ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Integrated Passive Devices and Switching Circuit Design for a 3D DC/DC Converter up to 60 V

This work presents the design and test of a switched-cap 3D DC/DC converter able to work up to 60V. The switches and the control circuits are integrated single-chip in a high-voltage (HV) MOS technology, and the passive devices are stacked on top of the chip. As an innovation versus the state-of-the-art, the work first presents the design of integrated passive devices, based on through silicon vias (TSV) MOS-compatible technology, which are suitable for switching converter applications up to 60V. Then, the implementation and experimental characterization of the switched-cap 3D DC/DC is proposed, with the silicon TSV capacitors stacked on top of the 0.35 mu m HV-MOS die. Compared with the state-of-the-art, the proposed 3D DC/DC converter is a compact circuit, able to directly regulate a wide input voltage range (from 6V to 60V) to a 5V, 2W output. Hence, it is suitable to supply low-power loads, such as control units and/or sensors, directly from the 48V power line available in hybrid vehicles or telecom and networking systems.
Stratified pooling based deep convolutional neural networks for human action recognition

Video based human action recognition is an active and challenging topic in computer vision. Over the last few years, deep convolutional neural networks (CNN) has become the most popular method and achieved the state-of-the-art performance on several datasets, such as HMDB-51 and UCF-101. Since each video has a various number of frame-level features, how to combine these features to acquire good video-level feature becomes a challenging task. Therefore, this paper proposed a novel action recognition method named stratified pooling, which is based on deep convolutional neural networks (SP-CNN). The process is mainly composed of five parts: (i) fine-tuning a pre-trained CNN on the target dataset, (ii) frame-level features extraction; (iii) the principal component analysis (PCA) method for feature dimensionality reduction; (iv) stratified pooling frame-level features to get video-level feature; and (v) SVM for multiclass classification. Finally, the experimental results conducted on HMDB-51 and UCF-101 datasets show that the proposed method outperforms the state-of-the-art.
Customized valorization of waste streams by Pseudomonas putida: State-of-the-art, challenges, and future trends

Preventing catastrophic climate events warrants prompt action to delay global warming, which threatens health and food security. In this context, waste management using engineered microbes has emerged as a long-term eco-friendly solution for addressing the global climate crisis and transitioning to clean energy. Notably, Pseudomonas putida can valorize industry-derived synthetic wastes including plastics, oils, food, and agricultural waste into products of interest, and it has been extensively explored for establishing a fully circular bioeconomy through the conversion of waste into bio-based products, including platform chemicals (e.g., cis,cis-muconic and adipic acid) and biopolymers (e.g., medium-chain length polyhydroxyalkanoate). However, the efficiency of waste pre-treatment technologies, capability of microbial cell factories, and practicability of synthetic biology tools remain low, posing a challenge to the industrial application of P. putida. The present review discusses the state-of-the-art, challenges, and future prospects for divergent biosynthesis of versatile products from waste-derived feedstocks using P. putida.
A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model With a Hybrid FPGA Implementation

Computing and attending to salient regions of a visual scene is an innate and necessary preprocessing step for both biological and engineered systems performing high-level visual tasks including object detection, tracking, and classification. Computational bandwidth and speed are improved by preferentially devoting computational resources to salient regions of the visual field. The human brain computes saliency effortlessly, but modeling this task in engineered systems is challenging. We first present a neuromorphic dynamic saliency model, which is bottom-up, feed-forward, and based on the notion of proto-objects with neurophysiological spatio-temporal features requiring no training. Our neuromorphic model outperforms state-of-the-art dynamic visual saliency models in predicting human eye fixations (i.e., ground truth saliency). Secondly, we present a hybrid FPGA implementation of the model for real-time applications, capable of processing 112 x 84 resolution frames at 18.71 Hz running at a 100 MHz clock rate-a 23.77xspeedup from the software implementation. Additionally, our fixed-point model of the FPGA implementation yields comparable results to the software implementation.
Non-human contributions to personality neuroscience - from fish through primates. An introduction to the special issue

