Miracle-dz/UniFAR-FTUs huggingface dataset:
Long‐Term Outcomes of Cardiac Resynchronization Therapy Using Apical Versus Nonapical Left Ventricular Pacing

Background Experimental evidence indicates that left ventricular (LV) apical pacing is hemodynamically superior to nonapical LV pacing. Some studies have shown that an LV apical lead position is unfavorable in cardiac resynchronization therapy. We sought to determine whether an apical LV lead position influences cardiac mortality after cardiac resynchronization therapy. Methods and Results In this retrospective observational study, the primary end point of cardiac mortality was assessed in relation to longitudinal (basal, midventricular, or apical) and circumferential (anterior, lateral, or posterior) LV lead positions, as well as right ventricular (apical or septal), assigned using fluoroscopy. Lead positions were assessed in 1189 patients undergoing cardiac resynchronization therapy implantation over 15 years. After a median follow‐up of 6.0 years (interquartile range: 4.4–7.7 years), an apical LV lead position was associated with lower cardiac mortality than a nonapical position (adjusted hazard ratio: 0.74; 95% confidence interval, 0.56–0.99) after covariate adjustment. There were no differences in total mortality or heart failure hospitalization. Death from pump failure was lower with apical than nonapical positions (adjusted hazard ratio: 0.69; 95% confidence interval, 0.51–0.94). Compared with a basal position, an apical LV position was also associated with lower risk of sudden cardiac death (adjusted hazard ratio: 0.34; 95% confidence interval, 0.13–0.93). No differences emerged between circumferential LV lead positions or right ventricular positions with respect to any end point. Conclusions In recipients of cardiac resynchronization therapy, an apical LV lead position was associated with better long‐term cardiac survival than a nonapical position. This effect was due to a lower risk of pump failure and sudden cardiac death.
Through synapses to spatial memory maps: a topological model

Various neurophysiological and cognitive functions are based on transferring information between spiking neurons via a complex system of synaptic connections. In particular, the capacity of presynaptic inputs to influence the postsynaptic outputs---the efficacy of the synapses---plays a principal role in all aspects of hippocampal neurophysiology. However, a direct link between the information processed at the level of individual synapses and the animal's ability to form memories at the organismal level has not yet been fully understood. Here, we investigate the effect of synaptic transmission probabilities on the ability of the hippocampal place cell ensembles to produce a cognitive map of the environment. Using methods from algebraic topology, we find that weakening synaptic connections increase spatial learning times, produce topological defects in the large-scale representation of the ambient space and restrict the range of parameters for which place cell ensembles are capable of producing a map with correct topological structure. On the other hand, the results indicate a possibility of compensatory phenomena, namely that spatial learning deficiencies may be mitigated through enhancement of neuronal activity.
Signal Quality and Electrode-Skin Impedance Evaluation in the Context of Wearable Electroencephalographic Systems

Recent advancement in technology has brought about increase in the application areas of wearable electroencephalographic devices. In that, new types of electrodes take place, and particular attention is needed to ensure the required quality of obtained signals. In this study, we evaluate electrode-skin impedance and signal quality for several kinds of electrodes when used in conditions typical for wearable devices. Results suggest that active dry electrode coated with gold alloy is superior while it was challenging to obtain appropriate signal quality when using passive dry electrodes. We also demonstrate electrode-skin impedance measurement using the analog frontend ADS1299, which is suitable for implementation in wearable devices.
Simulation as a means to develop firefighters as emergency care professionals

Objective. The aim of this study was to evaluate the simulated emergency care performed by firefighters and their perception of simulation as an educational method. Methods. This study had a mixed method with both a quantitative and a qualitative approach. Data were collected by simulation assessment, a questionnaire and written comments. Descriptive analysis was conducted on the quantitative data whereas a qualitative content analysis was conducted on the qualitative data. Finally, a contingent analysis was used where a synthesis configured both the quantitative and the qualitative results into a narrative result. Results. The cognitive workload that firefighters face during simulated emergency care is crucial for learning. In this study, the severity and complexity of the scenarios provided were higher than expected by the firefighters. Clearly stated conditions for the simulation and constructive feedback were considered positive for learning. Patient actors induced realism in the scenario, increasing the experience of stress, in comparison to a manikin. Conclusion. Simulation in a realistic on-scene environment increases firefighters’ cognitive ability to critically analyze problems and manage emergency care. Simulation of emergency care developed the firefighters as professionals.
Adolescents’ Psychological Well-Being: A Multidimensional Measure

