Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise synchronisation and stochastic bifurcations in lasers

This paper studies noise synchronisation in terms of random pullback attractors and their instabilities. We consider an ensemble of uncoupled lasers, each being a limit-cycle oscillator, which are driven by the same external white Gaussian noise. As the external-noise strength increases, there is an onset of synchronization and then subsequent loss of synchrony. Local analysis of the laser equations shows that synchronization becomes unstable via stochastic bifurcation to a random strange attractor. The locus of this bifurcation is calculated in the three-dimensional parameter space defined by the Hopf parameter, amount of amplitude-phase coupling or shear, and external-noise strength. The analysis uncovers a square-root law for this stochastic bifurcation.
Analgesic Medication in Fibromyalgia Patients: A Cross-Sectional Study

There is no approved drug for fibromyalgia syndrome (FMS) in Europe. In the German S3 guideline, amitriptyline, duloxetine, and pregabalin are recommended for temporary use. The aim of this study was to cross-sectionally investigate the current practice of medication in FMS patients in Germany. We systematically interviewed 156 patients with FMS, while they were participating in a larger study. The patients had been stratified into subgroups with and without a decrease in intraepidermal nerve fiber density. The drugs most commonly used to treat FMS pain were nonsteroidal anti-inflammatory drugs (NSAIDs) (41.0% of all patients), metamizole (22.4%), and amitriptyline (12.8%). The most frequent analgesic treatment regimen was “on demand” (53.9%), during pain attacks, while 35.1% of the drugs were administered daily and the remaining in other regimens. Median pain relief as self-rated by the patients on a numerical rating scale (0–10) was 2 points for NSAIDS, 2 for metamizole, and 1 for amitriptyline. Drugs that were discontinued due to lack of efficacy rather than side effects were acetaminophen, flupirtine, and selective serotonin reuptake inhibitors. Reduction in pain severity was best achieved by NSAIDs and metamizole. Our hypothesis that a decrease in intraepidermal nerve fiber density might represent a neuropathic subtype of FMS, which would be associated with better effectiveness of drugs targeting neuropathic pain, could not be confirmed in this cohort. Many FMS patients take “on-demand” medication that is not in line with current guidelines. More randomized clinical trials are needed to assess drug effects in FMS subgroups.
A skilful seasonal prediction for wintertime rainfall in southern Thailand

Year‐to‐year variations of southern Thailand rainfall (STR) in boreal winter exert profound social and economic impacts, whereas current multimodel ensemble prediction systems have low skills to predict the STR. This study proposes a physical‐based seasonal prediction model for the winter STR 1 month in advance using the outputs from the dynamic models. The prediction model is constructed using linear regression, with the tropical western Pacific (TWP) sea surface temperature (SST) anomaly in preceding October as a predictor. Its prediction skill in the leave‐five‐out cross‐validation is significantly higher than that of the multimodel ensemble mean. The mechanism behind this model is also discussed. In October, the warm TWP SST anomalies can trigger anomalous low‐level convergence surrounding the South China Sea in terms of the Matsuno–Gill mechanism and persist into the following winter, causing above‐than‐normal STR. This information is essential and may provide another perspective to improve the model prediction on the winter STR.
Fetal Programming of the Endocrine Pancreas: Impact of a Maternal Low-Protein Diet on Gene Expression in the Perinatal Rat Pancreas

In rats, the time of birth is characterized by a transient rise in beta cell replication, as well as beta cell neogenesis and the functional maturation of the endocrine pancreas. However, the knowledge of the gene expression during this period of beta cell expansion is incomplete. The aim was to characterize the perinatal rat pancreas transcriptome and to identify regulatory pathways differentially regulated at the whole organ level in the offspring of mothers fed a regular control diet (CO) and of mothers fed a low-protein diet (LP). We performed mRNA expression profiling via the microarray analysis of total rat pancreas samples at embryonic day (E) 20 and postnatal days (P) 0 and 2. In the CO group, pancreas metabolic pathways related to sterol and lipid metabolism were highly enriched, whereas the LP diet induced changes in transcripts involved in RNA transcription and gene regulation, as well as cell migration and apoptosis. Moreover, a number of individual transcripts were markedly upregulated at P0 in the CO pancreas: growth arrest specific 6 (Gas6), legumain (Lgmn), Ets variant gene 5 (Etv5), alpha-fetoprotein (Afp), dual-specificity phosphatase 6 (Dusp6), and angiopoietin-like 4 (Angptl4). The LP diet induced the downregulation of a large number of transcripts, including neurogenin 3 (Neurog3), Etv5, Gas6, Dusp6, signaling transducer and activator of transcription 3 (Stat3), growth hormone receptor (Ghr), prolactin receptor (Prlr), and Gas6 receptor (AXL receptor tyrosine kinase; Axl), whereas upregulated transcripts were related to inflammatory responses and cell motility. We identified differentially regulated genes and transcriptional networks in the perinatal pancreas. These data revealed marked adaptations of exocrine and endocrine in the pancreas to the low-protein diet, and the data can contribute to identifying novel regulators of beta cell mass expansion and functional maturation and may provide a valuable tool in the generation of fully functional beta cells from stem cells to be used in replacement therapy.
Bactericidal Activity of Carvacrol against Streptococcus pyogenes Involves Alteration of Membrane Fluidity and Integrity through Interaction with Membrane Phospholipids