The most fundamental emotional systems that show trait control are evolutionarily old and extensively conserved. Psychology in general has benefited from non-human neuroscience and from the analytical simplicity of behaviour in those with simpler nervous systems. It has been argued that integration between personality, psychopathology, and neuroscience is particularly promising if we are to understand the neurobiology of human experience. Here, we provide some general arguments for a non-human approach being at least as productive in relation to personality, psychopathology, and their interface. Some early personality theories were directly linked to psychopathology (e.g., Eysenck, Panksepp, and Cloninger). They shared a common interest in brain systems that naturally led to the use of non-human data; behavioural, neural, and pharmacological. In Eysenck's case, this also led to the selective breeding, at the Maudsley Institute, of emotionally reactive and non-reactive strains of rat as models of trait neuroticism or trait emotionality. Dimensional personality research and categorical approaches to clinical disorder then drifted apart from each other, from neuropsychology, and from non-human data. Recently, the conceptualizations of both healthy personality and psychopathology have moved towards a common hierarchical trait perspective. Indeed, the proposed two sets of trait dimensions appear similar and may even be eventually the same. We provide, here, an introduction to this special issue of Personality Neuroscience, where the authors provide overviews of detailed areas where non-human data inform human personality and its psychopathology or provide explicit models for translation to human neuroscience. Once all the papers in the issue have appeared, we will also provide a concluding summary of them.
E. coli biosensor based on modular GFP and luxI/luxR cyclic amplification circuit for sensitive detection of lysine

In this study, an E. coli biosensor based on modular green fluorescent protein and luxI/IuxR cycle amplification circuit was constructed for sensitive detection of bioavailable lysine. The results indicated that the luxI/IuxR positive feedback circuit based on quorum sensing can be used as a signal amplifier to improve the sensitivity to lysine detection with the detection limit of 256 nM. The presented method was more sensitive than the previously reported whole-cell fluorescent microbial biosensors. In addition, the developed E. coli biosensor was specific for lysine detection, and other amino acids and proteins did not cause any interference. The constructed genetic engineered biosensor was accurate for lysine detection, the lysine content of 6.87 ± 0.36% in tryptone was successfully measured, and after adding 10, 30, and 50 μM lysine in tryptone, the recoveries of 109.98 ± 10.44%, 103.88 ± 7.66%, and 105.89 ± 6.34% were obtained, respectively. Furthermore, as the design of the genetic engineered biosensor is modular, it can conceivably be utilized as a component in the design of more complex synthetic gene circuits without any changes to the amplifier and reporter system.
A Splitting-Based Iterative Algorithm for Accelerated Statistical X-Ray CT Reconstruction

Statistical image reconstruction using penalized weighted least-squares (PWLS) criteria can improve image-quality in X-ray computed tomography (CT). However, the huge dynamic range of the statistical weights leads to a highly shift-variant inverse problem making it difficult to precondition and accelerate existing iterative algorithms that attack the statistical model directly. We propose to alleviate the problem by using a variable-splitting scheme that separates the shift-variant and ("nearly") invariant components of the statistical data model and also decouples the regularization term. This leads to an equivalent constrained problem that we tackle using the classical method-of-multipliers framework with alternating minimization. The specific form of our splitting yields an alternating direction method of multipliers (ADMM) algorithm with an inner-step involving a "nearly" shift-invariant linear system that is suitable for FFT-based preconditioning using cone-type filters. The proposed method can efficiently handle a variety of convex regularization criteria including smooth edge-preserving regularizers and non-smooth sparsity-promoting ones based on the l(1)-norm and total variation. Numerical experiments with synthetic and real in vivo human data illustrate that cone-filter preconditioners accelerate the proposed ADMM resulting in fast convergence of ADMM compared to conventional (nonlinear conjugate gradient, ordered subsets) and state-of-the-art (MFISTA, split-Bregman) algorithms that are applicable for CT.
Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.
Palliative Care Utilization in Burn Patients

Background: Burn injuries are a common cause for hospitalization, and severe burns have an increased risk of death in patients with advanced age, inhalational injury, comorbid conditions. Very little is known about the utilization of palliative care consultation in burn patients. Objective: The aim of this study was to evaluate the factors influencing the utilization of inpatient palliative care consultation for patients with severe burn injuries. Methods: This was a retrospective chart review study at a single burn center. Results: Seventeen out of 191 patients (8.9%) received a palliative care consultation with the average time for consultation of 10.3 days. Factors that appear to impact consultation were age, presence of inhalational injury, and multiple comorbid conditions. Conclusion: Inpatient palliative care consultation was underutilized in patients with severe burn injurie. Further research into the outcomes of palliative care consultation could help further support the utility of early involvement in burn patients.
The education of art culture at Sultanate of Oman through the multidisciplinary integration between graphic design and eco-friendly textile printing. Part 1: Standardization of extraction and dyeing with natural wastes products

