Miracle-dz/UniFAR-FTUs huggingface dataset:
Satisfaction of physicians working in hospitals within the European Union: state of the evidence based on systematic review

BACKGROUND Despite the wide range of studies concerning physician satisfaction in different European countries, there is a lack of literature reviews synthesizing and analyzing current evidence evaluating satisfaction of physicians working in European hospitals. The goal of our research was to provide a general overview of the studies in this area and their results. METHODS We searched MEDLINE, Embase, PsycINFO, CINAHL and the Cochrane Library from January 2000 to January 2017 including both MESH/Emtree terms and free text words related to the subject with no language restrictions. The eligibility criteria included: (i) target population: physicians working in European hospitals, (ii) quantitative research aimed at assessing physician satisfaction and (iii) validated tools. We performed a narrative synthesis and meta-analysis. RESULTS A total of 8585 abstracts and 368 full text articles were independently screened by 2 reviewers against inclusion/exclusion criteria. Finally 61 studies were eligible for qualitative analysis. Included studies enrolled a total of 50 001 physicians from 17 countries. Sample sizes varied between 54 and 7090 participants (median: 336). According to our review ∼59% of physicians working in European hospitals are overall satisfied, 3.54 was the mean satisfaction among studies reporting data on a scale from 1 to 5, 4.81 for studies with a scale from 1 to 7, 6.12 among studies reporting data on a scale from 1 to 10, and 59.65 among studies with a scale from 0 to 100. CONCLUSIONS The level of physician satisfaction in Europe is moderate. There is a large variety of tools and scales used to assess it.
Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks

The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.
Networked Estimation of a Class of Fuzzy Stochastic Hybrid Systems With Variable Rates of Packet Losses

This paper focuses on the state estimator design problem for a class of Takagi–Sugeno fuzzy stochastic hybrid systems with intermittent measurements in discrete-time domain. The hybrid systems are characterized with the stochastic switching among a set of subsystems, and the switching is supposed to be governed by a semi-Markov process with finite sojourn time. The random packet dropouts are modeled by a Bernoulli distributed sequence, and the packet dropout rate, which is considered to be variable, is described by the semi-Markov stochastic process that governs the switching dynamics of the fuzzy stochastic hybrid system. A more general class of Lyapunov functions that not only depends on the system modes, but also on the time that the current mode has been in since the last mode switching, is employed to analyze the stability and ${H_{\infty }}$ performance of the estimation error system. Then, numerically testable conditions on the existence of a desired fuzzy mode-dependent state estimator are presented such that the estimation error system approaches to be mean square stable to an adjustable level and achieves a prescribed ${H_{\infty }}$ disturbance attenuation index. Finally, an illustrative example of a single-link robotic arm system is provided to demonstrate the effectiveness and the superiority of the design method of state estimator.
Changes in the appendicular skeleton during metamorphosis in the axolotl salamander (Ambystoma mexicanum)

Axolotl salamanders (Ambystoma mexicanum) remain aquatic in their natural state, during which biomechanical forces on their diarthrodial limb joints are likely reduced relative to salamanders living on land. However, even as sexually mature adults, these amphibians can be induced to metamorphose into a weight‐bearing terrestrial stage by environmental stress or the exogenous administration of thyroxine hormone. In some respects, this aquatic to terrestrial transition of axolotl salamanders through metamorphosis may model developmental and changing biomechanical skeletal forces in mammals during the prenatal to postnatal transition at birth and in the early postnatal period. To assess differences in the appendicular skeleton as a function of metamorphosis, anatomical and gene expression parameters were compared in skeletal tissues between aquatic and terrestrial axolotls that were the same age and genetically full siblings. The length of long bones and area of cuboidal bones in the appendicular skeleton, as well as the cellularity of cartilaginous and interzone tissues of femorotibial joints were generally higher in aquatic axolotls compared with their metamorphosed terrestrial siblings. A comparison of steady‐state mRNA transcripts encoding aggrecan core protein (ACAN), type II collagen (COL2A1), and growth and differentiation factor 5 (GDF5) in femorotibial cartilaginous and interzone tissues did not reveal any significant differences between aquatic and terrestrial axolotls.
Biomimetic Tissue Engineering: Tuning the Immune and Inflammatory Response to Implantable Biomaterials