Over the last few years, different theoretical approaches have emerged advocating for a positive understanding of adolescence, recognizing it as a stage characterized by plasticity, the acquisition of competences and the achievement of satisfactory levels of well-being and positive adjustment. Based on Ryff’s multidimensional model of psychological well-being, this study aims: (1) to develop an adjusted measuring instrument for adolescents (Brief Scale of Psychological Well-Being for Adolescents), as well as analysing its psychometric properties; and (2) to conduct a descriptive analysis of the levels of psychological and subjective well-being among adolescent boys and girls. A sample of 1590 Andalusian adolescents (51% girls), aged between 13 and 19 years old participated in this study. The Confirmatory Factor Analysis (CFA) showed the validity of the instrument, with a multidimensional factorial solution of four factors (self-acceptance, positive interpersonal relationships, autonomy and life development) with good levels of internal consistency. Descriptive analyses showed good scores of psychological and subjective well-being among the adolescents, with a significant impact of sex and age in both measures of well-being. The results are discussed in terms of the importance of considering adolescent well-being from a multidimensional view and the need to promote positive development from a multifactorial perspective which takes into account the diversity of the variables involved.
Aptamers and Aptasensors for Highly Specific Recognition and Sensitive Detection of Marine Biotoxins: Recent Advances and Perspectives

Marine biotoxins distribute widely, have high toxicity, and can be easily accumulated in water or seafood, exposing a serious threat to consumer health. Achieving specific and sensitive detection is the most effective way to prevent emergent issues caused by marine biotoxins; however, the previous detection methods cannot meet the requirements because of ethical or technical drawbacks. Aptamers, a kind of novel recognition element with high affinity and specificity, can be used to fabricate various aptasensors (aptamer-based biosensors) for sensitive and rapid detection. In recent years, an increasing number of aptamers and aptasensors have greatly promoted the development of marine biotoxins detection. In this review, we summarized the recent aptamer-related advances for marine biotoxins detection and discussed their perspectives. Firstly, we summarized the sequences, selection methods, affinity, secondary structures, and the ion conditions of all aptamers to provide a database-like information; secondly, we summarized the reported aptasensors for marine biotoxins, including principles, detection sensitivity, linear detection range, etc.; thirdly, on the basis of the existing reports and our own research experience, we forecast the development prospects of aptamers and aptasensors for marine biotoxins detection. We hope this review not only provides a comprehensive summary of aptamer selection and aptasensor development for marine biotoxins, but also arouses a broad readership amongst academic researchers and industrial chemists.
Adapted K-Core Decomposition and Visualization for Functional Magnetic Resonance Imaging Connectivity Networks

Medical imaging modalities, such as functional magnetic resonance imaging (fMRI) are being increasingly used to study the human brain. Analysis of the images has led to findings describing diseases, such as schizophrenia and post-traumatic stress disorder. One of the most widely used methods of analysis involves creating functional connectivity network (FCN) abstractions. These summarize the temporal relationships between regions of interest (ROIs) in the brain and can be used to easily compare subjects, e.g. healthy against schizophrenia. Visual analytics is widely used to facilitate such analysis, with existing approaches designed to enable and simplify detailed interpretation of single networks and pairs of networks in comparison. Prior to such detailed analysis, grouping and aggregation is often performed on the data, which is a time consuming and difficult task. Existing methods for doing this are commonly statistical, while others visualize the cohort without presenting vital network details of the individual FCNs. Thus, there is an opportunity for alternative visual analytics to facilitate the grouping by incorporating the network details. Graph decomposition, such as k-core decomposition, can be used to simplify the representation of networks, while retaining these vital network details. In this study, we propose an adapted k-core decomposition algorithm and visualization, which calculates the connected component information of nodes in the FCNs, a key detail in analysis. Our visualization combines this information with the decomposition to display more details about FCNs at a high-level than contemporary approaches. We present a prototype of our method, demonstrating the ability to group and aggregate the data without the loss of vital network details for further detailed analysis.
A Novel 4D Semi-Automated Algorithm for Volumetric Segmentation in Echocardiography