Scientific research today is marked by a growing differentiation and specialization in the disciplines and interdisciplinary research. This paper investigates a number of challenges of Interdisciplinary collaborations in implementation of a circular economy approach within textile and graphic designs. These challenges range from achieving economy of scale required for commercial viability and finding secondary markets for the recycled materials to developing 'resource efficient recycling processes' that are especially tailored to the specific needs of eco-friendly textile products. Art Education Department at Sultan Qaboos University created interdisciplinary education strategy through an artistic relevant, and engaging the textile printing and graphic design courses in a project. The aim of this project is to apply some natural waste products which contain pigments to be served as recycling sources for natural dyes in textile coloration regarding their increasing interest due to their biodegradability and environmental awareness. The research project was divided in two parts; part one depends on studying the optimum condition of extraction with different co-solvent on different textile fabrics, as the extraction can be an energy, economic and environmental friendly by waste recycling. Part two deals on studying the mixing of the resulted extractions in producing a variety of hues under the effect of different pH values, mordants and mordanting methods to promote artistic printed textile patterns assisted by the graphic design. Therefore, students are introduced to different and integrated ways of thinking, aiming to make them more aware of circular economy and sustainability issues in all of their art activities, from personal to professional environments. As a result, this research is considered as a double scientific leap in terms of development strategies for teaching art curricula through the Multidisciplinary Integration in serving the community and the environment. (C) 2020 Published by Elsevier Ltd.
Coadministration of seasonal influenza and COVID-19 vaccines: A systematic review of clinical studies

The lifting of non-pharmaceutical measures preventing transmission of SARS-CoV-2 (and other viruses, including influenza viruses) raises concerns about healthcare resources and fears of an increased number of cases of influenza and COVID-19. For the 2021-2022 influenza season, the WHO and >20 European countries promoted coadministration of influenza and COVID-19 vaccines. Recently, the French Health Authority recommended coupling the COVID-19 vaccination with the 2022-2023 influenza vaccination campaign for healthcare professionals and people at risk of severe COVID-19. The present systematic review examines published data on the safety, immunogenicity, efficacy/effectiveness, and acceptability/acceptance of coadministration of influenza and COVID-19 vaccines. No safety concerns or immune interferences were found whatever the vaccines or the age of vaccinated subjects (65- or 65+). No efficacy/effectiveness data were available. The results should reassure vaccinees and vaccinators in case of coadministration and increase vaccine coverage. Healthcare systems promoting coupled campaigns must provide the necessary means for successful coadministration.
[Incidence and causes of early end in awake surgery for language mapping not directly related to eloquence]

The incidence and causes that may lead to an early end (unfinished cortical/subcortical mapping) of awake surgery for language mapping are little known. A study was conducted on 41 patients with brain glioma located in the language area that had awake surgery under conscious sedation. Surgery was ended early in 6 patients. The causes were: tonic-clonic seizure (1), lack of cooperation due to fatigue/sleep (4), whether or not word articulation was involved, a decreased level of consciousness for ammonia encephalopathy that required endotracheal intubation (1). There are causes that could be expected and in some cases avoided. Tumour size, preoperative aphasia, valproate treatment, and type of anaesthesia used are variables to consider to avoid failure in awake surgery for language mapping. With these results, the following measures are proposed: l) If the tumour is large, perform surgery in two times to avoid fatigue, 2) if patient has a preoperative aphasia, do not use sedation during surgery to ensure that sleepiness does not cause worse word articulation, 3) if the patient is on valproate treatment, it is necessary to rule out the pre-operative symptoms that are not due to ammonia encephalopathy.
miRNA-15a/16: as tumor suppressors and more

Since their first discovery in chronic lymphocytic leukemia, miR-15a and miR-16 have been reported to act as tumor suppressors or potential oncomiRs in different types of cancer. This review summarizes the history, biological properties and the important functions of these two miRNAs in cancer. It also introduces their roles as regulators of immune responses and angiogenesis, endogenous controls as well as potential targets and hallmarks of cancer.
Welder's Anthrax: A Tale of 2 Cases

