ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Positron emission tomography in autoimmune encephalitis: Clinical implications and future directions

18 F-fluoro-deoxyglucose position emission tomography (18 F-FDG-PET) has been proven as a sensitive and reliable tool for diagnosis of autoimmune encephalitis (AE). More attention was paid to this kind of imaging because of the shortage of MRI, EEG, and CSF findings. FDG-PET has been assessed in a few small studies and case reports showing apparent abnormalities in cases where MRI does not. Here, we summarized the patterns (specific or not) in AE with different antibodies detected and the clinical outlook for the wide application of FDG-PET considering some limitations. Specific patterns based on antibody subtypes and clinical symptoms were critical for identifying suspicious AE, the most common of which was the anteroposterior gradient in anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and the medial temporal lobe hypermetabolism in limbic encephalitis. And the dynamic changes of metabolic presentations in different phases provided us the potential to inspect the evolution of AE and predict the functional outcomes. Except for the visual assessment, quantitative analysis was recently reported in some voxel-based studies of regions of interest, which suggested some clues of the future evaluation of metabolic abnormalities. Large prospective studies need to be conducted controlling the time from symptom onset to examination with the same standard of FDG-PET scanning.
Structural and Genomic Evolution of RRNPPA Systems and Their Pheromone Signaling

In Firmicutes, important processes such as competence development, sporulation, virulence, and biofilm formation are regulated by cytoplasmic quorum sensing (QS) receptors of the RRNPPA family using peptide-based communication. Although these systems regulate important processes in a variety of bacteria, their origin and diversification are poorly understood. Here, we integrate structural, genomic, and phylogenetic evidence to shed light on RRNPPA protein origin and diversification. The family is constituted by seven different subfamilies with different domain architectures and functions. Among these, three were found in Lactobacillales (Rgg, ComR, and PrgX) and four in Bacillales (AimR, NprR, PlcR, and Rap). The patterns of presence and the phylogeny of these proteins show that subfamilies diversified a long time ago, resulting in key structural and functional differences. The concordance between the distribution of subfamilies and the bacterial phylogeny was somewhat unexpected, since many of the subfamilies are very abundant in mobile genetic elements, such as phages, plasmids, and phage-plasmids. The existence of diverse propeptide architectures raises intriguing questions about their export and maturation. It also suggests the existence of diverse roles for the RRNPPA systems. Some systems encode multiple pheromones on the same propeptide or multiple similar propeptides, suggesting that they act as "chatterers." Many others lack pheromone genes and may be "eavesdroppers." Interestingly, AimR systems without associated propeptide genes were particularly abundant in chromosomal regions not classed as prophages, suggesting that either the bacterium or other mobile elements are eavesdropping on phage activity. IMPORTANCE Quorum sensing (QS) is a mechanism of bacterial communication, coordinating important decisions depending on bacterial population. QS regulates important processes not only in bacterial behavior but also in genetic mobile elements and host-guest interactions. In Firmicutes, the most important family of QS receptors is the RRNPPA family. Despite the importance of such systems in microbiology, we know little about RRNPPA origin and diversification. In this work, the combination of sequence analysis and structural biology allowed us to identify a very large number of novel systems but also to class of them in functional families and thereby study of their origin and functional diversification. Moreover, peptide pheromone analysis revealed new and intriguing mechanisms of communication, such as "eavesdropper" systems which only listen for the pheromone and "chatterers" that take control of the communication in their microenvironment.
Prevalence and prediction of dropout during depression treatment in routine outpatient care: an observational study

Efficacious treatments are available for major depressive disorder (MDD), but treatment dropout is common and decreases their effectiveness. However, knowledge about prevalence of treatment dropout and its risk factors in routine care is limited. The objective of this study was to determine the prevalence of and risk factors for dropout in a large outpatient sample. In this retrospective cohort analysis, routinely collected data from 2235 outpatients with MDD who had a diagnostic work-up between 2014 and 2016 were examined. Dropout was defined as treatment termination without achieving remission before the fourth session within six months after its start. Total and item scores on the Dutch Measure for Quantification of Treatment Resistance in Depression (DM-TRD) at baseline, and demographic variables were analyzed for their association with dropout using logistic regression and elastic net analyses. Data of 987 subjects who started routine outpatient depression treatment were included in the analyses of which 143 (14.5%) dropped out. Higher DM-TRD-scores were predictive for lower dropout odds [OR = 0.78, 95% CI = (0.70-0.86), p < 0.001]. The elastic net analysis revealed several clinical variables predictive for dropout. Higher SES, higher depression severity, comorbid personality pathology and a comorbid anxiety disorder were significantly associated with less dropout in the sample. In this observational study, treatment dropout was relatively low. The DM-TRD, an easy-to-use clinical instrument, revealed several variables associated with less dropout. When applied in daily practice and combined with demographical information, this instrument may help to reduce dropout and increase treatment effectiveness.
Consumer opinion on social policy approaches to promoting positive body image: Airbrushed media images and disclaimer labels

