ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Calibration-free, high-precision, and robust terahertz ultrafast metasurfaces for monitoring gastric cancers

Optical sensors, with great potential to convert invisible bioanalytical response into readable information, have been envisioned as a powerful platform for biological analysis and early diagnosis of diseases. However, the current extraction of sensing data is basically processed via a series of complicated and time-consuming calibrations between samples and reference, which inevitably introduce extra measurement errors and potentially annihilate small intrinsic responses. Here, we have proposed and experimentally demonstrated a calibration-free sensor for achieving high-precision biosensing detection, based on an optically controlled terahertz (THz) ultrafast metasurface. Photoexcitation of the silicon bridge enables the resonant frequency shifting from 1.385 to 0.825 THz and reaches the maximal phase variation up to 50° at 1.11 THz. The typical environmental measurement errors are completely eliminated in theory by normalizing the Fourier-transformed transmission spectra between ultrashort time delays of 37 ps, resulting in an extremely robust sensing device for monitoring the cancerous process of gastric cells. We believe that our calibration-free sensors with high precision and robust advantages can extend their implementation to study ultrafast biological dynamics and may inspire considerable innovations in the field of medical devices with nondestructive detection.
Techno-economic evaluation of biomass-to-fuels with solid-oxide electrolyzer

Thermochemical biomass-to-fuel conversion requires an increased hydrogen concentration in the syngas derived from gasification, which is currently achieved by water-gas-shift reaction and CO2 removal. State-of-the-art biomass-to-fuels convert less than half of the biomass carbon with the remaining emitted as CO2. Full conversion of biomass carbon can be achieved by integrating solid-oxide electrolyzer with different concepts: (1) steam electrolysis with the hydrogen produced injected into syngas, and (2) co-electrolysis of CO2 and H2O to convert the CO2 captured from the syngas. This paper investigates techno-economically steam- or co-electrolysis-based biomass-to-fuel processes for producing synthetic natural gas, methanol, dimethyl ether and jet fuel, considering system-level heat integration and optimal placement of steam cycles for heat recovery. The results show that state-of-the-art biomass-to-fuels achieve similar energy efficiencies of 48-51% (based on a lower heating value) for the four different fuels. The integrated concept with steam electrolysis achieves the highest energy efficiency: 68% for synthetic natural gas, 64% for methanol, 63% for dimethyl ether, and 56% for jet fuel. The integrated concept with co-electrolysis can enhance the state-of-the-art energy efficiency to 66% for synthetic natural gas, 61% for methanol, and 54% for jet fuel. The biomass-to-dimethyl ether with co-electrolysis only reaches an efficiency of 49%, due to additional heat demand. The levelized cost of the product of the integrated concepts highly depends on the price and availability of renewable electricity. The concept with co-electrolysis allows for additional operation flexibility without renewable electricity, resulting in high annual production. Thus, with limited annual available hours of renewable electricity, biomass-to-fuel with co-electrolysis is more economically convenient than that with steam electrolysis. For a plant scale of 60 MWth biomass input with the renewable electricity available for 1800 h annually, the levelized cost of product of biomass-to-synthesis-natural-gas with co-electrolysis is 35 $/GJ, 20% lower than that with steam-electrolysis.
Fast and Efficient Circuit Topologies for Finding the Maximum of n k-Bit Numbers

Finding the value and/or index of the maximum (or minimum) element of a set of n numbers (each with k-bits) is a fundamental arithmetic operation and is needed in many applications. This paper proposes several maximum-finder (or minimum-finder) circuit topologies, which are parallel. We wrote circuit generators at hardware description language level for our topologies and previous works. Then we synthesized these circuits for 20 different (n, k) cases (with values up to 64) and compared their efficiency in timing (latency), area, and energy. The timing complexity of our fastest topology is O(log n + log k), whereas the fastest in the literature is O(log n log k). The synthesis results showed that our fastest topology is 1.2-2.2 times (1.6 times on the average) faster than the state-of-the-art. In this paper, we argue that a more fair metric of area efficiency is area-timing product. In terms of ATP, our proposed topologies are better than the state-of-the-art in 19 out of the 20 cases. In terms of energy (i.e., power-timing product, abbreviated as PTP), we are better in 11 cases out of 20.
Bone marrow mesenchymal stem cells and exercise restore motor function following spinal cord injury by activating PI3K/AKT/mTOR pathway

