ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Assessment of hindlimb motor recovery after severe thoracic spinal cord injury in rats: classification of CatWalk XT® gait analysis parameters

Assessment of locomotion recovery in preclinical studies of experimental spinal cord injury remains challenging. We studied the CatWalk XT® gait analysis for evaluating hindlimb functional recovery in a widely used and clinically relevant thoracic contusion/compression spinal cord injury model in rats. Rats were randomly assigned to either a T9 spinal cord injury or sham laminectomy. Locomotion recovery was assessed using the Basso, Beattie, and Bresnahan open field rating scale and the CatWalk XT® gait analysis. To determine the potential bias from weight changes, corrected hindlimb (H) values (divided by the unaffected forelimb (F) values) were calculated. Six weeks after injury, cyst formation, astrogliosis, and the deposition of chondroitin sulfate glycosaminoglycans were assessed by immunohistochemistry staining. Compared with the baseline, a significant spontaneous recovery could be observed in the CatWalk XT® parameters max intensity, mean intensity, max intensity at%, and max contact mean intensity from 4 weeks after injury onwards. Of note, corrected values (H/F) of CatWalk XT® parameters showed a significantly less vulnerability to the weight changes than absolute values, specifically in static parameters. The corrected CatWalk XT® parameters were positively correlated with the Basso, Beattie, and Bresnahan rating scale scores, cyst formation, the immunointensity of astrogliosis and chondroitin sulfate glycosaminoglycan deposition. The CatWalk XT® gait analysis and especially its static parameters, therefore, seem to be highly useful in assessing spontaneous recovery of hindlimb function after severe thoracic spinal cord injury. Because many CatWalk XT® parameters of the hindlimbs seem to be affected by body weight changes, using their corrected values might be a valuable option to improve this dependency.
Trends in food consumption according to the degree of food processing among the UK population over 11 years

Although ultra-processed foods represent more than half of the total energy consumed by the UK population, little is known about the trend in food consumption considering the degree of food processing. We evaluated the trends of the dietary share of foods categorised according to the NOVA classification in a historical series (2018-2019) among the UK population. Data were acquired from the NDNS, a survey that collects diet information through a 4-d food record. We used adjusted linear regression to estimate the dietary participation of NOVA groups and evaluated the linear trends over the years. From 2008 to 2019, we observed a significant increase in the energy share of culinary ingredients (from 3·7 to 4·9 % of the total energy consumed; P-trend = 0·001), especially for butter and oils; and reduction of processed foods (from 9·6 to 8·6 %; P-trend = 0·002), especially for beer and wine. Unprocessed or minimally processed foods (≅30 %, P-trend = 0·505) and ultra-processed foods (≅56 %, P-trend = 0·580) presented no significant change. However, changes in the consumption of some subgroups are noteworthy, such as the reduction in the energy share of red meat, sausages and other reconstituted meat products as well as the increase of fruits, ready meals, breakfast cereals, cookies, pastries, buns and cakes. Regarding the socio-demographic characteristics, no interaction was observed with the trend of the four NOVA groups. From 2008 to 2019 was observed a significant increase in culinary ingredients and a reduction in processed food. Furthermore, it sheds light on the high share of ultra-processed foods in the contemporary British diet.
The Fast Heuristic Algorithms and Post-Processing Techniques to Design Large and Low-Cost Communication Networks

It is challenging to design large and low-cost communication networks. In this paper, we formulate this challenge as the prize-collecting Steiner Tree Problem (PCSTP). The objective is to minimize the costs of transmission routes and the disconnected monetary or informational profits. Initially, we note that the PCSTP is MAX SNP-hard. Then, we propose some post-processing techniques to improve suboptimal solutions to PCSTP. Based on these techniques, we propose two fast heuristic algorithms: the first one is a quasilinear time heuristic algorithm that is faster and consumes less memory than other algorithms; and the second one is an improvement of a state-of-the-art polynomial time heuristic algorithm that can find high-quality solutions at a speed that is only inferior to the first one. We demonstrate the competitiveness of our heuristic algorithms by comparing them with the state-of-the-art ones on the largest existing benchmark instances (169 800 vertices and 338 551 edges). Moreover, we generate new instances that are even larger (1 000 000 vertices and 10 000 000 edges) to further demonstrate their advantages in large networks. The state-of-the-art algorithms are too slow to find high-quality solutions for instances of this size, whereas our new heuristic algorithms can do this in around 6 to 45s on a personal computer. Ultimately, we apply our post-processing techniques to update the best-known solution for a notoriously difficult benchmark instance to show that they can improve near-optimal solutions to PCSTP. In conclusion, we demonstrate the usefulness of our heuristic algorithms and post-processing techniques for designing large and low-cost communication networks.
Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

