information incoordination and resource waste are likely to occur. In short, people's requirements for design have changed from a two-dimensional plane space to a three-dimensional space, and a two-dimensional plane design can no longer meet people's growing demand for artistic design. Aiming at tackling these problems, this paper proposed to establish a visual communication system based on artificial intelligence (AI). Through this method, the image can be made clearer and has a larger field of view and magnification. At the same time, the system was applied to modern art design, which is a type of innovation. The experimental results showed that the maximum distortion of the system designed in this paper was approximately 15%, and the maximum distortion of the traditional sample was about 20%. Compared with conventional samples, this system has great advantages in graphics transformation. In addition, the chromatic aberration of the optical system can be corrected to improve the imaging effect.
Patterning principles of morphogen gradients

Metazoan embryos develop from a single cell into three-dimensional structured organisms while groups of genetically identical cells attain specialized identities. Cells of the developing embryo both create and accurately interpret morphogen gradients to determine their positions and make specific decisions in response. Here, we first cover intellectual roots of morphogen and positional information concepts. Focusing on animal embryos, we then provide a review of current understanding on how morphogen gradients are established and how their spans are controlled. Lastly, we cover how gradients evolve in time and space during development, and how they encode information to control patterning. In sum, we provide a list of patterning principles for morphogen gradients and review recent advances in quantitative methodologies elucidating information provided by morphogens.
HANAC Syndrome Col4a1 Mutation Causes Neonate Glomerular Hyperpermeability and Adult Glomerulocystic Kidney Disease

Hereditary angiopathy, nephropathy, aneurysms, and muscle cramps (HANAC) syndrome is an autosomal dominant syndrome caused by mutations in COL4A1 that encodes the α1 chain of collagen IV, a major component of basement membranes. Patients present with cerebral small vessel disease, retinal tortuosity, muscle cramps, and kidney disease consisting of multiple renal cysts, chronic kidney failure, and sometimes hematuria. Mutations producing HANAC syndrome localize within the integrin binding site containing CB3[IV] fragment of the COL4A1 protein. To investigate the pathophysiology of HANAC syndrome, we generated mice harboring the Col4a1 p.Gly498Val mutation identified in a family with the syndrome. Col4a1 G498V mutation resulted in delayed glomerulogenesis and podocyte differentiation without reduction of nephron number, causing albuminuria and hematuria in newborns. The glomerular defects resolved within the first month, but glomerular cysts developed in 3-month-old mutant mice. Abnormal structure of Bowman's capsule was associated with metalloproteinase induction and activation of the glomerular parietal epithelial cells that abnormally expressed CD44,α-SMA, ILK, and DDR1. Inflammatory infiltrates were observed around glomeruli and arterioles. Homozygous Col4a1 G498V mutant mice additionally showed dysmorphic papillae and urinary concentration defects. These results reveal a developmental role for the α1α1α2 collagen IV molecule in the embryonic glomerular basement membrane, affecting podocyte differentiation. The observed association between molecular alteration of the collagenous network in Bowman's capsule of the mature kidney and activation of parietal epithelial cells, matrix remodeling, and inflammation may account for glomerular cyst development and CKD in patients with COL4A1-related disorders.