Regenerative medicine technologies rely heavily on the use of well‐designed biomaterials for therapeutic applications. The success of implantable biomaterials hinges upon the ability of the chosen biomaterial to negotiate with the biological barriers in vivo. The most significant of these barriers is the immune system, which is composed of a highly coordinated organization of cells that induce an inflammatory response to the implanted biomaterial. Biomimetic platforms have emerged as novel strategies that aim to use the principle of biomimicry as a means of immunomodulation. This principle has manifested itself in the form of biomimetic scaffolds that imitate the composition and structure of biological cells and tissues. Recent work in this area has demonstrated the promising potential these technologies hold in overcoming the barrier of the immune system and, thereby, improve their overall therapeutic efficacy. In this review, a broad overview of the use of these strategies across several diseases and future avenues of research utilizing these platforms is provided.
Consensus of Discrete-Time Multiagent Systems With Input Delays by Truncated Pseudo-Predictor Feedback

The consensus problem for multiagent systems (MASs) described by discrete-time linear systems with multiple input delays is investigated. Under two reasonable assumptions, a truncated pseudo-predictor feedback (TPPF) approach is established to solve the consensus problem. The proposed TPPF protocols allow arbitrarily large yet bounded delays and are easy to implement in practice since they are finite dimensional and only use the relative current state information of neighboring agents. Moreover, the proposed protocols can also achieve semi-global consensus of MASs if the actuators are subject to saturations. A numerical example is given to illustrate the effectiveness of the proposed approaches.
Impact of adverse drug reactions on the incremental cost-effectiveness of bedaquiline for drug-resistant tuberculosis.

BACKGROUND Adverse drug reactions (ADRs) are common during standard, long-course treatment for multidrug-resistant and rifampicin-resistant tuberculosis (MDR-/RR-TB). In particular, second-line injectables (SLIs) are associated with permanent hearing loss, acute renal injury and electrolyte imbalance. We adapted an established Markov model for ambulatory treatment to estimate the impact of the toxicity profile on the incremental cost-effectiveness ratio (ICER) for a proposed MDR-/RR-TB regimen replacing the SLI with bedaquiline (BDQ). METHODS Treatment effectiveness was evaluated in disability-adjusted life-years (DALYs). Clinical outcomes and ingredient costs from a provider perspective were derived from the South African public-sector treatment program or extracted from the literature. Costs and effectiveness were discounted at 3% per year over 10 years. RESULTS A BDQ-based MDR-/RR-TB regimen compared with the SLI regimen had a mean ICER of US$516 per DALY averted using the standard Markov model. Costs for both regimens increased and effectiveness decreased for the SLI regimen once adjusted for toxicity. The resulting ICER for the BDQ-based regimen was cost saving (US$96/patient) and more effective (0.96 DALYs averted) after adjusting for ADRs. CONCLUSION Decision-analysis models of treatment for MDR-/RR-TB, including new drug regimens, should consider the costs of managing ADRs and their sequelae.
Adaptive Control for Rendezvous Problem of Networked Uncertain Euler–Lagrange Systems

This paper further improves the results for the leader-following rendezvous problem for multiple uncertain Euler–Lagrange systems. Based on a self-tuning adaptive observer, which can work under the more relaxed assumption that the information of the leader system is only available to informed followers in the rendezvous network, a full state distributed control law, depending only on the neighbor’s information, is first proposed to preserve the connectivity of the initially connected rendezvous network, as well as to achieve the asymptotic tracking of leader’s signal. And then such full state control strategy is further improved to be independent of the relative velocity of two neighboring agents by introducing an additional time-varying nonlinear term, determined by potential function. At the same time, dynamic gains are also introduced to make control parameter independent of system dynamics.
Sparse parameter estimation of LTI models with lp sparsity using genetic algorithm