We propose a new statistical model for image restoration in which neighborhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighborhood is obtained, thereby modeling the strongest correlations in that neighborhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However, the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.
Specular Reflections Removal for Endoscopic Image Sequences With Adaptive-RPCA Decomposition

Specular reflections (i.e., highlight) always exist in endoscopic images, and they can severely disturb surgeons observation and judgment. In an augmented reality (AR)-based surgery navigation system, the highlight may also lead to the failure of feature extraction or registration. In this paper, we propose an adaptive robust principal component analysis (Adaptive-RPCA) method to remove the specular reflections in endoscopic image sequences. It can iteratively optimize the sparse part parameter during RPCA decomposition. In this new approach, we first adaptively detect the highlight image based on pixels. With the proposed distance metric algorithm, it then automatically measures the similarity distance between the sparse result image and the detected highlight image. Finally, the low-rank and sparse results are obtained by enforcing the similarity distance between the two types of images to fall within a certain range. Our method has been verified by multiple different types of endoscopic image sequences in minimally invasive surgery (MIS). The experiments and clinical blind tests demonstrate that the new Adaptive-RPCA method can obtain the optimal sparse decomposition parameters directly and can generate robust highlight removal results. Compared with the state-of-the-art approaches, the proposed method not only achieves the better highlight removal results but also can adaptively process image sequences.
Cytoplasmic G Protein-Coupled Estrogen Receptor 1 as a Prognostic Indicator of Breast Cancer: A Meta-Analysis

Purpose: To determine whether G protein-coupled estrogen receptor 1 (GPER1) is a suitable biomarker to predict the treatment outcome of breast cancer (BC). Methods: A meta-analysis of the literature was performed to clarify the correlation between GPER1 protein expression and BC outcome. The relationship between GPER1 mRNA expression and survival was analyzed using Breast Cancer Gene-Expression Miner (bc-GenExMiner) v4.6 software. Results: Six studies involving 2697 patients were included in the meta-analysis. Four studies reported the correlation between GPER1 protein expression and relapse-free survival (RFS) and 4 others reported the impact of GPER1 protein expression on overall survival (OS). The results showed that high GPER1 protein expression was not associated with RFS (hazard ratio [HR] = 1.58; 95% confidence interval [CI] = 0.71-3.48; P = .26) or OS (HR = 1.18; 95% CI = 0.64-2.18; P = .60). Subgroup analysis suggested that nuclear expression of GPER1 was not associated with OS (HR = 0.91; 95% CI = 0.77-1.08; P = .30), but high expression of cytoplasmic GPER1 was significantly associated with longer OS (HR = 0.69; 95% CI = 0.55-0.86; P = .001). Furthermore, the association of GPER1 mRNA and OS of BC patients was analyzed using bc-GenExMiner v4.6. Two data sets involving 4016 patients were included in the analysis. The targeted prognostic analysis results showed that high mRNA expression of GPER1 was predictive of better OS in BC patients (HR = 0.71; 95% CI = 0.59-0.86; P = .0005), which was remarkably similar to the result of cytoplasmic GPER1. Further subgroup analysis demonstrated that high mRNA expression of GPER1 was predictive of better OS in estrogen receptor (ER)-positive, but not ER-negative or triple-negative BC patients. Conclusions: High mRNA and cytoplasmic protein expression of GPER1 were predictive of better OS of BC patients.
An optimized sono-heterogeneous Fenton degradation of olive-oil mill wastewater organic matter by new magnetic glutarlaldehyde-crosslinked developed cellulose

