Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of Dengvaxia®: development to deployment

ABSTRACT Dengue is the world’s most prevalent and important arboviral disease. More than 50% of the world’s population lives at daily risk of infection and it is estimated more than 95 million people a year seek medical care following infection. Severe disease can manifest as plasma leakage and potential for clinically significant hemorrhage, shock, and death. Treatment is supportive and there is currently no licensed anti-dengue virus prophylactic or therapeutic compound. A single dengue vaccine, Sanofi Pasteur’s Dengvaxia®, has been licensed in 20 countries but uptake has been poor. A safety signal in dengue seronegative vaccine recipients stimulated an international re-look at the vaccine performance profile, new World Health Organization recommendations for use, and controversy in the Philippines involving the government, regulatory agencies, Sanofi Pasteur, clinicians responsible for testing and administering the vaccine, and the parents of vaccinated children. In this review, we provide an overview of Dengvaxia’s® development and discuss what has been learned about product performance since its licensure.
A Systematic Review and Meta-Analysis of the Impact of Mindfulness Based Interventions on Heart Rate Variability and Inflammatory Markers

Mindfulness Based Interventions (MBIs) have recently been increasingly used in clinical settings, and research regarding their effects on health has grown rapidly. However, with regard to the physiological effects of mindfulness practices, studies have reported associations that vary in strength and direction. Therefore, in this systematic review and meta-analysis, we aimed to systematically identify, appraise, and summarize the existing data from randomized and non-randomized controlled trials that examine physiological effects of the standardized MBIs by focusing on pro-inflammatory cytokines and C-reactive protein, and commonly used heart rate variability parameters. The following electronic databases were searched: MEDLINE (via Ovid), PsychINFO (via Ovid), PubMed, Web of Science, EMBASE, CINAHL, ProQuest (Dissertations and Theses), and Centre for Reviews and Dissemination. The systematic review identified 10 studies to be included in the meta-analysis, comprising in total 607 participants. The meta-analysis ended up with mixed and inconclusive results. This was assumedly due to the small number of the original studies and, in particular, to the lack of large, rigorously conducted RCTs. Therefore, the current meta-analysis highlights the necessity of larger, more rigorously conducted RCTs on physiological outcomes with standardized MBIs being compared to various forms of active controls, and with more long-term follow-ups.
Rational design of nanogels for overcoming the biological barriers in various administration routes.

Nanogels have been identified as outstanding nanocarrier candidates for drug delivery due to the desirable physiochemical properties and versatile applicability for diverse therapeutic molecules and imaging probes. One of the main challenges that hinder the clinical translation of nanogels is the low efficiency of drug transmitting to the target sites because of the impedance of complex biological barriers during the in vivo journey. The purpose of this review is to summarize the recent advances on the rational design and structure modulation of nanogels to overcome the barriers and challenges on the way to the site of action following various dosing modes. In particular, the functional moieties or domains were incorporated in the nanogels, allowing them to spontaneously regulate their structure and physiochemical properties to cross one or some of the multifaceted barriers. Importantly, the perspectives are presented in case of opportunities and challenges for the precise and efficacious therapeutic of nanogel formulations.
Becoming a Character: Dissociation in Conservatory Acting Students

ABSTRACT Actors must realistically portray imagined characters in imaginary circumstances by “becoming” their characters. What is it that allows them to do this? We suggest that acting is related to dissociation, a trait typically related to psychopathology. We measured dissociation in 53 conservatory acting students before and after six months of training, using the Dissociative Experiences Scale-II (DES-II). Although no significant changes occurred over time, acting students scored significantly higher than the general population on the DES-II. These scores were driven by just one of the subscales – absorption and imaginative involvement. We argue here that this subscale measures a trait central to the creative process. Furthermore, although the DES-II was designed to measure psychopathology, we found that it is also related to flow (a positive experience). Certain flow dimensions were predictive of dissociation and of its absorption and imaginative involvement subscale. We conclude that total DES-II scores may not necessarily indicate psychopathology in actors. At the very least, these results suggest that clinicians should take extra precaution when diagnosing dissociation in actors using the DES-II.
Relatives’ attachment anxiety mediates the association between perceived loss and expressed emotion in early psychosis