Segmentation of the left ventricle (LV) in temporal 3D echocardiography sequences poses a challenge. However, it is an essential component in generating quantitative clinical measurements for the diagnosis and treatment of various cardiac diseases. Identifying the endocardial borders of the left ventricle can be difficult due to the inherent properties of ultrasound. This study proposes a 4D segmentation algorithm that segments over temporal 3D volumes that has minimal user interaction and is based on a diffeomorphic registration approach. In contrast to several existing algorithms, the proposed method does not depend on training data or make any geometrical assumptions. The algorithm was evaluated on seven patients obtained from the Mazankowski Alberta Heart Institute, Edmonton, Canada in comparison to expert manual segmentation. The proposed approach yielded Dice scores of 0.94 (0.01), 0.91 (0.03) and 0.92 (0.02) at end diastole, at end systole and over the entire cardiac cycle, respectively. The corresponding Hausdorff distance values were 4.49 (1.01) mm, 4.94 (1.41) mm, and 5.05 (0.85) mm, respectively. These results demonstrate that the proposed 4D segmentation approach for the left ventricle is robust and can potentially be used in clinical practice.
Behavior-Interior-Aware User Preference Analysis Based on Social Networks

There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top-N hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately.
Study of the Automatic Detection of Parkison’s Disease Based on Speaker Recognition Technologies and Allophonic Distillation

The use of new tools to detect Parkinson’s Disease (PD) from speech articulatory movements can have a considerable impact in the diagnosis of patients. In this study, a novel approach involving speaker recognition techniques with allophonic distillation is proposed and tested separately in four parkinsonian speech databases (205 patients and 186 controls in total). This new scheme provides values between 72% and 94% of accuracy in the automatic detection of PD, depending on the database, and improvements up to 9% respect to baseline techniques. Results not only point towards the importance of the segmentation of the speech for the differentiation of parkinsonian and control speakers but confirm previous findings about the relevance of plosives and fricatives in the detection of parkinsonian dysarthria.
Evasion of phagotrophic predation by protist hosts and innate immunity of metazoan hosts by Legionella pneumophila

Legionella pneumophila is a ubiquitous environmental bacterium that has evolved to infect and proliferate within amoebae and other protists. It is thought that accidental inhalation of contaminated water particles by humans is what has enabled this pathogen to proliferate within alveolar macrophages and cause pneumonia. However, the highly evolved macrophages are equipped with more sophisticated innate defence mechanisms than are protists, such as the evolution of phagotrophic feeding into phagocytosis with more evolved innate defence processes. Not surprisingly, the majority of proteins involved in phagosome biogenesis (~80%) have origins in the phagotrophy stage of evolution. There are a plethora of highly evolved cellular and innate metazoan processes, not represented in protist biology, that are modulated by L. pneumophila, including TLR2 signalling, NF‐κB, apoptotic and inflammatory processes, histone modification, caspases, and the NLRC–Naip5 inflammasomes. Importantly, L. pneumophila infects haemocytes of the invertebrate Galleria mellonella, kill G. mellonella larvae, and proliferate in and kill Drosophila adult flies and Caenorhabditis elegans. Although coevolution with protist hosts has provided a substantial blueprint for L. pneumophila to infect macrophages, we discuss the further evolutionary aspects of coevolution of L. pneumophila and its adaptation to modulate various highly evolved innate metazoan processes prior to becoming a human pathogen.
Priming with oxytocin and relaxin improves cardiac differentiation of adipose tissue–derived stem cells