The present study highlights the olive mill wastewater (OMW) treatment characteristics through a sono-heterogeneous Fenton process using new designed [GTA-(PDA-g-DAC) @Fe3O4] and characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), magnetic properties measurements, and point of zero charge (pH pzc) analysis. A preliminary removal study showed significant degradation efficiency (75%) occurred combining the magnetic synthesized catalyst [GTA-(PDA-g-DAC)@Fe3O4] ([catalyst] = 2 g/L) with US /H2O2 and maintaining 500WL-1 ultrasonic power (US). The values obtained by US only were (13%), H2O2/US (18%), US/Fe3O4 (28%), and US /Fe3O4/H2O2(35%). The catalytic findings have shown that [GTA-(PDA-g-DAC)@Fe3O4] exhibited good properties for OMW compound's degradation. The sonocatalytic process coupling and extra oxidant addition resulted in the degradation substantial levels. For instance, the concomitant effect of degradation optimized parameters; H2O2 10 mM, [GTA-(PDA-g-DAC) @Fe3O4] nanocomposites 2.5 g/L, at pH 3, and T 35 °C for 70 min resulted in an almost complete mineralization of aqueous OMW solution followed by a significant decolorization. Oxidation results exhibited efficient degradation rates in total phenolic compounds (TPC), total amino compounds (TAC), and chemical oxygen demand (COD) oxidation rate were 89.88, 92.75, and 95.66 respectively following the optimized sono-heterogeneous catalytic Fenton process. The prepared magnetic catalyst exhibited a good stability during repeated cycles. The gathered findings gave the evidence that sono-heterogeneous catalytic Fenton process is a promising treatment technology for OMW effluents.
Magnetic diffusion: Scalability, reliability, and QoS of data dissemination mechanisms for wireless sensor networks

Envisioning a new generation of sensor network applications in healthcare and workplace safety, we seek mechanisms that provide timely and reliable transmissions of mission-critical data. Inspired by the physics in magnetism, we propose a simple diffusion-based data dissemination mechanism, referred to as the magnetic diffusion (MD). In that, the data sink, functioning like the magnet, propagates the magnetic charge to set up the magnetic field. Under the influence of the magnetic field, the sensor data, functioning like the metallic nails, are attracted towards the sink. We compare MD to the state-of-the-art mechanisms and find that MD: (1) performs the best in timely delivery of data, (2) achieves high data reliability in the presence of network dynamics, and yet (3) works as energy efficiently as the state of the art. These suggest that MD is an effective data dissemination solution to the mission-critical applications. (c) 2006 Elsevier B.V. All rights reserved.
A Practical Data Classification Framework for Scalable and High Performance Chip-Multiprocessors

State-of-the-art chip multiprocessor (CMP) proposals emphasize general optimizations designed to deliver computing power for many types of applications. Potentially, significant performance improvements that leverage application-specific characteristics such as data access behavior are missed by this approach. In this paper, we demonstrate how scalable and high-performance parallel systems can be built by classifying data accesses into different categories and treating them differently. We develop a novel compiler-based approach to speculatively detect a data classification termed practically private, which we demonstrate is ubiquitous in a wide range of parallel applications. Leveraging this classification provides efficient solutions to mitigate data access latency and coherence overhead in today's many-core architectures. While the proposed data classification scheme can be applied to many micro-architectural constructs including the TLB, coherence directory, and interconnect, we demonstrate its potential through an efficient cache coherence design. Specifically, we show that the compiler-assisted mechanism reduces an average of 46% coherence traffic and achieves up to 12%, 8%, and 5% performance improvement over shared, private, and state-of-the-art NUCA-based caching, respectively, depending on scenarios.
Open Innovation Readiness Assessment within Students in Poland: Investigating State-of-the-Art and Challenges

In light of Poland's innovation performance level being below 70% of the EU average, open innovation can be a key path for innovation capacity increase. This paper explores the readiness of students in Poland for open innovation (OI). The study is based on a survey of a sample of 500 students using the Computer-Assisted Web Interview research technique. The main aim of this paper is to investigate Polish students' attitude to open innovation-in particular in terms of social product development, crowdsourcing, crowdfunding, and the sharing economy-to assess the state-of-the-art and identify challenges. Students are selected as the target group because they are open-minded, eager to use new solutions, and will soon enter the business sector to either become the staff of companies or set up their own startups or SMEs. However, the study shows that Polish students, if they use the OI-based platforms at all, use them passively. The key barriers identified within this study are a lack of knowledge about the open innovation paradigm, its elements and opportunities, and an issue of trust. Therefore, a change of mindset, the adjustment of universities' curricula, and the development of open innovation culture are critical.
PROTON plus : A Placement and Routing Tool for 3D Optical Networks-on-Chip with a Single Optical Layer