A common reaction experienced by family members of patients with psychosis is grief for the loss of their healthy relative. Importantly, high levels of perceived loss have been related to the manifestation of high expressed emotion (EE), which includes the negative attitudes expressed by relatives toward an ill family member. However, the mechanisms underlying the relationship between relatives’ perceived loss and EE attitudes in the early stages of psychosis are still not fully understood. In this regard, attachment theory has been suggested as a useful framework for understanding this link. The current study aimed to examine: (1) whether relatives’ perceived loss was associated with relatives’ EE dimensions (i.e., criticism and emotional over-involvement (EOI)), and (2) whether such associations were mediated by relatives’ attachment dimensions (i.e., anxiety and avoidance). Seventy-eight relatives of patients with early psychosis completed the Mental Illness Version of the Texas Inventory of Grief for the assessment of loss reactions. Attachment dimensions and EE attitudes were assessed by the Psychosis Attachment Measure and the Family Questionnaire, respectively. Findings indicated that relatives’ perceived loss was associated with EE dimensions. Relatives’ attachment anxiety, but not avoidance, mediated the relationship of perceived loss with both criticism and EOI. Findings highlight the importance of examining the role of relatives’ attachment characteristics for understanding how perceptions of loss might impact the manifestation of EE attitudes in the early stages of psychosis. Family interventions aimed at assisting relatives to improve their management of negative emotional reactions to loss are fundamental to prevent impairing loss reactions and the entrenchment of high-EE attitudes.
Spectral Reflectance Based Heart Rate Measurement from Facial Video

Remote detection of the cardiac pulse has a number of applications in sports and medicine, and can be used to determine an individual’s physiological state. Previous approaches to estimate Heart Rate (HR) from video require the subject to remain stationary and employ background information to eliminate illumination interferences. The present research proposes a spectral reflectance-based novel illumination rectification method to eliminate illumination variations in the video. Our method does not rely on the background of the video and is robust to extreme motion interferences (head movements). Furthermore, in order to tackle extreme motion artifacts, the present framework introduces a novel feature point recovery system which recovers the feature tracking points lost during extreme head movements of the subject. Finally, the individual HR estimates from multiple feature points are combined to produce an average HR. We evaluate the efficacy of our framework on the MAHNOB-HCI dataset, a publicly available dataset employed by previous methods. Our HR measurement framework outperformed previous methods and had a root mean square error (RMSE) of 5.21%.
Fake news detection on social media via implicit crowd signals

The proliferation of Fake News on social media has been a source of widespread concern. One of the main approaches to automatically detect this type of news is based on crowd signals, i.e., opinions manifested by social media users concerning whether the news are fake or not. Although promising, this approach depends on information that is not always available: the explicit opinion of the users about the news to be checked. To overcome this drawback, this article proposes a crowd signal-based method that does not demand the users' explicit opinion to detect Fake News. The proposed method infers the users' opinions from their news spreading (publication/propagation) behavior. Preliminary experiments with two real-world datasets provided evidence that the proposed method can detect Fake News without demanding the explicit opinion of the users about the news and without compromising the classification results obtained by the state-of-the-art crowd signal-based method.
End-to-End Timing Analysis of Sporadic Cause-Effect Chains in Distributed Systems

A cause-effect chain is used to define the logical order of data dependent tasks, which is independent from the execution order of the jobs of the (periodic/sporadic) tasks. Analyzing the worst-case End-to-End timing behavior, associated to a cause-effect chain, is an important problem in embedded control systems. For example, the detailed timing properties of modern automotive systems are specified in the AUTOSAR Timing Extensions. In this paper, we present a formal End-to-End timing analysis for distributed systems. We consider the two most important End-to-End timing semantics, i.e., the button-to-action delay (termed as the maximum reaction time) and the worst-case data freshness (termed as the maximum data age). Our contribution is significant due to the consideration of the sporadic behavior of job activations, whilst the results in the literature have been mostly limited to periodic activations. The proof strategy shows the (previously unexplored) connection between the reaction time (data age, respectively) and immediate forward (backward, respectively) job chains. Our analytical results dominate the state of the art for sporadic task activations in distributed systems and the evaluations show a clear improvement for synthesized task systems as well as for a real world automotive benchmark setting.
Effects of high-quality nursing care on psychological outcomes in patients with chronic heart failure

