ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Longitudinal Invariance of the Strengths and Difficulties Questionnaire Across Ages 4 to 16 in the ALSPAC Sample

The Strengths and Difficulties Questionnaire (SDQ) has been widely used to study children's psychosocial development longitudinally; however, such analyses assume longitudinal measurement invariance, that is, they presuppose that symptom manifestations are measured comparably across different ages. Violations of this assumption could bias longitudinal analyses and should therefore be empirically tested. This study tested longitudinal measurement invariance within a confirmatory factor analysis framework in the U.K.-based Avon Longitudinal Study of Parents and Children (N = 13,988). Results indicated that SDQ scores showed configural, metric, scalar, and residual invariance across ages 7, 8, 9, 11, 13, and 16, supporting its use for comparing variances, covariances, and means over time within a latent variable model as well as using observed scores. At age 4, configural invariance was not supported, indicating that mental health symptoms as measured by the SDQ manifest differently at this age, thus necessitating caution when comparing symptoms as measured by SDQ scores at this age to later ages.
Gap in the conditioned stimulus: Differential impacts on temporal expectancy in appetitive and aversive conditions in rats

We analyzed, through a Pavlovian conditioning procedure in rats, the temporal pattern of behavior in appetitive and aversive conditions within subjects, and the difference in inferred temporal working memory functioning with the Gap paradigm. For both conditions, we paired a 60-s conditioned stimulus (CS: tone1 or tone2) with an unconditioned stimulus (US: shock or chocolate pellet) delivered 20s after CS onset. The analyses of mean response rate and individual-trial data were performed during Probe trials, consisting of CS alone, and trials in which gaps of different position or duration were inserted, to assess the effect of the temporal manipulation on behavior. The results showed: (1) An anticipatory peak time in the aversive condition but better accuracy in the appetitive condition, (2) constancy in the Weber fraction suggesting that the difference in peak time was under clock control, (3) a graded effect of gap parameters only in the aversive condition and (4) different gap effects between conditions when a gap was inserted early in the CS. These results highlight behavioral differences between aversive and appetitive conditions and suggest that the temporal working memory mechanism was not engaged in the same manner in each condition.
Nickel-Catalyzed Site-Selective Intermolecular C(sp3 )-H Amidation

A nickel-catalyzed site-selective intermolecular amidation of saturated C(sp3 )-H bonds is reported. This mild protocol exhibits a predictable reactivity pattern to incorporate amide functions at C(sp3 )-H sites adjacent to nitrogen and oxygen atoms in either cyclic or acyclic frameworks, thus offering a complementary reactivity profile to existing oxidative-type processes or metal-catalyzed C(sp3 )-N bond-forming reactions operating via two-electron manifolds.
An In-Bore Receiver for Magnetic Resonance Imaging

In magnetic resonance imaging, the use of array detection and the number of detector elements have seen a steady increase over the past two decades. As a result, per-channel analog connection via long coaxial cable, as commonly used, poses an increasing challenge in terms of handling, safety, and coupling among cables. This situation is exacerbated when complementary recording of radiofrequency transmission or NMR-based magnetic field sensing further add to channel counts. A generic way of addressing this trend is the transition to digital signal transmission, enabled by digitization and first-level digital processing close to detector coils and sensors in the magnet bore. The foremost challenge that comes with this approach is to achieve high dynamic range, linearity, and phase stability despite interference by strong static, audiofrequency, and radiofrequency fields. The present work reports implementation of a 16-channel in-bore receiver, performing signal digitization and processing with subsequent optical transmission over fiber. Along with descriptions of the system design and construction, performance evaluation is reported. The resulting device is fully MRI compatible providing practically equal performance and signal quality compared to state-of-the-art RF digitizers operating outside the magnet. Its use is demonstrated by examples of head imaging and magnetic field recording.
A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.
Cement Augmentation of Two-Level Lumbar Corpectomy Cage After Malposition: A Novel Salvage Procedure Technical Note

Intervertebral cage mispositioning is an uncommon complication of a posterior lumbar corpectomy. Most frequently, cages are placed obliquely, laterally, or protruding. However, there are few reports of implanted cages that fail to contact the adjacent vertebral endplate and thus no descriptions of successful revisions. The objective of this case report is to report a unique case of minimally invasive rescue vertebroplasty with cement augmentation following a lumbar corpectomy that resulted in graft-endplate noncontact in a medically complicated patient A 60-year-old male with a history of active intravenous (IV) drug use, untreated hepatitis C virus (HCV) infection, and chronic malnourishment presented with low back pain. He had a history of vertebral osteomyelitis managed with intravenous antibiotics, although he was noncompliant with infusions. The diagnosis of L2-L3 discitis-osteomyelitis with intradiscal abscess causing cord compression was made using inpatient lumbar imaging. The initial intervention was accomplished with L2 and L3 vertebral corpectomy with decompression and expandable cage placement as well as a T10-pelvis posterior fixation. Despite the resolution of presenting symptoms, routine postoperative radiographs identified noncontact between the inferior surface of the cage and the superior endplate of the L4 vertebral body. Salvage therapy was pursued via fluoroscopy-guided vertebroplasty with cement augmentation to correct cage malposition. Secondary surgical intervention was successful in bringing the intervertebral cage into contact with the adjacent vertebral body. Lower extremity strength improved, and back pain was resolved. The postoperative motor examination remained unchanged after the rescue procedure. Accurate intraoperative cage placement can be difficult in patients with poor bone quality, especially in the setting of ongoing infection and cachexia. For this reason, routine postoperative imaging is crucial to assessing graft complications. In patients who are poor candidates for revision surgery, we demonstrate that an interventional radiology-based approach may be successful in correcting cage mispositioning and preventing further changes during healing and fusion.
Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota's high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark.
Attacks on state-of-the-art face recognition using attentional adversarial attack generative network