Optical Networks-on-Chip (ONoCs) are a promising technology to overcome the bottleneck of low bandwidth of electronic Networks-on-Chip. Recent research discusses power and performance benefits of ONoCs based on their system-level design, while layout effects are typically overlooked. As a consequence, laser power requirements are inaccurately computed from the logic scheme but do not consider the layout. In this article, we propose PROTON+, a fast tool for placement and routing of 3D ONoCs minimizing the total laser power. Using our tool, the required laser power of the system can be decreased by up to 94% compared to a state-of-the-art manually designed layout. In addition, with the help of our tool, we study the physical design space of ONoC topologies. For this purpose, topology synthesis methods (e.g., global connectivity and network partitioning) as well as different objective function weights are analyzed in order to minimize the maximum insertion loss and ultimately the system's laser power consumption. For the first time, we study optimal positions of memory controllers. A comparison of our algorithm to a state-of-the-art placer for electronic circuits shows the need for a different set of tools custom-tailored for the particular requirements of optical interconnects.
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation. They are rendered difficult due to a growing number of emitter types and varied effects of real-world channels upon the radio signal. Existing spectrum monitoring techniques are capable of acquiring massive amounts of radio and real-time spectrum data using compact sensors deployed in a variety of settings. However, state-of-the-art methods that use such data to classify emitter types and detect communication schemes struggle to achieve required levels of accuracy at a computational efficiency that would allow their implementation on low-cost computational platforms. In this paper, we present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals, and infer modulation or technology type using the learned features. The algorithm utilizes a compact neural network architecture readily implemented on a low-cost computational platform while exceeding state-of-the-art accuracy. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals, often demonstrating superior performance compared to state-of-the-art methods.
Sampling biases obscure the early diversification of the largest living vertebrate group

Extant ray-finned fishes (Actinopterygii) dominate marine and freshwater environments, yet spatio-temporal diversity dynamics following their origin in the Palaeozoic are poorly understood. Previous studies investigate face-value patterns of richness, with only qualitative assessment of biases acting on the Palaeozoic actinopterygian fossil record. Here, we investigate palaeogeographic trends, reconstruct local richness and apply richness estimation techniques to a recently assembled occurrence database for Palaeozoic ray-finned fishes. We identify substantial fossil record biases, such as geographical bias in sampling centred around Europe and North America. Similarly, estimates of diversity are skewed by extreme unevenness in the occurrence distributions, reflecting historical biases in sampling and taxonomic practices, to the extent that evenness has an overriding effect on diversity estimates. Other than a genuine rise in diversity in the Tournaisian following the end-Devonian mass extinction, diversity estimates for Palaeozoic actinopterygians appear to lack biological signal, are heavily biased and are highly dependent on sampling. Increased sampling of poorly represented regions and expanding sampling beyond the literature to include museum collection data will be critical in obtaining accurate estimates of Palaeozoic actinopterygian diversity. In conjunction, applying diversity estimation techniques to well-sampled regional subsets of the 'global' dataset may identify accurate local diversity trends.
Design of a Host Interface Logic for GC-Free SSDs

Garbage collection (GC) and resource contention on I/O buses (channels) are among the critical bottlenecks in solid-state drives (SSDs) that cannot be easily hidden. Most existing I/O scheduling algorithms in the host interface logic (HIL) of state-of-the-art SSDs are oblivious to such low-level performance bottlenecks in SSDs. As a result, SSDs may violate quality of service (QoS) requirements by not being able to meet the deadlines of I/O requests. In this paper, we propose a novel host interface I/O scheduler that is both GC aware and QoS aware. The proposed scheduler redistributes the GC overheads across noncritical I/O requests and reduces channel resource contention. Our experiments with workloads from various application domains revealed that the proposed client-level SSD scheduler reduces the standard deviation for latency by 52.5% and the worst-case latency by 86.6%, compared to the state-of-the-art I/O schedulers used for the HIL. In addition, for I/O requests smaller than a superpage, the proposed scheduler avoids channel resource conflicts and reduces latency by 29.2% in comparison to the state-of-the-art I/O schedulers. Furthermore, we present an extension of the proposed I/O scheduler for enterprise SSDs based on the NVMe protocol.
Spintronic Integrate-Fire-Reset Neuron with Stochasticity for Neuromorphic Computing

Spintronics has been recently extended to neuromorphic computing because of its energy efficiency and scalability. However, a biorealistic spintronic neuron with probabilistic "spiking" and a spontaneous reset functionality has not been demonstrated yet. Here, we propose a biorealistic spintronic neuron device based on the heavy metal (HM)/ferromagnet (FM)/antiferromagnet (AFM) spin-orbit torque (SOT) heterostructure. The spintronic neuron can autoreset itself after firing due to the exchange bias of the AFM. The firing process is inherently stochastic because of the competition between the SOT and AFM pinning effects. We also implement a restricted Boltzmann machine (RBM) and stochastic integration multilayer perceptron (SI-MLP) using our proposed neuron. Despite the bit-width limitation, the proposed spintronic model can achieve an accuracy of 97.38% in pattern recognition, which is even higher than the baseline accuracy (96.47%). Our results offer a spintronic device solution to emulate biologically realistic spiking neurons.