ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The possible association between exposure to air pollution and the risk for congenital malformations

Background: Over the last decade, there is growing evidence that exposure to air pollution may be associated with increased risk for congenital malformations. Objectives: To evaluate the possible association between exposures to air pollution during pregnancy and congenital malformations among infants born following spontaneously conceived (SC) pregnancies and assisted reproductive technology (ART) pregnancies. Methods: This is an historical cohort study comprising 216,730 infants: 207,825 SC infants and 8905 ART conceived infants, during the periods 1997-2004. Air pollution data including sulfur dioxide (SO2), particulate matter <10 mu m (PM10), nitrogen oxides (NOx) and ozone (O-3) were obtained from air monitoring stations database for the study period. Using a geographic information system (GIS) and the Kriging procedure, exposure to air pollution during the first trimester and the entire pregnancy was assessed for each woman according to her residential location. Logistic regression models with generalized estimating equation (GEE) approach were used to evaluate the adjusted risk for congenital malformations. Results: In the study cohort increased concentrations of PM10 and NOx pollutants in the entire pregnancy were associated with slightly increased risk for congenital malformations: OR 1.06(95% CI, 1.01-1.11) for 10 mu g/m(3) increase in PM10, and OR 1.03(95% CI, 1.01-1.04) for 10 ppb increase in NOR. Specific malformations were evident in the circulatory system (for PM10 and NOx exposure) and genital organs (for NOx exposure). SO2 and O-3 pollutants were not significantly associated with increased risk for congenital malformations. In the ART group higher concentrations of SO2 and O-3 in entire pregnancy were associated (although not significantly) with an increased risk for congenital malformations: OR 1.06 (95% CI, 0.96-1.17) for 1 ppb increase in SO2 and OR 1.15(95% CI, 0.69-1.91) for 10 ppb increase in O-3. Conclusions: Exposure to higher levels of PM10 and NOx during pregnancy was associated with an increased risk for congenital malformations. Specific malformations were evident in the circulatory system and genital organs. Among ART pregnancies possible adverse association of SO2 and O-3 exposure was also observed. Further studies are warranted, including more accurate exposure assessment and a larger sample size for ART pregnancies, in order to confirm these findings. (C) 2014 Elsevier Inc. All rights reserved.
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference onMedical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Inadequate sleep duration may attenuate the anti-inflammatory effects of fish consumption in a healthy Japanese population: a cross-sectional study

High fish consumption may be associated with lower inflammation, suppressing atherosclerotic CVD (ASCVD). Long sleep duration, as well as short sleep, may contribute to inflammation, thus facilitating ASCVD. This study investigated the overall association between fish consumption, sleep duration and leucocytes count. We conducted a cross-sectional study between April 2019 and March 2020 with a cohort of 8947 apparently healthy participants with no history of ASCVD (average age, 46·9 ± 12·3 years and 59 % males). The average frequency of fish consumption and sleep duration were 2·13 ± 1·26 d/week and 6·0 ± 0·97 h/d. Multivariate linear regression analysis revealed that increased fish consumption was an independent determinant of sleep duration (β = 0·084, P < 0·0001). Additionally, habitual aerobic exercise (β = 0·059, P < 0·0001) or cigarette smoking (β = −0·051, P < 0·0001) and homoeostasis model assessment-insulin resistance (HOMA-IR) (β = −0·039, P = 0·01) were independent determinants of sleep duration. Furthermore, multivariate linear regression analysis identified fish consumption as an independent determinant of leucocytes count (β = −0·091, P < 0·0001). However, a significant U-shaped curve was found between leucocytes count and sleep duration, with 6–7 h of sleep as the low value (P = 0·015). Higher fish consumption may be associated with a lower leucocytes count in the presence of adequate sleep duration and healthy lifestyle behaviors. However, long sleep duration was also related to increased inflammation, even in populations with high fish consumption. Further studies are needed to clarify the causality between these factors.
Development of SARS-CoV-2 animal vaccines using a stable and efficient NDV expression system