Disclaimer labels on airbrushed media images have generated political attention and advocacy as a social policy approach to promoting positive body image. Experimental research suggests that labelling is ineffective and consumers' viewpoints have been overlooked. A mixed-method study explored British consumers' ( N = 1555, aged 11-78 years) opinions on body image and social policy approaches. Thematic analysis indicated scepticism about the effectiveness of labelling images. Quantitatively, adults, although not adolescents, reported that labelling was unlikely to improve body image. Appearance diversity in media and reorienting social norms from appearance to function and health were perceived as effective strategies. Social policy and research implications are discussed.
Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.
Comparison of the biodegradability of the grey fraction of municipal solid waste of Barcelona in mesophilic and thermophilic conditions

The results of the start-up of two digesters in mesophilic and thermophilic conditions, together with its steady results at several organic loading rates are described. A kinetic study is also carried out which allows one to estimate the ultimate methane production, together with the first-order kinetic constant. Operation at thermophilic temperature yields better results as it allows a more loaded reactor and the methane production is slightly higher.
Computer-aided design of the RF-cavity for a high-power S-band klystron

This article describes the computer-aided design of the RF-cavity for a S-band klystron operating at 2856 MHz. State-of-the-art electromagnetic simulation tools SUPERFISH, CST Microwave studio, HFSS and MAGIC have been used for cavity design. After finalising the geometrical details of the cavity through simulation, it has been fabricated and characterised through cold testing. Detailed results of the computer-aided simulation and cold measurements are presented in this article.
Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leveragesa chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate-the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256 x 256 image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.
Taxonomy for Evaluation of Distributed Control Strategies for Distributed Finergy Resources

Distributed control strategies applied to power distribution control problems are meant to offer robust and scalable integration of distributed energy resources (DERs). However, the term "distributed control" is often loosely applied to a variety of very different control strategies. In particular there is a lack of discrimination between aspects related to communication topology, physical distribution of components, and associated control objectives. This has lead to a lack of objective criteria for performance comparison and general quality assessment of state of the art distributed control solutions. For such comparison, a classification is required that is consistent across the different aspects mentioned above. This paper develops systematic categories of control strategies that accounts for communication, control and physical distribution aspects of the problem, and provides a set of criteria that can be assessed for these categories. The proposed taxonomy is applied to the state of the art as part of a review of existing work on distributed control of DER. Finally, we demonstrate the applicability and usefulness of the proposed classification to researchers and system designers.
Adaptive Multi-Objective Evolutionary Algorithms for Overtime Planning in Software Projects

Software engineering and development is well-known to suffer from unplanned overtime, which causes stress and illness in engineers and can lead to poor quality software with higher defects. Recently, we introduced a multi-objective decision support approach to help balance project risks and duration against overtime, so that software engineers can better plan overtime. This approach was empirically evaluated on six real world software projects and compared against state-of-the-art evolutionary approaches and currently used overtime strategies. The results showed that our proposal comfortably outperformed all the benchmarks considered. This paper extends our previous work by investigating adaptive multi-objective approaches to meta-heuristic operator selection, thereby extending and (as the results show) improving algorithmic performance. We also extended our empirical study to include two new real world software projects, thereby enhancing the scientific evidence for the technical performance claims made in the paper. Our new results, over all eight projects studied, showed that our adaptive algorithm outperforms the considered state of the art multi-objective approaches in 93 percent of the experiments (with large effect size). The results also confirm that our approach significantly outperforms current overtime planning practices in 100 percent of the experiments (with large effect size).
Nuclear organization of the rock hyrax (Procavia capensis) amygdaloid complex

The current study details the nuclear organization of the rock hyrax amygdaloid complex using both Nissl and myelin stains, along with a range of immunohistochemical stains. The rock hyrax appears to be the least derived of the Afrotherians, a group with a huge range of body phenotypes, life histories and specialized behaviours, brain sizes, and ecological niches. In this sense, the rock hyrax represents a species where the organization of the amygdaloid complex may be reflective of that in stem Eutherian mammals. Our analysis indicates that the nuclear organization of the rock hyrax amygdaloid complex is indeed very similar to that in other mammals studied, with four major nuclear groupings (the deep or basolateral group; the superficial or cortical-like or corticomedial group; the centromedial group; and the other amygdaloid nuclei) being observed, which is typical of Eutherian mammals. Moreover, each of these groupings is composed of several nuclei, the vast majority of which were readily identified in the rock hyrax. Small nuclei identified in rodents and primates were absent in the superficial and centromedial groups, seemingly involved with olfaction. A novel shell-like nucleus of the accessory basal nuclear cluster was observed in the rock hyrax, again, likely to be involved in olfaction. The current study underlines the conserved nature of nuclear parcellation in the Eutherian mammal amygdaloid complex and indicates that across most species, the flow of information processing related to species-specific affective-laden stimuli and the resultant physiological and behavioural outcomes are likely to be similar across species.
Dual-Wavelength Bit Input Optical RAM With Three SOA-XGM Switches

We demonstrate a novel all-optical static RAM cell that exploits wavelength diversity in the incoming optical streams towards reducing the number of active elements. The circuit requires only three semiconductor optical amplifiers-cross gain modulation gates for successful read/write operation, yielding a 25% reduction in power consumption compared to state-of-the-art configurations. Proof-of-concept experimental verification is presented at 8 Mb/s using fiber-interconnected off-the-shelf bulk components.
EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes

Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% - 99.89%; precision: 91.90% - 99.45%; sensitivity: 91.61% - 99.53%; specificity: 91.60% - 99.96%; f1 score: 91.70% - 99.48%; and area under the curve: 0.9688 - 0.9998).
Three new cytotoxic annonaceous acetogenins from the seeds of Annona squamosa

Three new annonaceous acetogenins, annotemoyin L (1), annotemoyin Y (2) and annotemoyin X, (3) were isolated from the seeds of Annona squamosa Linn. Their structures were ascertained by chemical methods and spectral data. The cytotoxic activities of compounds against three multidrug-resistant cancer cell lines were evaluated, and compound 3 exerted strong cytotoxicity against SMMC 7721/ADR (IC50 0.163 μM), A549/T (IC50 0.064 μM) and MCF-7/ADR (IC50 0.057 μM).
Real-Time Monocular Joint Perception Network for Autonomous Driving

Comprehensive and accurate perception of the real 3D world is the basis of autonomous driving. However, many perceptual methods focus on a single task or object type, and the accuracy of existing multi-task or multi-object methods is difficult to balance against their real-time performance. This paper presents a unified framework for concurrent dynamic multi-object joint perception, which introduces a real-time monocular joint perception network termed MJPNet. In MJPNet relative weightings are automatically learned by a series of developed network branches. By training an end-to-end deep convolutional neural network on a shared feature encoder and many proposed decoding sub-branches, the information of the 2D category and 3D position/pose/size of an object are reconstructed both simultaneously and accurately. Moreover, the effective information among subtasks is transferred by multi-stream learning, guaranteeing the accuracy of each task. Compared to various state-of-the-arts, comprehensive evaluations on the benchmark of challenging image sequences demonstrate the superior performance of our 2D detection and 3D reconstruction of depth, lateral distance, orientation, and heading angle. Moreover, on the KITTI test set, the real-time runtime (up to 15 fps) of MJPNet significantly outran the public state-of-the-art visual detection methods. Accompanying video: https://youtu.be/Z-goToOlI94.
HBsAg sT123N mutation induces stronger antibody responses to HBsAg and HBcAg and accelerates in vivo HBsAg clearance

Immune escape mutants with mutations in the hepatitis B surface antigen (HBsAg) major hydrophilic region (MHR) often emerge in association with diagnostic failure or breakthrough of HBV infection in patients with anti-HBs antibodies. Some mutants harboring substitutions to Asn in HBsAg MHR may have an additional potential N-glycosylation site. We have previously showed that sT123N substitution could generate additional N-glycosylated forms of HBsAg. In the present study, 1.3-fold-overlength HBV genomes containing the sT123N substitution were digested from the pHBV1.3-sT123N construct and subcloned into the pAAV vector to generate pAAV1.3-sT123N for hydrodynamic injection (HI) in mice. Viral expression and replication were phenotypically characterized by transient transfection. The results demonstrated that sT123N substitution impaired virion secretion, resulting in intracellular retention of HBcAg. Using the HBV HI mouse model, we found that mice mounted significantly stronger antibody responses to HBsAg and HBcAg, which accelerated HBsAg clearance. Thus, additional N-glycosylation generated by amino acid substitutions in HBsAg MHR may significantly modulate specific host immune responses and influence HBV infection in vivo. Our results help further the understanding of the role of immune escape mutants with N-linked glycosylation in the biology of HBV infection.
BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image segmentation and recognition. Fusing low-resolution HSIs with high-resolution RGB images to reconstruct high-resolution HSIs has attracted great research attention recently. In this paper, we propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model. The experimental results demonstrate the effectiveness of our method both in simulations and in our real experiments. The proposed method outperforms other state-of-the-art nonblind and blind fusion methods on two popular HSI datasets. Our related code and data is available at https://github.com/CPREgroup/Real-Spec-RGB-Fusion.
U-LanD: Uncertainty-Driven Video Landmark Detection

This paper presents U-LanD, a framework for automatic detection of landmarks on key frames of the video by leveraging the uncertainty of landmark prediction. We tackle a specifically challenging problem, where training labels are noisy and highly sparse. U-LanD builds upon a pivotal observation: a deep Bayesian landmark detector solely trained on key video frames, has significantly lower predictive uncertainty on those frames vs. other frames in videos. We use this observation as an unsupervised signal to automatically recognize key frames on which we detect landmarks. As a test-bed for our framework, we use ultrasound imaging videos of the heart, where sparse and noisy clinical labels are only available for a single frame in each video. Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly outperform the state-of-the-art non-Bayesian counterpart by a noticeable absolute margin of 42% in R-2 score, with almost no overhead imposed on the model size.
Performance analysis of 1300 nm SLEDs - impact of temperature and length scaling

The impact of self-heating and cavity length on the spectral emission properties of SLEDs is investigated using a state-of-the-art simulation tool. Simulated data are compared to measurements for two InP-based benchmark devices operating around 1300 nm, and excellent agreement is achieved in either case.