With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (A(3)GN), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person.
Structure Tensor Riemannian Statistical Models for CBIR and Classification of Remote Sensing Images

This paper deals with parametric techniques for the description of texture on very high resolution (VHR) remote sensing images. These techniques focus on the property of anisotropy as described by the local structure tensor (LST). The novelty of this paper consists in proposing several comprehensive statistical frameworks to handle LST fields for rotation-invariant texture discrimination tasks. These frameworks are all based on probability models defined on the Riemannian manifold of positive definite matrices: a recent Riemannian Gaussian model on the affine-invariant metric space and a multivariate Gaussian distribution on the Log-Euclidean space. A thorough comparison of the proposed methods is performed with respect to some state-of-the-art texture analysis methods. Three experimental protocols are considered based on VHR remote sensing data. The first one consists of a content-based image retrieval (CBIR) protocol for browsing oyster field patches. The second one concerns a supervised classification protocol for grouping maritime pine forest stands in different age classes. The third one is, again, a CBIR protocol performed on the UC Merced land use/land cover patch collection. Tensor-based approaches show similar or even better results than the state-of-the-art texture analysis methods considered for comparison in all the experimental contexts.
Antiviral effects of natural small molecules on aquatic rhabdovirus by interfering with early viral replication

Spring viremia of carp virus (SVCV) is globally widespread and poses a serious threat to aquatic ecology and aquaculture due to its broad host range. To develop effective agents to control SVCV infection, we selected 16 naturally active small molecules to assess their anti-SVCV activity. Notably, dihydroartemisinin (DHA) (100 µmol/L) and (S, S)-(+)-tetrandrine (TET) (16 µmol/L) exhibited high antiviral effects in epithelioma papulosum cyprinid (EPC) cells, with inhibitory rates of 70.11% and 73.54%, respectively. The possible antiviral mechanisms were determined as follows: 1. Pre-incubation with DHA and TET decreased viral particle infectivity in fish cells, suggesting that horizontal transmission of SVCV in the aquatic environment was disrupted; 2. Although neither had an effect on viral adhesion, TET (but not DHA) interfered with SVCV entry into host cells (>80%), suggesting that TET may have an antiviral function in early viral replication. For in vivo study, both agents enhanced the survival rate of SVCV-infected zebrafish by 53.3%, significantly decreased viral load, and modulated the expression of antiviral-related genes, indicating that DHA and TET may stimulate the host innate immune response to prevent viral infection. Overall, our findings indicated that DHA and TET had positive effects on suppressing SVCV infection by affecting early-stage viral replication, thus holding great potential as immunostimulants to reduce the risk of aquatic rhabdovirus disease outbreaks.
Revisiting Linear Discriminant Techniques in Gender Recognition

Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource-limited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice.
Applying a Foucauldian lens to the Canadian code of ethics for registered nurses as a discursive mechanism for nurses professional identity

This study examines the Canadian Code of Ethics for Registered Nurses as a discursive mechanism for shaping nurses' professional identity using a Foucauldian lens. Nurses are considered essential in healthcare, yet the nursing profession has struggled to be recognized for its discipline-specific knowledge and expertise and, as such, has remained the subject of and subject to the dominant discourses within healthcare and society generally. Developing a professional identity in nursing begins after the necessary education and training are achieved and embodies the profession's history, values, code of ethics, and expectations of the profession that distinguish it from other professions. Since nurses' professional identity is shaped through discourse, it raises the question of whether there are spaces to reconceptualize nurses' subject position within health care. Since professional identity is considered the embodiment of knowledge and practice, the code of ethics bears examination both for its effect on nurses' professional identity and as a potential site from which to challenge hegemonic assumptions. This article discusses the concept of professional identity in nursing and its development through the discursive formations in the code of ethics. The sources of power/knowledge are examined as both mechanisms of control and as spaces for change.
Video-Based Intervention for Improving Maternal Retention and Adherence to HIV Treatment: Patient Perspectives and Experiences