With the continuation of the coronavirus disease 2019 pandemic and the emergence of new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, the control of the spread of the virus remains urgent. Various animals, including cats, ferrets, hamsters, nonhuman primates, minks, tree shrews, fruit bats, and rabbits, are susceptible to SARS-CoV-2 infection naturally or experimentally. Therefore, to avoid animals from becoming mixing vessels of the virus, vaccination of animals should be considered. In the present study, we report the establishment of an efficient and stable system using Newcastle disease virus (NDV) as a vector to express SARS-CoV-2 spike protein/subunit for the rapid generation of vaccines against SARS-CoV-2 in animals. Our data showed that the S and S1 protein was sufficiently expressed in rNDV-S and rNDV-S1-infected cells, respectively. The S protein was incorporated into and displayed on the surface of rNDV-S viral particles. Intramuscular immunization with rNDV-S was found to induce the highest level of binding and neutralizing antibodies, as well as strong S-specific T-cell response in mice. Intranasal immunization with rNDV-S1 provoked a robust T-cell response but barely any detectable antibodies. Overall, the NDV-vectored vaccine candidates were able to induce profound humoral and cellular immunity, which will provide a good system for developing vaccines targeting both T-cell and antibody responses.
Marine viral particles reveal an expansive repertoire of phage-parasitizing mobile elements

Phage satellites are mobile genetic elements that propagate by parasitizing bacteriophage replication. We report here the discovery of abundant and diverse phage satellites that were packaged as concatemeric repeats within naturally occurring bacteriophage particles in seawater. These same phage-parasitizing mobile elements were found integrated in the genomes of dominant co-occurring bacterioplankton species. Like known phage satellites, many marine phage satellites encoded genes for integration, DNA replication, phage interference, and capsid assembly. Many also contained distinctive gene suites indicative of unique virus hijacking, phage immunity, and mobilization mechanisms. Marine phage satellite sequences were widespread in local and global oceanic virioplankton populations, reflecting their ubiquity, abundance, and temporal persistence in marine planktonic communities worldwide. Their gene content and putative life cycles suggest they may impact host-cell phage immunity and defense, lateral gene transfer, bacteriophage-induced cell mortality and cellular host and virus productivity. Given that marine phage satellites cannot be distinguished from bona fide viral particles via commonly used microscopic techniques, their predicted numbers (∼3.2 × 1026 in the ocean) may influence current estimates of virus densities, production, and virus-induced mortality. In total, the data suggest that marine phage satellites have potential to significantly impact the ecology and evolution of bacteria and their viruses throughout the oceans. We predict that any habitat that harbors bacteriophage will also harbor similar phage satellites, making them a ubiquitous feature of most microbiomes on Earth.
A Simplified Sphere Decoding-Based Detector for Generalized SCMA Codebooks

Sparse code multiple access (SCMA) is one of the promising schemes to meet high connectivity and spectral efficiency in the future wireless networks. The iterative detectors, for example message passing algorithm (MPA), can provide near optimal multiuser detection (MUD) performance but becomes infeasible when the codebook size is large or the overloading factor is high. Recently, sphere decoding (SD) has been considered in the MUD of SCMA by rewriting the generalized transmission into a linear system. In this work, we first review the state-of-the-art SD-based detectors for SCMA: sphere decoding for SCMA (SD-SCMA) and generalized SD-SCMA (GSD-SCMA). We not only explain the state-of-the-art in a comprehensive way, but also exploit the sorted QR decomposition and Schnorr-Euchner enumeration to accelerate the tree search. Although GSD-SCMA overcomes the codebook constraint of SD-SCMA, its computational complexity is extremely sensitive to the overloading factor. To satisfy the trade-off between complexity and MUD performance, we propose two pruning algorithms, PRUN1 and PRUN2, and introduce the simplified GSD-SCMA (SGSD-SCMA). In the paper, error probabilities of the proposed pruning algorithms are derived. Simulation results show that the proposed detector outperforms the iterative detectors and SD-based state-of-the-art when the overloading factor is moderate and the codebook size is large.
A Vitamin B2 -Photocatalysed Approach to Methionine Analogues

Access to new non-canonical amino acid residues is crucial for medicinal chemistry and chemical biology. Analogues of the amino acid methionine have been far less explored-despite their use in biochemistry, pharmacology and peptide bioconjugation. This is largely due to limited synthetic access. Herein, we exploit a new disconnection to access non-natural methionines through the development of a photochemical method for the radical α-C-H functionalization of sulfides with alkenes, in water, using inexpensive and commercially-available riboflavin (vitamin B2 ) as a photocatalyst. Our photochemical conditions allow the two-step synthesis of novel methionine analogues-by radical addition to unsaturated amino acid derivatives-and the chemoselective modification of peptide side-chains to yield non-natural methionine residues within small peptides. The mechanism of the bio-inspired flavin photocatalysis has been probed by experimental, DFT and TDDFT studies.
PDBsum1: A standalone program for generating PDBsum analyses