Although many therapeutic interventions have shown promise in treating spinal cord injury, focusing on a single aspect of repair cannot achieve successful and functional regeneration in patients following spinal cord injury . In this study, we applied a combinatorial approach for treating spinal cord injury involving neuroprotection and rehabilitation, exploiting cell transplantation and functional sensorimotor training to promote nerve regeneration and functional recovery. Here, we used a mouse model of thoracic contusive spinal cord injury to investigate whether the combination of bone marrow mesenchymal stem cell transplantation and exercise training has a synergistic effect on functional restoration. Locomotor function was evaluated by the Basso Mouse Scale, horizontal ladder test, and footprint analysis. Magnetic resonance imaging, histological examination, transmission electron microscopy observation, immunofluorescence staining, and western blotting were performed 8 weeks after spinal cord injury to further explore the potential mechanism behind the synergistic repair effect. In vivo, the combination of bone marrow mesenchymal stem cell transplantation and exercise showed a better therapeutic effect on motor function than the single treatments. Further investigations revealed that the combination of bone marrow mesenchymal stem cell transplantation and exercise markedly reduced fibrotic scar tissue, protected neurons, and promoted axon and myelin protection. Additionally, the synergistic effects of bone marrow mesenchymal stem cell transplantation and exercise on spinal cord injury recovery occurred via the PI3K/AKT/mTOR pathway. In vitro, experimental evidence from the PC12 cell line and primary cortical neuron culture also demonstrated that blocking of the PI3K/AKT/mTOR pathway would aggravate neuronal damage. Thus, bone marrow mesenchymal stem cell transplantation combined with exercise training can effectively restore motor function after spinal cord injury by activating the PI3K/AKT/mTOR pathway.
RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification

While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.
Human and Scene Motion Deblurring Using Pseudo-Blur Synthesizer

Present-day deep learning-based motion deblurring methods utilize the pair of synthetic blur and sharp data to regress any particular framework. This task is designed for directly translating a blurry image input into its restored version as output. The aforementioned approach relies heavily on the quality of the synthetic blurry data, which are only available before the training stage. Handling this issue by providing a large amount of data is expensive for common usage. We answer this challenge by providing an on- the-fly blurry data augmenter that can be run during training and test stages. To fully utilize it, we incorporate an unorthodox scheme of deblurring framework that employs the sequence of blur-deblur-reblur-deblur steps. The reblur step is assisted by a reblurring module (synthesizer) that provides the reblurred version (pseudo-blur) of its sharp or deblurred counterpart. The proposed module is also equipped with hand-crafted prior extracted using the state-of-the-art human body statistical model. This prior is employed to map human and non-human regions during adversarial learning to fully perceive the characteristics of human-articulated and scene motion blurs. By engaging this approach, our deblurring module becomes adaptive and achieves superior outcomes compared to recent state-of-the-art deblurring algorithms.
Susceptibility versus resistance in alveolar echinococcosis (larval infection with Echinococcus multilocularis)

Epidemiological studies have demonstrated that the majority of human individuals exposed to infection with Echinococcus spp. eggs exhibit resistance to disease as shown by either seroconversion to parasite--specific antigens, and/or the presence of 'dying out' or 'aborted' metacestodes, not including hereby those individuals who putatively got infected but did not seroconvert and who subsequently allowed no development of the pathogen. For those individuals where infection leads to disease, the developing parasite is partially controlled by host immunity. In infected humans, the type of immune response developed by the host accounts for the subsequent trichotomy concerning the parasite development: (i) seroconversion proving infection, but lack of any hepatic lesion indicating the failure of the parasite to establish and further develop within the liver; or resistance as shown by the presence of fully calcified lesions; (ii) controlled susceptibility as found in the "conventional" alveolar echinococcosis (AE) patients who experience clinical signs and symptoms approximately 5-15 years after infection, and (iii) uncontrolled hyperproliferation of the metacestode due to an impaired immune response (AIDS or other immunodeficiencies). Immunomodulation of host immunity toward anergy seems to be triggered by parasite metabolites. Beside immunomodulating IL-10, TGFβ-driven regulatory T cells have been shown to play a crucial role in the parasite-modulated progressive course of AE. A novel CD4+CD25+ Treg effector molecule FGL2 recently yielded new insight into the tolerance process in Echinococcus multilocularis infection.
Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease

Alzheimer's disease (AD) can present with distinct clinical variants. Identifying the earliest neurodegenerative changes associated with each variant has implications for early diagnosis, and for understanding the mechanisms that underlie regional vulnerability and disease progression in AD. We performed voxel-based morphometry to detect atrophy patterns in early clinical stages of four AD phenotypes: Posterior cortical atrophy (PCA, "visual variant," n=93), logopenic variant primary progressive aphasia (lvPPA, "language variant," n=74), and memory-predominant AD categorized as early age-of-onset (EOAD, <65 years, n=114) and late age-of-onset (LOAD, >65 years, n=114). Patients with each syndrome were stratified based on: (1) degree of functional impairment, as measured by the clinical dementia rating (CDR) scale, and (2) overall extent of brain atrophy, as measured by a neuroimaging approach that sums the number of brain voxels showing significantly lower gray matter volume than cognitively normal controls (n=80). Even at the earliest clinical stage (CDR=0.5 or bottom quartile of overall atrophy), patients with each syndrome showed both common and variant-specific atrophy. Common atrophy across variants was found in temporoparietal regions that comprise the posterior default mode network (DMN). Early syndrome-specific atrophy mirrored functional brain networks underlying functions that are uniquely affected in each variant: Language network in lvPPA, posterior cingulate cortex-hippocampal circuit in amnestic EOAD and LOAD, and visual networks in PCA. At more advanced stages, atrophy patterns largely converged across AD variants. These findings support a model in which neurodegeneration selectively targets both the DMN and syndrome-specific vulnerable networks at the earliest clinical stages of AD.
COVID-19 Vaccination Responses with Different Vaccine Platforms in Patients with Inborn Errors of Immunity

Patients with inborn errors of immunity (IEI) in Argentina were encouraged to receive licensed Sputnik, AstraZeneca, Sinopharm, Moderna, and Pfizer vaccines, even though most of the data of humoral and cellular responses combination on available vaccines comes from trials conducted in healthy individuals. We aimed to evaluate the safety and immunogenicity of the different vaccines in IEI patients in Argentina. The study cohort included adults and pediatric IEI patients (n = 118) and age-matched healthy controls (HC) (n = 37). B cell response was evaluated by measuring IgG anti-spike/receptor binding domain (S/RBD) and anti-nucleocapsid(N) antibodies by ELISA. Neutralization antibodies were also assessed with an alpha-S protein-expressing pseudo-virus assay. The T cell response was analyzed by IFN-γ secretion on S- or N-stimulated PBMC by ELISPOT and the frequency of S-specific circulating T follicular-helper cells (TFH) was evaluated by flow cytometry.No moderate/severe vaccine-associated adverse events were observed. Anti-S/RBD titers showed significant differences in both pediatric and adult IEI patients versus the age-matched HC cohort (p < 0.05). Neutralizing antibodies were also significantly lower in the patient cohort than in age-matched HC (p < 0.01). Positive S-specific IFN-γ response was observed in 84.5% of IEI patients and 82.1% presented S-specific TFH cells. Moderna vaccines, which were mainly administered in the pediatric population, elicited a stronger humoral response in IEI patients, both in antibody titer and neutralization capacity, but the cellular immune response was similar between vaccine platforms. No difference in humoral response was observed between vaccinated patients with and without previous SARS-CoV-2 infection.In conclusion, COVID-19 vaccines showed safety in IEI patients and, although immunogenicity was lower than HC, they showed specific anti-S/RBD IgG, neutralizing antibody titers, and T cell-dependent cellular immunity with IFN-γ secreting cells. These findings may guide the recommendation for a vaccination with all the available vaccines in IEI patients to prevent COVID-19 disease.
Stable, Low Power and Bit-Interleaving Aware SRAM Memory for Multi-Core Processing Elements

