ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Feasibility of using PALSAR technology as a signal amplifier for antibody bridging assay

The immunogenicity testing of oligonucleotide drugs using an antibody bridging assay has been scarcely investigated. We developed a highly sensitive antibody bridging assay model and assessed it using probe alteration link self-assembly reactions (PALSAR) technology as a signal amplifier. Methods: The concentration of each probe was optimized, and the bridging assay model was compared with and without signal amplification. Cut-point and analytical sensitivity were determined, and accuracy, precision and drug tolerance were evaluated. Results: The PALSAR bridging assay achieved a net signal 21-36 times higher than that obtained with the conventional method. The analytical sensitivity achieved was 48.8 ng/ml, with adequate accuracy, precision and drug tolerance. Conclusion: PALSAR technology is feasible for developing an antibody bridging assay using oligonucleotides as capture and detection probes.
Semi-Supervised Learning With Deep Embedded Clustering for Image Classification and Segmentation

Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labeling data are a time-consuming and costly human (expert) intelligent task. Semi-supervised methods leverage this issue by making use of a small labeled dataset and a larger set of unlabeled data. In this paper, we present a flexible framework for semi-supervised learning that combines the power of supervised methods that learn feature representations using state-of-the-art deep convolutional neural networks with the deeply embedded clustering algorithm that assigns data points to clusters based on their probability distributions and feature representations learned by the networks. Our proposed semi-supervised learning algorithm based on deeply embedded clustering (SSLDEC) learns feature representations via iterations by alternatively using labeled and unlabeled data points and computing target distributions from predictions. During this iterative procedure, the algorithm uses labeled samples to keep the model consistent and tuned with labeling, as it simultaneously learns to improve feature representation and predictions. The SSLDEC requires a few hyper-parameters and thus does not need large labeled validation sets, which addresses one of the main limitations of many semi-supervised learning algorithms. It is also flexible and can be used with many stateof-the-art deep neural network configurations for image classification and segmentation tasks. To this end, we implemented and tested our approach on benchmark image classification tasks as well as in a challenging medical image segmentation scenario. In benchmark classification tasks, the SSLDEC outperformed several state-of-the-art semi-supervised learning methods, achieving 0.46% error on MNIST with 1000 labeled points and 4.43% error on SVHN with 500 labeled points. In the iso-intense infant brain MRI tissue segmentation task, we implemented SSLDEC on a 3D densely connected fully convolutional neural network where we achieved significant improvement over supervised-only training as well as a semi-supervised method based on pseudo-labeling. Our results show that the SSLDEC can be effectively used to reduce the need for costly expert annotations, enhancing applications, such as automatic medical image segmentation.
Split-Dose Administration Enhances Immune Responses Elicited by a mRNA/Lipid Nanoparticle Vaccine Expressing Respiratory Syncytial Virus F Protein

mRNA vaccines have recently received significant attention due to their role in combating the SARS-CoV-2 pandemic. As a platform, mRNA vaccines have been shown to elicit strong humoral and cellular immune responses with acceptable safety profiles for prophylactic use. Despite their potential, industrial challenges have limited realization of the vaccine platform on a global scale. Critical among these challenges are supply chain considerations, including mRNA production, cost of goods, and vaccine frozen-chain distribution. Here, we assess the delivery of lipid nanoparticle-encapsulated mRNA (mRNA/LNP) vaccines using a split-dose immunization regimen as an approach to develop mRNA dose-sparing vaccine regimens with potential to mitigate mRNA supply chain challenges. Our data demonstrate that immunization by a mRNA/LNP vaccine encoding respiratory syncytial virus pre-F (RSV pre-F) over a 9 day period elicits comparable or superior magnitude of antibodies when compared to traditional bolus immunization of the vaccine. The split-dose immunization regimens evaluated in our studies were designed to mimic reported drug or antigen release profiles from microneedle patches, highlighting the potential benefit of pairing mRNA vaccines with patch-based delivery technologies to enable sustained release and solid-state stabilization. Overall, our findings provide a proof of concept to support further investigations into the development of sustained delivery approaches for mRNA/LNP vaccines.
De novo variants and recombination at 4q35: Hints for preimplantation genetic testing in facioscapulohumeral muscular dystrophy

Facioscapulohumeral muscular dystrophy (FSHD) has been associated with the deletion of an integral number of 3.3 kb units of the polymorphic D4Z4 repeat array at 4q35. The prenatal identification of this defect can be carried out on chorionic villi or amniocytes, whereas preimplantation genetic testing for monogenic disorders (PGT-M) requires molecular markers linked to the D4Z4 allele of reduced size. In this context the reliability of this association is crucial. To test the informativeness of the nearby polymorphic markers we investigated recombination at 4q35 using the polymorphic markers D4S1523, D4S163 and D4S139 positioned at 0.55, 0.5 and 0.21 Mb proximal to the D4Z4 array respectively. We determined the probability of recombination events to occur in the D4Z4-D4S1523 interval considering 86 subjects belonging to 12 FSHD families and found a recombination frequency of 14% between D4Z4 and D4S1523. Our study also revealed the occurrence of de novo variants and germline mosaicism. These findings highlight the recombinogenic nature of the 4q subtelomere and indicate that caution should be taken when interpreting PGT-M results. It is advisable that a woman who underwent a PGT-M cycle undertakes a prenatal DNA analysis to confirm the size of the D4Z4 alleles carried by the fetus.
High precision U/Th dating of the rock paintings at Mt. Huashan, Guangxi, southern China

The rock art and the associated natural scenery at 38 sites located in the Zuojiang River valley, in the southwest of Guangxi Zhuang Autonomous Region, southern China, were inscribed recently on UNESCO's World Heritage List. The painted panel at the site of Mt. Huashan is probably the largest known rock art panel in the world, consisting of approximately 1900 identifiable figures and occupying an area of approximately 8000 m(2). To determine a precise age on the rock art at Mt. Huashan, 56 secondary carbonate layers above and below the paintings were studied for their mineralogy, oxygen, and carbon isotopic compositions and dated by the Th-230/U method. The Th-230/U dating results demonstrate that ages of the rock paintings can be bracketed between 1856 +/- 16 and 1728 +/- 41yr BP corresponding to the middle to the end of the Eastern Han dynasty (AD 25 to 220). The results imply that the rock painting practices at Mt. Huashan probably lasted more than a century, and the Zuojiang rock art is younger than that at Baiyunwan and Cangyuan in Yunnan Province by 1 to 10 centuries.
New mechanisms for the kidney-protective effect of alkali in chronic kidney disease

Worldwide, more than one in ten adults are estimated to have chronic kidney disease (CKD). As CKD progresses, both the cost of treatment and associated risk of morbidity and mortality increase exponentially. As such, there is a great need for therapies that effectively slow CKD progression. Evidence from several small clinical trials indicates that alkali therapy may slow the rate of CKD progression. The biological mechanisms underlying this protective effect, however, remain unknown. In their recently published manuscript, Pastor Arroyo et al. (Clin Sci (Lond) (2022) 136(8): https://doi.org/10.1042/CS20220095) demonstrate that the alkali sodium bicarbonate protects against loss of renal function in a crystal nephropathy model in mice. Using unbiased approaches in both mice and human tissue, the authors go on to identify two novel mechanisms that may underly this protection. The first pathway is through promoting pathways of cell metabolism, which they speculate helps the remaining functional nephrons adapt to the greater metabolic needs required to maintain kidney filtration. The second pathway is by restoration of α-Klotho levels, which may limit the expression of adhesion molecules in the injured kidney. This, the authors speculate, may prevent inflammation from driving the functional decline of the kidney. Identifying these novel pathways represents an important step forward harnessing the potential benefits of alkali therapy in CKD.
Physical, mechanical, and biological characterization of robocasted carbon nanotube reinforced microwave sintered calcium phosphate scaffolds for bone tissue engineering