PDBsum1 is a standalone set of programs to perform the same structural analyses as provided by the PDBsum web server (https://www.ebi.ac.uk/pdbsum). The server has pages for every entry in the Protein Data Bank (PDB) and can also process user-uploaded PDB files, returning a password-protected set of pages that are retained for around 3 months. The standalone version described here allows for in-house processing and indefinite retention of the results. All data files and images are pre-generated, rather than on-the-fly as in the web version, so can be easily accessed. The program runs on Linux, Windows, and mac operating systems and is freely available for academic use at https://www.ebi.ac.uk/thornton-srv/software/PDBsum1.
Multiple-value exclusive-or sum-of-products minimization algorithms

In this work, a novel Multiple Valued Exclusive-Or Sum Of Products (MVESOP) minimization formulation is analyzed and an algorithm is presented that detects minimum MVESOP expressions when the weight of the function is less than eight. A heuristic MVESOP algorithm based on a novel cube transformation operation is then presented. Experimental results on MCNC benchmarks and randomly generated functions indicate that the algorithm matches or outperforms the quality of the state of the art in ESOP minimizers.
Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.
Bone-on-a-chip platforms and integrated biosensors: Towards advanced in vitro bone models with real-time biosensing

Bone diseases, such as osteoporosis and bone defects, often lead to structural and functional deformities of the patient's body. Understanding the complicated pathophysiology and finding new drugs for bone diseases are in dire need but challenging with the conventional cell and animal models. Bone-on-a-chip (BoC) models recapitulate key features of bone at an unprecedented level and can potentially shift the paradigm of future bone research and therapeutic development. Nevertheless, current BoC models predominantly rely on off-chip analysis which provides only endpoint measurements. To this end, integrating biosensors within the BoC can provide non-invasive, continuous monitoring of the experiment progression, significantly facilitating bone research. This review aims to summarize research progress in BoC and biosensor integrations and share perspectives on this exciting but rudimentary research area. We first introduce the research progress of BoC models in the study of bone remodeling and bone diseases, respectively. We then summarize the need for BoC characterization and reported works on biosensor integration in organ chips. Finally, we discuss the limitations and future directions of BoC models and biosensor integrations as next-generation technologies for bone research.
Behaviors Related to Medication Safety and Use During Pregnancy

Background: Most women take medication during pregnancy despite limited scientific evidence on safety. We investigated medication use, including changes in and reasons for changes in use during pregnancy, with attention to medication use in pregnant women with chronic conditions. Materials and Methods: We conducted an online survey of pregnant women aged ≥18 years (n = 1,226). We calculated descriptive statistics for aspects of medication use and performed multivariable logistic regression to examine associations between change in use and chronic conditions. Results: Seventy-nine percent of women took at least one medication during pregnancy. Among those, 63.2% made at least one medication change: 42.0% started, 34.9% stopped, 30.0% missed dose(s), and 18.1% lowered dose(s) from that originally prescribed or recommended. More than a third (36.5%) of women who stopped, lowered, or missed medication did so independent of health care provider advice; 54.0% cited concern about birth or developmental defects as reasons for change. Odds of medication change were higher for women with chronic conditions: digestive conditions-starting (adjusted odds ratio [AOR] = 1.8, 95% confidence interval [CI] = 1.1-2.7), stopping (AOR = 2.1, 95% CI = 1.4-3.3), and lowering (AOR = 2.4, 95% CI = 1.7-3.3) medication; mental health conditions-starting (AOR = 1.6, 95% CI = 1.2-2.2), stopping (AOR = 3.0, 95% CI = 2.3-4.0), or missing (AOR = 2.1, 95% CI = 1.6-2.8) medication; pain conditions-stopping (AOR = 2.9, 95% CI = 2.0-4.2); and respiratory conditions-starting (AOR = 2.0, 95% CI = 1.3-3.1), stopping (AOR = 1.7, 95% CI = 1.1-2.6), and missing (AOR = 2.2, 95% CI = 1.4-3.4) medication. Conclusions: Most pregnant women take medication and many, including those with chronic conditions, change their medication use during pregnancy. Medication change may occur independent of health care provider advice and due to women's safety concerns.
CO2 utilisation by photocatalytic conversion to methane and methanol