Abstract Background: This study will assess the effects of high quality nursing care (HQNC) on psychological outcomes (PCO) in patients with chronic heart failure (CHF). Methods: We will carry out a through search in 7 databases: PUBMED, EMBASE, Cochrane Library, Web of Science, Chinese Biomedical Literature Database, WANGFANG, and China National Knowledge Infrastructure. Eligibility criteria will be randomized controlled trials on assessing effects of HQNC on PCO in patients with CHF. Cochrane risk of bias evaluation will be utilized for methodological quality. Results: This proposed study will summarize a rational synthesis of current evidence for HQNC on PCO in patients with CHF. Conclusion: The results of this study will provide convinced evidence for judging the effects of HQNC on PCO in patients with CHF.
Ensemble-Based Modeling of the NMR Spectra of Solid Solutions: Cation Disorder in Y2(Sn,Ti)2O7

The sensitivity of NMR to the local environment, without the need for any long-range order, makes it an ideal tool for the characterization of disordered materials. Computational prediction of NMR parameters can be of considerable help in the interpretation and assignment of NMR spectra of solids, but the statistical representation of all possible chemical environments for a solid solution is challenging. Here, we illustrate the use of a symmetry-adapted configurational ensemble in the simulation of NMR spectra, in combination with solid-state NMR experiments. We show that for interpretation of the complex and overlapped lineshapes that are typically observed, it is important to go beyond a single-configuration representation or a simple enumeration of local environments. The ensemble method leads to excellent agreement between simulated and experimental spectra for Y2(Sn,Ti)2O7 pyrochlore ceramics, where the overlap of signals from different local environments prevents a simple decomposition of the experimental spectral lineshapes. The inclusion of a Boltzmann weighting confirms that the best agreement with experiment is obtained at higher temperatures, in the limit of full disorder. We also show that to improve agreement with experiment, in particular at low dopant concentrations, larger supercells are needed, which might require alternative simulation approaches as the complexity of the system increases. It is clear that ensemble-based modeling approaches in conjunction with NMR spectroscopy offer great potential for understanding configurational disorder, ultimately aiding the future design of functional materials.
Specification Mining and Robust Design under Uncertainty

In this paper, we propose Stochastic Temporal Logic (StTL) as a formalism for expressing probabilistic specifications on time-varying behaviors of controlled stochastic dynamical systems. To make StTL a more effective specification formalism, we introduce the quantitative semantics for StTL to reason about the robust satisfaction of an StTL specification by a given system. Additionally, we propose using the robustness value as the objective function to be maximized by a stochastic optimization algorithm for the purpose of controller design. Finally, we formulate an algorithm for parameter inference for Parameteric-StTL specifications, which allows specifications to be mined from output traces of the underlying system. We demonstrate and validate our framework on two case studies inspired by the automotive domain.
A Virtual Pressure and Force Sensor for Safety Evaluation in Collaboration Robot Application †

Recent developments in robotics have resulted in implementations that have drastically increased collaborative interactions between robots and humans. As robots have the potential to collide intentionally and/or unexpectedly with a human during the collaboration, effective measures to ensure human safety must be devised. In order to estimate the collision safety of a robot, this study proposes a virtual sensor based on an analytical contact model that accurately estimates the peak collision force and pressure as the robot moves along a pre-defined path, even before the occurrence of a collision event, with a short computation time. The estimated physical interaction values that would be caused by the (hypothetical) collision were compared to the collision safety thresholds provided within ISO/TS 15066 to evaluate the safety of the operation. In this virtual collision sensor model, the nonlinear physical characteristics and the effect of the contact surface shape were included to assure the reliability of the prediction. To verify the effectiveness of the virtual sensor model, the force and pressure estimated by the model were compared with various experimental results and the numerical results obtained from a finite element simulation.
Critical assessment of methods of protein structure prediction (CASP)—Round XIII

CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically “ab initio” modeling). Progress was driven by the successful application of deep learning techniques to predict inter‐residue distances. In turn, these results drove dramatic improvements in three‐dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long‐standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template‐based models. Other areas—model refinement, accuracy estimation, and the structure of protein assemblies—have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants.
Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation

As artificial intelligence (AI)- or deep-learning-based technologies become more popular, the main research interest in the field is not only on their accuracy, but also their efficiency, e.g., the ability to give immediate results on the users’ inputs. To achieve this, there have been many attempts to embed deep learning technology on intelligent sensors. However, there are still many obstacles in embedding a deep network in sensors with limited resources. Most importantly, there is an apparent trade-off between the complexity of a network and its processing time, and finding a structure with a better trade-off curve is vital for successful applications in intelligent sensors. In this paper, we propose two strategies for designing a compact deep network that maintains the required level of performance even after minimizing the computations. The first strategy is to automatically determine the number of parameters of a network by utilizing group sparsity and knowledge distillation (KD) in the training process. By doing so, KD can compensate for the possible losses in accuracy caused by enforcing sparsity. Nevertheless, a problem in applying the first strategy is the unclarity in determining the balance between the accuracy improvement due to KD and the parameter reduction by sparse regularization. To handle this balancing problem, we propose a second strategy: a feedback control mechanism based on the proportional control theory. The feedback control logic determines the amount of emphasis to be put on network sparsity during training and is controlled based on the comparative accuracy losses of the teacher and student models in the training. A surprising fact here is that this control scheme not only determines an appropriate trade-off point, but also improves the trade-off curve itself. The results of experiments on CIFAR-10, CIFAR-100, and ImageNet32 × 32 datasets show that the proposed method is effective in building a compact network while preventing performance degradation due to sparsity regularization much better than other baselines.
Complete cleavage of the N≡N triple bond by Ta2N+ via degenerate ligand exchange at ambient temperature: A perfect catalytic cycle

Significance Catalysts accelerate chemical reactions by opening energetically favorable pathways. Although catalytic processes are ubiquitous, the actual catalytically active species often remain elusive due to their very nature. The interaction between the catalyst and the substrate is usually fast, and the rather short-lived intermediates are difficult to identify. As a catalytically active species, Ta2N+ was discovered. This tiny “molecular machine” cleaves the very strong N2 triple bond and reassembles it at ambient temperature. As the N2 unit, which is liberated during the process, contains the nitrogen atom of Ta2N+, this reaction serves as a rare gas-phase example for the operation of the Mars/van Krevelen mechanism, which itself belongs to the basic concepts of heterogenous catalysis. An unprecedented, spontaneous, and complete cleavage of the triple bond of N2 in the thermal reaction of 15N2 with Ta214N+ was observed experimentally by Fourier transform ion cyclotron resonance mass spectrometry; mechanistic aspects of the degenerate ligand exchange were addressed by high-level quantum chemical calculations. The “hidden” dis- and reassembly of N2, mediated by Ta2N+, constitutes a full catalytic cycle. A frontier orbital analysis reveals that the scission of the N2 triple bond is essentially governed by the donation of d-electrons from the 2 metal centers into antibonding π*-orbitals of N2 and by the concurrent migration of electrons from bonding π- and σ-orbitals of N2 into empty d-orbitals of the metals. This work may contribute to a rational design of catalysts in order to reduce the still enormous energy demand required for an artificial dinitrogen activation.
Speech understanding and listening effort in cochlear implant users – microphone beamformers lead to significant improvements in noisy environments

Objectives: To evaluate the effect of microphone directionality, i.e. beamforming, on speech understanding in noise with the SONNET audio processor. Methods: Speech reception thresholds (SRTs) were tested in three different microphone settings (omnidirectional, adaptive, and fixed beamformer (natural)) and assessed via the Oldenburg Sentence Test and the Just Understanding Speech Test. Subjects rated the listening effort needed to understand speech in different signal-to-noise ratios (−10, −5, 0, 5, 10, 15 dB SNR) via a Visual Analogue Scale. For all test methods, speech was presented at 0° azimuth while fixed and uncorrelated masking noise was presented simultaneously from five loudspeakers positioned at ±70°, ±135°, and 180° azimuth. Results: Compared to the omnidirectional mode, significant improvements (p<0.001) were shown in mean SRTs for both the natural (3.3 dB SNR) and adaptive (5.2 dB SNR) settings. Using the natural or the adaptive setting required significantly less listening effort than using the omnidirectional setting for the SNR conditions −5 dB SNR (p=0.002) and 0 dB SNR (p<0.001). Discussion: The beamformer settings significantly improved speech understanding in noise over the omnidirectional setting. Due our multi-speaker test setup, we conclude that beamforming should yield significantly better and less stressful speech understanding in demanding real-life listening situations.
Using topic modeling to find main discussion topics in brazilian political websites

Knowing the main discussion topics debated by the general public is a valuable asset to politicians and professionals involved with politics. Lately, alternative media websites became popular venues in which political ideas are debated without the influence of mainstream media. In this article, we propose the construction of a topic modeling framework, using LSI, LDA, and HDP, to identify main discussion issues in political websites. Experiments show that these models presented results similar to state of the art, offering a viable solution to track political discourse in left-wing and right-wing websites.