Sparse optimisation for the identification of parametric linear model structure is equivalent to the estimation of the parameter vector. After relaxing the assumption on the order of the system, sparse optimisation techniques can be utilised to find the optimal model. This paper proposes an optimisation method to find the sparse parameter estimates. For this purpose, lp norm (0 < p < 1) penalty of parameter vector is added to the quadratic loss function which is further minimised using genetic algorithm. For the model structures other than ARX, a simulation model is realised using conditions on the quadratic simulation error. A real coded genetic algorithm is used to minimise the simulation error model. Simulation results are given for the ARX and output error model structures to show the effectiveness of the simulation error model method.
Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface

Objective: We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way. Methods: We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time. Results: A dataset of 7 subjects (4–7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach. Conclusion: We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion. Significance: The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.
Extraction and Automatic Grouping of Joint and Individual Sources in Multisubject fMRI Data Using Higher Order Cumulants

The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of joint sources by applying a simple and efficient strategy to determine the type of sources (joint or individual). The algorithm also categorizes similar sources automatically across data sets through an optimization process. The proposed algorithm is evaluated by analyzing simulated functional magnetic resonance imaging (fMRI) multisubject data sets, and its performance is compared with existing alternatives. We investigate clean and noisy fMRI signals and consider two source models. Our results reveal that the proposed algorithm outperforms its alternatives in terms of the mean joint signal to interference ratio. We also apply the proposed algorithm on a public-available real fMRI multisubject data set, which was acquired during naturalistic auditory experience. The extracted results are in accordance with the previous studies on naturalistic audio listening and results of a recent study investigated this data set, which demonstrates that the JI-ThICA algorithm can be applied to extract reliable and meaningful information from multisubject fMRI data.
Exploiting Combination Effect for Unsupervised Feature Selection by  $\ell_{2,0}$  Norm

In learning applications, exploring the cluster structures of the high dimensional data is an important task. It requires projecting or visualizing the cluster structures into a low dimensional space. The challenges are: 1) how to perform the projection or visualization with less information loss and 2) how to preserve the interpretability of the original data. Recent methods address these challenges simultaneously by unsupervised feature selection. They learn the cluster indicators based on the $k$ nearest neighbor similarity graph, then select the features highly correlated with these indicators. Under this direction, many techniques, such as local discriminative analysis, nonnegative spectral analysis, nonnegative matrix factorization, etc., have been successfully introduced to make the selection more accurate. In this paper, we focus on enhancing the unsupervised feature selection in another perspective, namely, making the selection exploit the combination effect of the features. Given the expected feature amount, previous works operate on the whole features then select those of high coefficients one by one as the output. Our proposed method, instead, operates on a group of features initially then update the selection when a better group appears. Compared to the previous methods, the proposed method exploits the combination effect of the features by $\ell {}_{2,0}$ norm. It improves the selection accuracy where the cluster structures are strongly related to a group of features. We conduct the experiments on six open access data sets from different domains. The experimental results show that our proposed method is more accurate than the recent methods which do not specially consider the combination effect of the features.
Postfire nitrogen balance of Mediterranean shrublands: Direct combustion losses versus gaseous and leaching losses from the postfire soil mineral nitrogen flush