This study analyses the influence of the addition of Multi Walled Carbon Nanotubes (MWCNT) on the physical, mechanical, and biological behaviour of Calcium Phosphate (CP) bone scaffolds developed using the robocasting technique for bone regeneration. Three different mass percentages (0.5, 1, and 2 wt%) of MWCNT are added to the CP powder and a slurry is prepared using a CMC binder for printing the scaffolds. The scaffolds were printed in 2 infill ratios, 50 and 100%, and were sintered under an inert atmosphere in a microwave furnace which was then taken for various characterization studies. Physical characterisation studies revealed that the shrinkage rate of scaffolds is very low compared to other additive manufacturing techniques. The incorporation of 0.5 wt% of MWCNT produced the best results in mechanical characterization studies with a compressive strength of 10.38 MPa and 11.89 MPa for 50% and 100% infill ratios respectively. In Vitro Biocompatibility studies also proved that 0.5 wt% MWCNT samples are the most suitable for cell growth while the hemocompatibility tests showed that the samples are blood compatible. . The 100% infill samples fared better than the 50% samples in physical and mechanical properties. The results suggest that the MWCNT incorporated CP scaffolds can be used to treat critical size bone defects.
Novel features for art movement classification of portrait paintings

The increasing availability of extensive digitized fine art collections opens up new research directions. In particular, correctly identifying the artistic style or art movement of paintings is crucial for large artistic database indexing, painter authentication, and mobile recognition of painters. Even though the implementation of CNN on artwork classification improved the performance dramatically compared to tradition classifier, the feature extraction methods are still valuable to help establishing better image representation for both common classifiers and neural networks. The main goal of this article is to present three novel features and a mature model structure for artistic movement recognition of portrait paintings. The proposed features include two unique color features and one texture feature: (a) Modified Color Distance (MCD), (b) ColorRatio Feature and (c) Weber's law Based Texture Feature. We demonstrate the superiority of our proposed method over the state-of-the-art approaches, and how successful our features are to support features from various neural networks. Another contribution of our work is a new portrait database that consists of 927 paintings from 6 different art movements. Extensive computer evaluations on this database show that we achieved an average accuracy of 98% for classifying two categories and 82.6% for classifying all 6 categories. Besides, our novel features improved the performance of pre trained CNN significantly. (c) 2021 Elsevier B.V. All rights reserved.
Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration

With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future.
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging

Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process, they do not provide any uncertainty information about the reconstructed image, which severely limits their use in practice, especially for safety-critical imaging applications. In this article, we propose a learning-based image reconstruction framework that incorporates the observation model into the reconstruction task and that is capable of quantifying epistemic and aleatoric uncertainties, based on deep unrolling and Bayesian neural networks. We demonstrate the uncertainty characterization capability of the proposed framework on magnetic resonance imaging and computed tomography reconstruction problems. We investigate the characteristics of the epistemic and aleatoric uncertainty information provided by the proposed framework to motivate future research on utilizing uncertainty information to develop more accurate, robust, trustworthy, uncertainty-aware, learning-based image reconstruction and analysis methods for imaging problems. We show that the proposed framework can provide uncertainty information while achieving comparable reconstruction performance to state-of-the-art deep unrolling methods.
Organ Preservation in Rectal Cancer: An Overview of the Dutch Perspective and Recent Developments

Although current guidelines on rectal cancer treatment often recommend rectal resection with or without neoadjuvant (chemo)radiotherapy, there is growing interest in organ-preserving treatment approaches among patients and clinicians in the Netherlands. Currently, multiple ongoing studies are investigating the value of different non-operative treatment modalities to improve tumour response rates and increase the chance of successful organ preservation. Papillon contact X-ray brachytherapy is a promising treatment modality to improve the chance of organ preservation, which seems especially relevant for elderly and frail patients unable or refusing to undergo total mesorectal excision surgery. The elderly and frail patient with rectal cancer poses a significant challenge and warrants a thorough multidisciplinary approach to provide the most optimal organ-preserving treatment. In this overview, an insight into the Dutch perspectives and developments within the field of organ preservation and the set-up of a Papillon facility to complete the spectrum of organ-preserving treatment options in a tertiary referral centre for rectal cancer treatment has been provided.
Improved compressive tracking based on pixelwise learner