The machine learning and convolutional neural network (CNN)-based intelligent artificial accelerator needs significant parallel data processing from the cache memory. The separate read port is mostly used to design built-in computational memory (CRAM) to reduce the data processing bottleneck. This memory uses multi-port reading and writing operations, which reduces stability and reliability. In this paper, we proposed a self-adaptive 12T SRAM cell to increase the read stability for multi-port operation. The self-adaptive technique increases stability and reliability. We increased the read stability by refreshing the storing node in the read mode of operation. The proposed technique also prevents the bit-interleaving problem. Further, we offered a butterfly-inspired SRAM bank to increase the performance and reduce the power dissipation. The proposed SRAM saves 12% more total power than the state-of-the-art 12T SRAM cell-based SRAM. We improve the write performance by 28.15% compared with the state-of-the-art 12T SRAM design. The total area overhead of the proposed architecture compared to the conventional 6T SRAM cell-based SRAM is only 1.9 times larger than the 6T SRAM cell.
A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.
Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry

Remote sensing of physiological parameters could be a cost effective approach to improving health care, and low-power sensors are essential for remote sensing because these sensors are often energy constrained. This paper presents a power optimized photoplethysmographic sensor interface to sense arterial oxygen saturation, a technique to dynamically trade off SNR for power during sensor operation, and a simple algorithm to choose when to acquire samples in photoplethysmography. A prototype of the proposed pulse oximeter built using commercial-off-the-shelf (COTS) components is tested on 10 adults. The dynamic adaptation techniques described reduce power consumption considerably compared to our reference implementation, and our approach is competitive to state-of-the-art implementations. The techniques presented in this paper may be applied to low-power sensor interface designs where acquiring samples is expensive in terms of power as epitomized by pulse oximetry.
Sublime Experience for Sustainable Underground Space: Integration of the Artists' Works in Chichu Art Museum

This paper investigates a vision of the underground environment associated with an aesthetic discipline. Its fundamental notion is sublimity, which was a phenomenon that involved a number of artworks engaged with changing the perception of the underground experience. This paper seeks to clarify how the idea of the living environment underground has changed by examining the works of writers, painters, and architects who have drawn inspiration from the concept of imaginary underworlds. Through a case study of the Chichu Art Museum, a representative underground space in terms of a sustainable relationship between architectural spaces and nature that could be experienced as sublime, this paper considers how to integrate visitors to distribute their awareness of artists' work. It also stimulates visitors' perceptions of a more sustainable future through sublime experiences, offering a way to understand underground integration with artworks. Therefore, this paper contributes to the knowledge of the relationship between architecture and artwork by increasing the aesthetic value of the underground space and considering how art intervenes in architecture to create a sustainable didactic.
FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network

An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work, we describe FRODO (Free Rejection of Out-of-Distribution), a publicly available method that can be easily employed for any trained network to detect input data from a different distribution than is expected. FRODO uses the statistical distribution of intermediate layer outputs to define the expected in-distribution (ID) input image properties. New samples are judged based on the Mahalanobis distance (MD) of their layer outputs from the defined distribution. The method can be applied to any network, and we demonstrate the performance of FRODO in correctly rejecting OOD samples on three distinct architectures for classification, localization, and segmentation tasks in chest X-rays. A dataset of 21,576 X-ray images with 3,655 in-distribution samples is defined for testing. The remaining images are divided into four OOD categories of varying levels of difficulty, and performance at rejecting each type is evaluated using receiver operating characteristic (ROC) analysis. FRODO achieves areas under the ROC (AUC) of between 0.815 and 0.999 in distinguishing OOD samples of different types. This is shown to be comparable with the best-performing state-of-the-art method tested, with the substantial advantage that FRODO integrates seamlessly with any network and requires no extra model to be constructed and trained.
Encoding Time Series as Multi-Scale Signed Recurrence Plots for Classification Using Fully Convolutional Networks

Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to achieve accuracy. Such an approach has been shown to achieve impressive results, raising the interest of the community in it. However, it remains unsolved how to handle not only the variability in the distinctive region scale and the length of sequences but also the tendency confusion problem. In this paper, we tackle the problem using Multi-scale Signed Recurrence Plots (MS-RP), an improvement of RP, and propose a novel method based on MS-RP images and Fully Convolutional Networks (FCN) for TSC. This method first introduces phase space dimension and time delay embedding of RP to produce multi-scale RP images; then, with the use of asymmetrical structure, constructed RP images can represent very long sequences (>700 points). Next, MS-RP images are obtained by multiplying designed sign masks in order to remove the tendency confusion. Finally, FCN is trained with MS-RP images to perform classification. Experimental results on 45 benchmark datasets demonstrate that our method improves the state-of-the-art in terms of classification accuracy and visualization evaluation.