Bacillus anthracis has traditionally been considered the etiologic agent of anthrax. However, anthrax-like illness has been documented in welders and other metal workers infected with Bacillus cereus group spp. harboring pXO1 virulence genes that produce anthrax toxins. We present 2 recent cases of severe pneumonia in welders with B. cereus group infections and discuss potential risk factors for infection and treatment options, including antitoxin.
Improvements on deinterleaving of radar pulses in dynamically varying signal environments

An electronic support system receiver which is a passive receiver picks up an interleaved stream of pulses and extracts their pulse parameters. These parameters are sent to a deinterleaving subsystem which sorts them and forms pulse cells that each are assumed to belong to a specific emitter. In this paper, we develop a method for this task of deinterleaving of radar pulse sequences. For this aim, a novel pulse amplitude tracking algorithm is proposed for dynamically varying signal environments wherein radar parameters can change abruptly. This method particularly works for air-to-air engagements where pulse amplitude distortion due to channel effects can be considered negligible. Simulation results show that the proposed algorithm incorporated with a clustering algorithm improves deinterleaving of radar emitters that have agile pulse parameters such as airborne radars. (C) 2017 Elsevier Inc. All rights reserved.
Tumor Microenvironment-Adaptive Nanoplatform Synergistically Enhances Cascaded Chemodynamic Therapy

Chemodynamic therapy (CDT), a noninvasive strategy, has emerged as a promising alternative to conventional chemotherapy for treating tumors. However, its therapeutic effect is limited by the amount of H2O2, pH value, the hypoxic environment of tumors, and it has suboptimal tumor-targeting ability. In this study, tumor cell membrane-camouflaged mesoporous Fe3O4 nanoparticles loaded with perfluoropentane (PFP) and glucose oxidase (GOx) are used as a tumor microenvironment-adaptive nanoplatform (M-mFeP@O2-G), which synergistically enhances the antitumor effect of CDT. Mesoporous Fe3O4 nanoparticles are selected as inducers for photothermal and Fenton reactions and as nanocarriers. GOx depletes glucose within tumor cells for starving the cells, while producing H2O2 for subsequent ·OH generation. Moreover, PFP, which can carry O2, relieves hypoxia in tumor cells and provides O2 for the cascade reaction. Finally, the nanoparticles are camouflaged with osteosarcoma cell membranes, endowing the nanoparticles with homologous targeting and immune escape abilities. Both in vivo and in vitro evaluations reveal high synergistic therapeutic efficacy of M-mFeP@O2-G, with a desirable tumor-inhibition rate (90.50%), which indicates the great potential of this platform for clinical treating cancer.
Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval

Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., sparse hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (high-dimensional) image features into a low-dimensional Hamming space, while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space. We argue that the anchor set utilized in sparse representation is also crucial, which was unfortunately underestimated by the prior art. To this end, we propose a novel SH method that optimizes the integration of the anchors, such that the features can be better embedded and binarized, termed as Sparse Hashing with Optimized Anchor Embedding. The central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features. We formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited. Extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state-of-the-art related methods.
Consistency of Eye Coloration Across Different Relationship Partners

Studies have indicated that people are attracted to partners who resemble themselves or their parents, in terms of physical traits including eye color. We might anticipate this inclination to be relatively stable, giving rise to a sequential selection of similar partners who then represent an individual's "type". We tested this idea by examining whether people's sequential partners resembled each other at the level of eye color. We gathered details of the eye colors of the partners of participants (N = 579) across their adult romantic history (N = 3250 relationships), in three samples, comprising two samples which made use of self-reports from predominantly UK-based participants, and one which made use of publicly available information about celebrity relationship histories. Recorded partner eye colors comprised black (N = 39 partners), dark brown (N = 884), light brown (N = 393), hazel (N = 224), blue (N = 936), blue green (N = 245), grey (N = 34), and green (N = 229). We calculated the proportion of identical eye colors within each participant's relationship history, and compared that to 100,000 random permutations of our dataset, using t-tests to investigate if the eye color of partners across an individual's relationship history was biased relative to chance (i.e., if there was greater consistency, represented by higher calculated proportions of identical eye colors, in the original dataset than in the permutations). To account for possible eye color reporting errors and ethnic group matching, we ran the analyses restricted to White participants and to high-confidence eye color data; we then ran the analyses again in relation to the complete dataset. We found some limited evidence for some consistency of eye color across people's relationship histories in some of the samples only when using the complete dataset. We discuss the issues of small effect sizes, partner-report bias, and ethnic group matching in investigating partner consistency across time.