Previous studies have identified the heart as a source and a target tissue for oxytocin and relaxin hormones. These hormones play important roles in the regulation of cardiovascular function and repair of ischemic heart injury. In the current study, we examined the impact of oxytocin and relaxin on the development of cardiomyocytes from mesenchymal stem cells. For this purpose, mouse adipose tissue–derived stem cells (ADSCs) were treated with different concentrations of oxytocin or relaxin for 4 days. Three weeks after initiation of cardiac induction, differentiated ADSCs expressed cardiac‐specific genes, Gata4, Mef2c, Nkx2.5, Tbx5, α‐ and β‐Mhc, Mlc2v, Mlc2a and Anp, and cardiac proteins including connexin 43, desmin and α‐actinin. 10 −7 M oxytocin and 50 ng/mL relaxin induced the maximum upregulation in the expression of cardiac markers. A combination of oxytocin and relaxin induced cardiomyocyte differentiation more potently than the individual factors. In our experiment, oxytocin‐relaxin combination increased the population of cardiac troponin I‐expressing cells to 6.84% as compared with 2.36% for the untreated ADSCs, 3.7% for oxytocin treatment and 3.41% for relaxin treatment groups. In summary, the results of this study indicated that oxytocin and relaxin hormones individually and in combination can improve cardiac differentiation of ADSCs, and treatment of the ADSCs and possibly other mesenchymal stem cells with these hormones may enhance their cardiogenic differentiation and survival after transplantation into the ischemic heart tissue.
Reactive Oxygen Species as Regulators of MDSC-Mediated Immune Suppression

Reactive oxygen species (ROS) molecules are implicated in signal transduction pathways and thereby control a range of biological activities. Immune cells are constantly confronted with ROS molecules under both physiologic and pathogenic conditions. Myeloid-derived suppressor cells (MDSCs) are immunosuppressive, immature myeloid cells and serve as major regulators of pathogenic and inflammatory immune responses. In addition to their own release of ROS, MDSCs often arise in oxidative-stress prone environments such as in tumors or during inflammation and infection. This evidently close relationship between MDSCs and ROS prompted us to summarize what is currently known about ROS signaling within MDSCs and to elucidate how MDSCs use ROS to modulate other immune cells. ROS not only activate anti-oxidative pathways but also induce transcriptional programs that regulate the fate and function of MDSCs. Furthermore, MDSCs release ROS molecules as part of a major mechanism to suppress T cell responses. Targeting redox-regulation of MDSCs thus presents a promising approach to cancer therapy and the role of redox-signaling in MDSCs in other disease states such as infection, inflammation and autoimmunity would appear to be well worth investigating.
Pervasive 3D Reconstruction to Identify Hidden 3D Survival Spaces for Search and Rescue Management

Efficient management of urban search and rescue is a very important field for our society. This is mainly due to the fact that efficient management directly affects peoples’ lives. Currently, ICT tools have been investigated for an efficient operation of urban search and rescue, such as robotic technologies or pervasive sensors able to capture the current situation awareness of a collapsing event. It is clear that 3D information is an essential element in efficient managing of urban search and rescue, since it models the geometry of the collapsing scene. Additionally, 3D models are combined with structural simulators in order to identify the best parameters causing a building collapse. In this way, hidden or internal survival spaces are able to be identified by matching the captured 3D models (they represent the current, actual conditions of a collapse) with those generated by the structural simulators (they predict of how a building should be collapsed based on structural parameters). In this way, 3D survival spaces are involved, which is salient information for managing urban search and rescue operations. This paper presents the results of the European Funded project, named INACHUS, “Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue”. It mainly describes the 3D modelling framework used to capture the geometry of the collapsing scene and to identify the 3D hidden survival spaces by comparing the actual captured with the structural simulated 3D meshes. It also describes a visualization engine toolkit used= for depicting the collapsing scene in 3D domain, improving the decision making of the civil protection agencies.
A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease

The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead.
Soft Error Characterization on Scientific Applications

Decreasing transistor sizes, aggressive power optimization techniques and higher operation frequencies lead to increase error rates. While researchers addressed reliable computing, there is still lack of study for providing the fundamental understanding of error propagation. In this work, we characterize error propagation at software level by utilizing error propagation speed metric. It is validated on a set of commonly used iterative solvers, where the speed of error propagation is modeled for the different input–output pairs. Additionally, we study two different methods for slowing down the error propagation. Firstly, the effect of various algorithmic choices of sorting on error propagation profiles is examined whether such choices have an impact on error propagation profiles. Experimental results show that sorting algorithms differ in error propagation patterns. Secondly, different loop transformation techniques are considered for slowing down the error propagation speed. Specifically, while loop tiling causes a significant change in error propagation, the impact of loop unrolling is negligible for the given applications.