This work expands upon state-of-the-art multiscale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixelwise classification stage is incorporated in the CT-based tracker to obtain a relatively stable appearance model, by distinguishing object pixels from the background. In addition, we identify potential distracting regions that are used in a feedback strategy to handle occlusion and avoid drifting toward nearby regions with similar appearances. We evaluate our approach on several benchmark datasets to demonstrate its effectiveness with respect to the state-of-the-art tracking algorithms. (c) 2018 SPIE and IS&T
Myrciaria Genus: Bioactive Compounds and Biological Activities

The Myrtaceae family is of angiosperms, imposing its size and economic, cultural, and scientific importance. The genus Myrciaria, belonging to this family, has 33 species currently accepted, many of which are research targets aimed at elucidating their bioactive compounds and biological activities. Most species of the Myrciaria genus have terpenes in their composition, mainly mono and sesquiterpenes, and phenolic compounds such as tannins, phenolic acids, and flavonoids. Other secondary metabolites are also observed, such as alkaloids, steroids, coumarins, saponins, and naphthoquinones. These bioactive compounds are closely related to these species' most diverse biological activities: antioxidant, anti-inflammatory, analgesic, antiproliferative, antimicrobial, antiparasitic, insecticide, metabolic, protective, and nutraceutical. This work aims to provide a review of secondary metabolites and medicinal properties related to the genus Myrciaria, thus stimulating further studies on the species of this genus.
Commercial Aircraft Electrification-Current State and Future Scope

Electric and hybrid-electric aircraft propulsion are rapidly revolutionising mobility technologies. Air travel has become a major focus point with respect to reducing greenhouse gas emissions. The electrification of aircraft components can bring several benefits such as reduced mass, environmental impact, fuel consumption, increased reliability and quicker failure resolution. Propulsion, actuation and power generation are the three key areas of focus in more electric aircraft technologies, due to the increasing demand for power-dense, efficient and fault-tolerant flight components. The necessity of having environmentally friendly aircraft systems has promoted the aerospace industry to use electrically powered drive systems, rather than the conventional mechanical, pneumatic or hydraulic systems. In this context, this paper reviews the current state of art and future advances in more electric technologies, in conjunction with a number of industrially relevant discussions. In this study, a permanent magnet motor was identified as the most efficient machine for aircraft subsystems. It is found to be 78% and 60% more power dense than switch-reluctant and induction machines. Several development methods to close the gap between existing and future design were also analysed, including the embedded cooling system, high-thermal-conductivity insulation materials, thin-gauge and high-strength electrical steel and integrated motor drive topology.
Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA

Deep Learning algorithms have become state-of-the-art methods for multiple fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks (CNN) stand out. This kind of network is expensive in terms of computational resources due to the large number of operations required to process a frame. In recent years, several frame-based chip solutions to deploy CNN for real time have been developed. Despite the good results in power and accuracy given by these solutions, the number of operations is still high, due the complexity of the current network models. However, it is possible to reduce the number of operations using different computer vision techniques other than frame-based, e.g., neuromorphic event-based techniques. There exist several neuromorphic vision sensors whose pixels detect changes in luminosity. Inspired in the leaky integrate-and-fire (LIF) neuron, we propose in this manuscript an event-based field-programmable gate array (FPGA) multiconvolution system. Its main novelty is the combination of a memory arbiter for efficient memory access to allow row-by-row kernel processing. This system is able to convolve 64 filters across multiple kernel sizes, from 1 x 1 to 7 x 7, with latencies of 1.3 mu s and 9.01 mu s, respectively, generating a continuous flow of output events. The proposed architecture will easily fit spike-based CNNs.