VITAL Start is a video-based intervention aimed to improve maternal retention in HIV care and adherence to antiretroviral therapy (ART) in Malawi. We explored the experiences of pregnant women living with HIV (PWLHIV) not yet on ART who received VITAL Start before ART initiation to assess the intervention's acceptability, feasibility, fidelity of delivery, and perceived impact. Between February and September 2019, we conducted semi-structured interviews with a convenience sample of 34 PWLHIV within one month of receiving VITAL Start. The participants reported that VITAL Start was acceptable and feasible and had good fidelity of delivery. They also reported that the video had a positive impact on their lives, encouraging them to disclose their HIV status to their sexual partners who, in turn, supported them to adhere to ART. The participants suggested using a similar intervention to provide health-related education/counseling to people with long term conditions. Our findings suggest that video-based interventions may be an acceptable, feasible approach to optimizing ART retention and adherence amongst PWLHIV, and they can be delivered with high fidelity. Further exploration of the utility of low cost, scalable, video-based interventions to address health counseling gaps in sub-Saharan Africa is warranted.
The Ideal Anticoagulation Strategy in ST-Elevation Myocardial Infarction

Heparin has been the principal anticoagulant in the management of ST-elevation myocardial infarction (STEMI) but has several limitations. Although glycoprotein IIb/IIIa inhibitors have been major adjuncts in previous years, in the era of novel P2Y12 receptor inhibitors they may have a greater role in bailout. Low molecular weight heparins have been extensively studied in fibrinolysis trials but data in primary percutaneous coronary intervention (PCI) are scarce. The direct thrombin inhibitor bivalirudin overcomes several shortcomings of heparins and has demonstrated a significant reduction in bleeding outcomes and net adverse cardiac events at the cost of increased acute stent thrombosis. This review discusses the pharmacology and clinical trial evidence for different anticoagulant treatment options in STEMI with a proposed selection strategy in contemporary primary PCI.
Correspondence on "Synergy and Antagonism between Allosteric and Active-Site Inhibitors of Abl Tyrosine Kinase"

Soellner published on the interplay between allosteric and adenosine triphosphate (ATP)-competitive inhibitors of ABL kinase, showing that the latter preferably binds to different conformational states of ABL compared to allosteric agents that specifically target the ABL myristate pocket (STAMP) and deducing that asciminib cannot bind to ABL simultaneously with ATP-competitive drugs. These results are to some extent in line with ours, although our analyses of dose-response matrices from combinations of asciminib with imatinib, nilotinib or dasatinib, show neither synergy nor antagonism, but suggest additive antiproliferative effects on BCR-ABL-dependent KCL22 cells. Furthermore, our X-ray crystallographic, solution nuclear magnetic resonance (NMR), and isothermal titration calorimetry studies show that asciminib can bind ABL concomitantly with type-1 or -2 ATP-competitive inhibitors to form ternary complexes. Concomitant binding of asciminib with imatinib, nilotinib, or dasatinib might translate to benefit some chronic myeloid leukaemia patients.
Weighted sparse representation for human ear recognition based on local descriptor

A two-stage ear recognition framework is presented where two local descriptors and a sparse representation algorithm are combined. In a first stage, the algorithm proceeds by deducing a subset of the closest training neighbors to the test ear sample. The selection is based on the K-nearest neighbors classifier in the pattern of oriented edge magnitude feature space. In a second phase, the co-occurrence of adjacent local binary pattern features are extracted from the preselected subset and combined to form a dictionary. Afterward, sparse representation classifier is employed on the developed dictionary in order to infer the closest element to the test sample. Thus, by splitting up the ear image into a number of segments and applying the described recognition routine on each of them, the algorithm finalizes by attributing a final class label based on majority voting over the individual labels pointed out by each segment. Experimental results demonstrate the effectiveness as well as the robustness of the proposed scheme over leading state-of-the-art methods. Especially when the ear image is occluded, the proposed algorithm exhibits a great robustness and reaches the recognition performances outlined in the state of the art. (C) 2016 SPIE and IS&T
Edge-Embedded Multi-Dropout Framework for Real-Time Face Alignment

We propose the Edge-Embedded Multi-Dropout (EEMD) framework for real-time face alignment. The EEMD framework extracts facial edge features and explores multiple dropout architecture for locating facial landmarks. It consists of two major component networks, namely the Contour Detection Network (CDN) and the Multi-Dropout Network (MDN); and two supplementary networks, one for face detection and the other for pose regression. When a face is detected by the face detector, its pose will be classified by the pose classifier, then the associated facial edges be detected by the CDN, and then the landmarks be located by the MDN. The embedding of the CDN into the EEMD framework describes the observation that most landmarks are located on the contours/edges of the facial components and of the whole face. We revise a state-of-the-art edge detector as part of the base network for the CDN. The MDN is proposed to better design the regression architecture with appropriate dropout settings for better preventing overfitting and enhancing regression accuracy. Unlike most of the 2D approaches unable to locate landmarks in extreme poses, the proposed framework can detect landmarks on profile faces, i.e., & x00B1;90 & x00B0; in yaw, <italic>in real time</italic>. Evaluated on benchmark databases, the EEMD demonstrates a competitive performance to other state-of-the-art approaches with a satisfying runtime speed.
Selective clustering for representative paintings selection

Selective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods.