Miracle-dz/UniFAR-FTUs huggingface dataset:
The APSANTICO Study: A Prospective Observational Study to Evaluate Antiphospholipid Antibody Profiles in Patients with Thromboembolic Antiphospholipid Syndrome (APS) after COVID-19 Infection and/or Vaccination

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new virus discovered in December 2019 that causes coronavirus disease 19 (COVID-19) and various vaccinations have been developed. The extent to which COVID-19 infections and/or COVID-19 vaccinations alter antiphospholipid antibodies (aPL) in patients with thromboembolic antiphospholipid syndrome (APS) remains unclear. Eighty-two patients with confirmed thromboembolic APS were included in this prospective non-interventional trial. Blood parameters including lupus anticoagulants, anticardiolipin IgG- and IgM-antibodies, and anti-ß2-glycoprotein I IgG- and IgM-antibodies were assessed prior to and after COVID-19 vaccination and/or COVID-19 infection. No increases in aPL in the total study population were detected. In fact, low but significant decreases were observed for anticardiolipin IgG- and anti-β2-glycoprotein I IgG-antibodies, while anticardiolipin IgM- and anti-b2-glycoprotein I IgM-antibodies slightly increased only in patients with COVID-19 infection and vaccination. Although the investigated patient group is known to have a high risk of recurrent thrombosis, only one arterial thrombotic event was diagnosed (1.2%, 1/82). This low recurrence rate was probably due to the high vaccination rates prior to infections and a high rate of effective anticoagulation. Our data show that COVID-19 infections and/or vaccinations do not deteriorate the clinical course of anticoagulated thromboembolic APS patients.
Purine metabolism regulates Vibrio splendidus persistence associated with protein aggresome formation and intracellular tetracycline efflux

A small subpopulation of Vibrio splendidus AJ01 that was exposed to tetracycline at 10 times the minimal inhibitory concentration (MIC) still survived, named tetracycline-induced persister cells in our previous work. However, the formation mechanisms of persister is largely unknown. Here, we investigated tetracycline-induced AJ01 persister cells by transcriptome analysis and found that the purine metabolism pathway was significantly downregulated, which was consistent with lower levels of ATP, purine, and purine derivatives in our metabolome analysis. Inhibition of the purine metabolism pathway by 6-mercaptopurine (6-MP, inhibits ATP production), increased persister cell formation and accompanied with the decreasing intracellular ATP levels and increasing cells with protein aggresome. On the other hand, the persister cells had reduced intracellular tetracycline concentrations and higher membrane potential after 6-MP treatment. Inhibition of the membrane potential by carbonyl cyanide m-chlorophenyl hydrazone reversed 6-MP-induced persistence and resulted in higher levels of intracellular tetracycline accumulation. Meanwhile, cells with 6-MP treatment increased the membrane potential by dissipating the transmembrane proton pH gradient, which activated efflux to decrease the intracellular tetracycline concentration. Together, our findings show that reduction of purine metabolism regulates AJ01 persistence and is associated with protein aggresome formation and intracellular tetracycline efflux.
Bootstrap procedures in a spatial-temporal model

Two bootstrap procedures are introduced into the hybrid of the backfitting algorithm and the Cochrane–Orcutt procedure in the estimation of a spatial-temporal model. The use of time blocks of consecutive observations in resampling steps proved to be optimal in terms of stability and efficiency of estimates. Between iterations, there were minimal changes in the empirical distributions of the parameter estimates associated with the covariate and temporal effects indicating convergence of the algorithm. Crop yield data are used to illustrate the proposed methods. The simulation study indicated that prediction error from the fitted model (estimated from either Method 1 or Method 2) is very low. Also, the prediction error is relatively robust to the number of spatial units and the number of time points.
Posterior Vitreous Detachment and Its Role in the Evolution of Dry to Wet Age Related Macular Degeneration

Purpose To examine the state of the posterior vitreous in eyes with exudative age-related macular degeneration, AMD, non-exudative AMD and in normal eyes. Study This is a prospective, cross-sectional study. Methods B-scan ultrasonography and Optical Coherence Tomography, OCT were performed in 165 patients older than 65 years with any AMD and in 22 patients older than 65 years with normal eyes in order to diagnose the eyes with complete posterior vitreous detachment, PVD and the eyes with persistent central vitreomacular adhesion, VMA. All patients were selected from the outpatient clinic of the Ophthalmology Department in the University Hospital of Patras. Fundus Fluoroangiography, FFA was used in order to determine the development of exudative AMD from non-exudative AMD. Follow up time was 48 months. Results 16/171 eyes with exudative AMD (9.36%) had complete PVD, and the rest 155/171 (90.64%) had central VMA. Eleven of 138 eyes with non-exudative AMD (7.97%) had complete PVD and the remaining 127 eyes (92.03%) had central VMA. During the 48 months of the study, 28 eyes, all with central VMA progressed to exudative AMD. Conclusion Vitreomacular adhesion is associated with both exudative and non-exudative AMD. Progression of the non-exudative eyes to exudative AMD seems to be lower in eyes with complete PVD. On the other hand, the progression of normal eyes to exudative AMD appears to be independent of the posterior vitreous status. Larger and longer studies need to replicate these findings and support the potential of a protective role of complete posterior vitreous detachment in the evolution of the disease.
Electrical impedance myography combined with quantitative assessment techniques in paretic muscle of stroke survivors: Insights and challenges

Aging is a non-modifiable risk factor for stroke and the global burden of stroke is continuing to increase due to the aging society. Muscle dysfunction, common sequela of stroke, has long been of research interests. Therefore, how to accurately assess muscle function is particularly important. Electrical impedance myography (EIM) has proven to be feasible to assess muscle impairment in patients with stroke in terms of micro structures, such as muscle membrane integrity, extracellular and intracellular fluids. However, EIM alone is not sufficient to assess muscle function comprehensively given the complex contributors to paretic muscle after an insult. This article discusses the potential to combine EIM and other common quantitative methods as ways to improve the assessment of muscle function in stroke survivors. Clinically, these combined assessments provide not only a distinct advantage for greater accuracy of muscle assessment through cross-validation, but also the physiological explanation on muscle dysfunction at the micro level. Different combinations of assessments are discussed with insights for different purposes. The assessments of morphological, mechanical and contractile properties combined with EIM are focused since changes in muscle structures, tone and strength directly reflect the muscle function of stroke survivors. With advances in computational technology, finite element model and machine learning model that incorporate multi-modal evaluation parameters to enable the establishment of predictive or diagnostic model will be the next step forward to assess muscle function for individual with stroke.
The convex and monotone functions associated with second-order cone

Like the matrix-valued functions used in solutions methods for semidefinite programs (SDPs) and semidefinite complementarity problems (SDCPs), the vector-valued functions associated with second-order cones are defined analogously and also used in solutions methods for second-order-cone programs (SOCPs) and second-order-cone complementarity problems (SOCCPs). In this article, we study further about these vector-valued functions associated with second-order cones (SOCs). In particular, we define the so-called SOC-convex and SOC-monotone functions for any given function . We discuss the SOC-convexity and SOC-monotonicity for some simple functions, e.g., f(t) = t 2 t 3 1/t t 1/2, |t|, and [t]+. Some characterizations of SOC-convex and SOC-monotone functions are studied, and some conjectures about the relationship between SOC-convex and SOC-monotone functions are proposed.
Comment: Bayesian Statistics in the Twenty First Century

In my first reading of Gelman–Robert (GR), I felt a bit chagrined. I spent many years fighting to publish Bayesian work, and still have to fight, but not as hard as before. My first attempt to publish a Bayesian data analysis in a scientific journal resulted in rejection with a brief report that basically said, “We know that Bayesian statistics is subjective, and we don’t want that in our journal. ” Fortunately, that article and others like it were subsequently published. On many occasions, I have heard statistical colleagues make subtle disparaging remarks about Bayesian statistics. My favorite response is, “The problem with Bayesian statistics is that it is too easy. ” With the popularization and rapid development of Markov chain Monte Carlo (MCMC) methods that occurred after Gelfand and Smith (1990), there has been an explosion of Bayesian activity putting Bayesian work more into the mainstream. Sometime after 1990, I decided to be a Bayesian (I used to do both Bayesian and frequentist stuff) by which I mean working with scientists to analyze their data, eliciting prior information from them and their colleagues. I now practice the art of data analysis in pursuit of answers to scientific questions only using the Bayesian approach. For many years, I wrote Bayesian articles comparing Bayesian with frequent methods, on frequentist terms. The reverse rarely occurs, that is, frequentist articles that compare their methods with Bayesian ones. Samaniego (2010) and references therein are exceptions but Samaniego’s stated goal is to make such comparisons. I realized from teaching Bayesian statistics to graduate students outside of statistics that such comparisons were not necessary to sell the Bayesian approach, since it already seemed completely natural to them. I wrote a book with friends that makes no attempt to compare Bayesian methods and analyses with frequentist methods; see Christensen et al. (2010). When I first read GR, I wondered why they wanted to dredge up, what to me was, old news, bad memories, and water over the dam. As I now read GR, I realize that there are probably many who use Bayesian statistical methods and who are unfamiliar with the views that GR discussed. I was not aware of the Feller quote but it characterizes what was a widely held mindset. In my final reading, I found the article to be enormously
Matured compost amendment improves compost nutrient content by changing the bacterial community during the composting of Chinese herb residues

Composting is a sustainable strategy to deal with organic waste. Our research aimed to study the influence of an amendment of 10% matured compost (MC) during Chinese herb residue (CHR) compost. Here, a 60-day CHR compost was performed, and MC application was able to reduce the nitrogen loss and enhance the humic acid accumulation during the composting as compared with the non-inoculated control (NC), by 25 and 19%, respectively. Furthermore, the matured compost amendment improved the diversity of the bacterial community, increased the complexity of the co-occurrence network, and changed the keystone and module hub bacteria during composting. The increased abundance levels of Thermopolyspora, Thermobispora, and Thermosporomyces, which were significantly higher in MC than in NC, may contribute to the degradation of cellulose and the formation of humic acid. Overall, this study extends our understanding of the effects of matured compost reflux on compost quality and the bacterial community.
SPATIOTEMPORAL CHAOS SYNCHRONIZATION BY ACTIVE\|OCCASIONAL COUPLING

This paper proposes the occasional coupling synchronization scheme of the active\|passive decomposition (active\|occasional coupling). This scheme consists of synchronization and autonomous phases.In the former,the synchronization scheme is used to synchronize the drive and the response systems;in the latter,small errors will lead to the two systems separated from each other and desynchronization.But if the synchronization phases are large enough,the full synchronization will be obtained.The synchronization condition is discussed theoretically.The spatiotemporal chaos in coupled map lattice system is achieved and the synchronization condition is given.Moreover,the relation between the synchronization phases and the coupling strength is investigated numerically.  PACC: 0545
Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing

Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit system. Since the metro or other rail transit have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing train bogie-bearings under variable working conditions is challenging due to wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for bogie-bearing fault diagnosis is proposed to deal with the above problems. A third-order tensor model is established to be suitable for variable working conditions. Furthermore, a density-based affinity propagation tensor (DAP-Tensor) clustering algorithm is presented to identify different failures with unlabeled. Train bogie and public data sets were employed to simulate three probable conditions of train operation: high-frequency impact, speed variation, and load change. Compared with existing clustering methods in three cases, the proposed DAP-Tensor performs better in identifying bearing faults under variable working conditions. Moreover, The DAP-tensor has a comparable recognition rate to some deep learning methods, which unsupervised characteristics show it has potential for applications on rail transit trains.
Regime shifts found in the Northern Hemisphere SST field

A 'regime shift' is characterized by an abrupt transition from one quasi-steady climatic state to another, and its transition period is much shorter than the lengths of the individual epochs of each climatic state. In the present study, we investigate when regime shifts occurred and what was the difference in climatic states before and after the shifts, using the wintertime sea surface temperature (SST) field in the Northern Hemisphere. The relationship between changes in the SST field, and those in the atmospheric circulation, is also investigated. In order to detect organized patterns of the SST variations, we apply an empirical orthogonal function (EOF) analysis. As the results, the first mode is identical to El Nino/Southern Oscillation (ENSO) and so-called Pacific Decadal Oscillation (PDO), and corresponds to the Pacific/North American (PNA) pattern. The second mode, which relates to the Arctic Oscillation (AO), has a zonally elongated signal in both the North Atlantic, and North Pacific. EOF analyses to each oceanic basin are made separately, and the robustness of these modes is confirmed. In the present study, we define the regime shifts as the 'significant' and 'systematic' changes between the two quasi-steady states, continuing more than 5 years. Then, in order to identify the years when regime shifts occurred in the SST field, we carefully inspect the time series of original gridded SST data and those of the EOF modes. As a result, six regime shifts are detected in the study period from the 1910s to the 1990s: 1925/26, 1945/46, 1957/58, 1970/71, 1976/77 and 1988/89. It is ascertained that the shifts at almost all grids are completed within one year. All regime shifts having similar SST and atmospheric circulation pattern, including the changes in an intensity of the Aleutian Low (AL), and the corresponding SST changes in the central North Pacific. All regime shifts can be well described by the combination of the first and the second EOF modes. The duration between each regime shift is about 10 years, which are identical to the PDO. The simultaneous shifts in the first, and the second EOF modes, imply that the change in the AL activity associated with the PNA pattern, might have some connection with that of the AO.
Topological horseshoes and delay differential equations

We show that if an ordinary differential equation $x'=f(x)$, where $x\in \mathbb R^n$ and $f \in \mathcal C^1$, has a topological horseshoe, then the corresponding delay equation $x'(t)=f(x(t-h))$ for small $h >0$ also has a topological horseshoe, i.e. symbolic dynamics and an infinite number of periodic orbits. A method of computation of $h$ is given in terms of topological properties of solutions of differential inclusion $x'(t) \in f(x(t)) + \bar B(0,\delta)$.
A Survey of Demonstration Learning

With the fast improvement of machine learning, reinforcement learning (RL) has been used to automate human tasks in different areas. However, training such agents is difficult and restricted to expert users. Moreover, it is mostly limited to simulation environments due to the high cost and safety concerns of interactions in the real world. Demonstration Learning is a paradigm in which an agent learns to perform a task by imitating the behavior of an expert shown in demonstrations. It is a relatively recent area in machine learning, but it is gaining significant traction due to having tremendous potential for learning complex behaviors from demonstrations. Learning from demonstration accelerates the learning process by improving sample efficiency, while also reducing the effort of the programmer. Due to learning without interacting with the environment, demonstration learning would allow the automation of a wide range of real world applications such as robotics and healthcare. This paper provides a survey of demonstration learning, where we formally introduce the demonstration problem along with its main challenges and provide a comprehensive overview of the process of learning from demonstrations from the creation of the demonstration data set, to learning methods from demonstrations, and optimization by combining demonstration learning with different machine learning methods. We also review the existing benchmarks and identify their strengths and limitations. Additionally, we discuss the advantages and disadvantages of the paradigm as well as its main applications. Lastly, we discuss our perspective on open problems and research directions for this rapidly growing field.
Many fields interaction: Beam splitters and waveguide arrays

We study the interaction of many fields. We obtain an effective Hamiltonian for this system by using a method recently introduced that produces a small rotation to the Hamiltonian that allows to neglect some terms in the rotated Hamiltonian. We show that coherent states remain coherent under the action of a quadratic Hamiltonian and by solving the eigenvalue and eigenvector problem for tridiagonal matrices we also show that a system of n interacting harmonic oscillators, initially in coherent states, remain coherent during the interaction.
Reduction of harmonic distortion in dual magnet type microspeaker

Harmonic distortion due to uneven magnetic fields is becoming more important in mobile phones to generate high quality sound. In this paper, analysis of sound pressure level and reduction of harmonic distortion is presented by considering mechanical and magnetic coupling effects. It shows that high-performance microspeakers with lower harmonic distortion is realizable with proper magnetic circuit design. Experimental verification is also followed for dual magnet-type microspeakers.
A model for the spreading and compaction of two-phase viscous gravity currents

Two-phase viscous gravity current theory has numerous applications in the natural sciences, from small-scale lava, sedimentary and glacial flows, to large-scale flows of partially molten mantle. We develop the general equations for two-phase viscous gravity currents composed of a high viscosity matrix and low viscosity fluid for both constant volume and constant flux conditions. A loss of fluid phase is taken into account at the current's upper boundary and corresponds to the degassing of a lava flow or loss of water in sedimentary flows. As the current spreads, its surface increases and fluid loss is facilitated, which modifies the mixture density and viscosity and thus the current's shape; hence spreading of the flow affects fluid loss and vice-versa. Our results show that two-phase gravity currents retain and transport the fluid out to large distances, but the fluid is almost entirely lost within a region of finite radius. This ‘loss radius’ depends on the flow's volume or flux, fluid and matrix properties as well as on the size of fluid parcels or matrix permeability. Application to lava flows shows that degassing occurs over a large area, which affects gas release and transport in the atmosphere.
A partially collapsed Gibbs sampler for Bayesian quantile regression

We introduce a set of new Gibbs sampler for Bayesian analysis of quantile regression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler. Although the MetropolisHastings algorithm has been employed in Bayesian quantile regression, including median regression, PCG has superior convergence properties to an ordinary Gibbs sampler. Moreover, Our PCG sampler algorithm, which is based on a theoretic derivation of an asymmetric Laplace as scale mixtures of normal distributions, requires less computation than the ordinary Gibbs sampler and can significantly reduce the computation involved in approximating the Bayes Factor and marginal likelihood. Like the ordinary Gibbs sampler, the PCG sample can also be used to calculate any associated marginal and predictive distributions. The quantile regression PCG sampler is illustrated by analysing simulated data and the data of length of stay in hospital. The latter provides new insight into hospital performance. C-code along with an R interface for our algorithms is publicly available on request from the first author. JEL classification: C11, C14, C21, C31, C52, C53.