In this paper we intend to give a broad overview of natural and artificial photosynthesis systems. We point out seven orders of magnitude difference of the rates of water splitting between natural and state of the art artificial photosynthesis in favour of natural photosynthesis. We reviewed the open literature for photocatalytic water splitting, CO2 reduction and suggested routes for improvements in the low yields.
MEG: Memory and Energy Efficient Garbled Circuit Evaluation on Smartphones

Garbled circuits are general tools that allow two parties to compute any function without disclosing their respective inputs. Applications of this technique vary from distributed privacy-preserving machine learning tasks to secure outsourced authentication. Unfortunately, the energy cost of garbled circuit evaluation protocols is substantial. This limits the applicability of garbled circuits in scenarios that involve battery-operated devices, such as Internet-of-Things (IoT) devices and smartphones. In this paper, we propose MEG, a Memory-and Energyefficient Garbled circuit evaluation mechanism. MEG utilizes batch data transmission and multi-threading to reduce memory and energy consumption. We implement MEG on an Android smartphone and compare its performance and energy consumption with state-of-the-art techniques using two garbled circuits of widely different sizes (AES-128 and 256-bit edit distance). Our results show that, compared with " plain" garbled circuit evaluation, MEG decreases memory consumption by more than 90%. When compared with current pipelined garbled circuit evaluation techniques, MEG's energy usage was 42% lower for AES-128 and 23% lower for EDT-256. Furthermore, our multi-thread implementation of MEG decreased circuit evaluation time by up to 56.7% for AES-128, and by up to 13.5% for EDT-256, compared with state-of-the-art pipelining techniques.
Suspension-Aware Earliest-Deadline-First Scheduling Analysis

While the earliest-deadline-first (EDF) scheduling algorithm has extensively been utilized in real-time systems, there is almost no literature considering EDF for task sets with dynamic self-suspension behavior. To be precise, there is no specialized result for uniprocessor systems, besides the trivial suspension-oblivious approach. The work by Liu and Anderson (in ECRTS 2013) and Dong and Liu (in RTSS 2016) for suspension-aware multiprocessor global EDF can also be applied to uniprocessor systems and therefore be considered the state-of-the-art. In this work, two novel schedulability analyses (one for sporadic and one for periodic task sets) for suspension-aware EDF on uniprocessor systems are proposed, which outperform the state-of-the-art on such systems in empirical and theoretical comparison. We further show that the analysis by Dong and Liu is in fact not suspension-aware for uniprocessor systems.
Neural Stem Cells Secreting Anti-HER2 Antibody Improve Survival in a Preclinical Model of HER2 Overexpressing Breast Cancer Brain Metastases

The treatment of human epidermal growth factor receptor 2 (HER2)-overexpressing breast cancer has been revolutionized by trastuzumab. However, longer survival of these patients now predisposes them to forming HER2 positive brain metastases, as the therapeutic antibodies cannot cross the blood brain barrier. The current oncologic repertoire does not offer a rational, nontoxic targeted therapy for brain metastases. In this study, we used an established human neural stem cell line, HB1.F3 NSCs and generated a stable pool of cells secreting a high amount of functional full-length anti-HER2 antibody, equivalent to trastuzumab. Anti-HER2Ab secreted by the NSCs (HER2Ab-NSCs) specifically binds to HER2 overexpressing human breast cancer cells and inhibits PI3K-Akt signaling. This translates to HER2Ab-NSC inhibition of breast cancer cell growth in vitro. Preclinical in vivo experiments using HER2Ab overexpressing NSCs in a breast cancer brain metastases (BCBM) mouse model demonstrate that intracranial injection of HER2Ab-NSCs significantly improves survival. In effect, these NSCs provide tumor localized production of HER2Ab, minimizing any potential off-target side effects. Our results establish HER2Ab-NSCs as a novel, nontoxic, and rational therapeutic approach for the successful treatment of HER2 overexpressing BCBM, which now warrants further preclinical and clinical investigation.