Fire is a major factor controlling global carbon (C) and nitrogen (N) cycling. While direct C and N losses caused by combustion have been comparably well established, important knowledge gaps remain on postfire N losses. Here, we quantified both direct C and N combustion losses as well as postfire gaseous losses (N2O, NO and N2) and N leaching after a high‐intensity experimental fire in an old shrubland in central Spain. Combustion losses of C and N were 9.4 Mg C/ha and 129 kg N/ha, respectively, representing 66% and 58% of initial aboveground vegetation and litter stocks. Moreover, fire strongly increased soil mineral N concentrations by several magnitudes to a maximum of 44 kg N/ha 2 months after the fire, with N largely originating from dead soil microbes. Postfire soil emissions increased from 5.4 to 10.1 kg N ha−1 year−1 for N2, from 1.1 to 1.9 kg N ha−1 year−1 for NO and from 0.05 to 0.2 kg N ha−1 year−1 for N2O. Maximal leaching losses occurred 2 months after peak soil mineral N concentrations, but remained with 0.1 kg N ha−1 year−1 of minor importance for the postfire N mass balance. 15N stable isotope labelling revealed that 33% of the mineral N produced by fire was incorporated in stable soil N pools, while the remainder was lost. Overall, our work reveals significant postfire N losses dominated by emissions of N2 that need to be considered when assessing fire effects on ecosystem N cycling and mass balance. We propose indirect N gas emissions factors for the first postfire year, equalling to 7.7% (N2‐N), 2.7% (NO‐N) and 5.0% (N2O‐N) of the direct fire combustion losses of the respective N gas species.
Phylogeny, evolution, and biogeographic history of Calandrinia (Montiaceae).

PREMISE OF THE STUDY Calandrinia are small, succulent herbs that vary broadly in habitat, morphology, life history, and photosynthetic metabolism. The lineage is placed within the Montiaceae, which in turn is sister to the rest of the Portulacineae (Caryophyllales). Calandrinia occupy two distinct biogeographic regions, one in the Americas (~14 species), and one in Australia (~74 species). Past analyses of the Montiaceae present conflicting hypotheses for the phylogenetic placement and monophyly of Calandrinia, and to date, there has been no molecular phylogenetic analysis of the Australian species. METHODS Using a targeted gene enrichment approach, we sequenced 297 loci from multiple gene families across the Montiaceae, including all named and 16 putative new species of Australian Calandrinia, and the enigmatic monotypic genus Rumicastrum. KEY RESULTS All data sets and analyses reject the monophyly of Calandrinia, with Australian and New World Calandrinia each comprising distinct and well-supported clades, and Rumicastrum nested within Australian Calandrinia. We provide the first well-supported phylogeny for Australian Calandrinia, which includes all named species and several phrase-named taxa. CONCLUSIONS This study brings much needed clarity to relationships within Montiaceae and confirms that New World and Australian Calandrinia do not form a clade. Australian Calandrinia is a longtime resident of the continent, having diverged from its sister lineage ~30 Ma, concurrent with separation of Australia from Antarctica. Most diversification occurred during the middle Miocene, with lowered speciation and/or higher extinction rates coincident with the establishment of severe aridity by the late Miocene.
Increasing Nerve Autograft Length Increases Senescence and Reduces Regeneration

Background: Nerve grafting with an autograft is considered the gold standard. However, the functional outcomes of long (>3 cm) nerve autografting are often poor. The authors hypothesized that a factor contributing to these outcomes is the graft microenvironment, where long compared to short autografts support axon regeneration to different extents. Methods: A rat sciatic nerve defect model was used to compare regeneration in short (2 cm) and long (6 cm) isografts. Axon regeneration and cell populations within grafts were assessed using histology, retrograde labeling of neurons regenerating axons, immunohistochemistry, quantitative reverse transcriptase polymerase chain reaction, and electron microscopy at 4 and/or 8 weeks. Results: At 8 weeks, for distances of both 1 and 2 cm from the proximal coaptation (equivalent regenerative distance), long isografts had reduced numbers of regenerated fibers compared with short isografts. Similarly, the number of motoneurons regenerating axons was reduced in the presence of long isografts compared with short isografts. Considering the regenerative microenvironments between short and long isografts, cell densities and general populations within both short and long isografts were similar. However, long isografts had significantly greater expression of senescence markers, which included senescence-associated &bgr;-galactosidase, p21, and p16, and distinct chromatin changes within Schwann cells. Conclusions: This study shows that axon regeneration is reduced in long compared with short isografts, where long isografts contained an environment with an increased accumulation of senescent markers. Although autografts are considered the gold standard for grafting, these results demonstrate that we must continue to strive for improvements in the autograft regenerative environment.