Background: Carvacrol, a mono-terpenoid phenol found in herbs, such as oregano and thyme, has excellent antibacterial properties against Streptococcus pyogenes. However, its mechanism of bactericidal activity on S. pyogenes has not been elucidated. Objectives: This study investigated the bactericidal mechanism of carvacrol using three strains of S. pyogenes. Methods: Flow cytometry (FCM) experiments were conducted to determine carvacrol’s membrane permeabilization and cytoplasmic membrane depolarization activities. Protoplasts of S. pyogenes were used to investigate carvacrol’s effects on the membrane, followed by gel electrophoresis. The carvacrol-treated protoplasts were examined by transmission electron microscopy (TEM) to observe ultrastructural morphological changes. The fluidity of the cell membrane was measured by steady-state fluorescence anisotropy. Thin-layer chromatographic (TLC) profiling was conducted to study the affinity of carvacrol for membrane phospholipids. Results: Increased membrane permeability and decreased membrane potential from FCM and electron microscopy observations revealed that carvacrol killed the bacteria primarily by disrupting membrane integrity, leading to whole-cell lysis. Ultra-structural morphological changes in the membrane induced by carvacrol over a short period were confirmed using the S. pyogenes protoplast and membrane isolate models in vitro. In addition, changes in the other biophysical properties of the bacterial membrane, including concentration- and time-dependent increased fluidity, were observed. TLC experiments showed that carvacrol preferentially interacts with membrane phosphatidylglycerol (P.G.), phosphatidylethanolamine (P.E.), and cardiolipins (CL). Conclusions: Carvacrol exhibited rapid bactericidal action against S. pyogenes by disrupting the bacterial membrane and increasing permeability, possibly due to affinity with specific membrane phospholipids, such as P.E. , P.G. , and CL. Therefore, the bactericidal concentration of carvacrol (250 µg/mL) could be used to develop safe and efficacious natural health products for managing streptococcal pharyngitis or therapeutic applications.
Research on the Ablation Characteristics and Thermomechanical Coupling Matching of Continuous-Wave/Pulsed Combined Lasers

In laser processing and electro-optical countermeasures, the laser ablation efficiency on the target is of great importance. Compared with traditional single laser irradiation, ablation by continuous-wave (CW)/pulsed combined laser has the advantages of high ablation efficiency, strong controllability, and low laser energy output requirements. However, current research on the synergistic mechanism and matching of combined laser irradiation requires improvement. In this study, damage experiments were mainly conducted on two modes of combined laser, CW/millisecond (CW/ms) and CW/nanosecond (CW/ns) laser. The temperature distribution, ablation rate, and ablation range under different irradiation conditions were tested quantitatively. The ablation characteristics and thermomechanical coupling matching of the combined laser were analysed and physically modelled. The results showed that compared with the pulsed laser alone, the ablation rate of the CW/ms combined mode was greater by approximately one order of magnitude, while that of the CW/ms combined mode was greater by approximately three orders of magnitude. Moreover, the thermomechanical coupling matching of the combined laser can be regarded as the matching of the impact pressure distribution of the pulsed laser with the range of the melting pool. The results of this study may offer guidance for further improving the synergistic enhancement damage mechanism of the combined laser.
Poly(sodium acrylate)-Modified Magnetite Nanoparticles for Separation of Heavy Metals from Aqueous Solutions

Two types of magnetite nanoparticles: unmodified (Fe3O4 NPs), and modified with poly(sodium acrylate) (Fe3O4/PSA NPs) were synthesized by the co-precipitation method and characterized using different techniques: X-ray diffraction (XRD), transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), Brunauer–Emmett–Teller (BET) adsorption, Fourier-transform infrared spectroscopy (FTIR). Additionally, magnetic properties and the effect of pH on the zeta potential were analyzed for both types of nanoparticles. Magnetites were used as adsorbents for seven heavy metal ions (Zn(II), Cu(II), Ni(II), Cd(II), Pb(II), Cr(III), Cr(VI)) within the pH range of 3–7. Research revealed nanometric particle sizes, a specific surface area of 140–145 m2/g, and superparamagnetic properties of both tested materials. Moreover, the presence of PSA functional groups in modified magnetite was confirmed, which lowered the pH of the isoelectric point. Both types of magnetite were effective metal ion adsorbents, with metal cations more effectively removed on Fe3O4/PSA NPs and Cr(VI) anions on Fe3O4 NPs. The adsorption of most of the examined cations (performed at pH = 5) can be well described by the Langmuir isotherm model, whereas the adsorption of Cr(VI) ions on modified magnetite correlated better with the Freundlich model. The Dubinin–Radushkevich model confirmed that chemisorption is the predominant process. The adsorption of all metal ions was well-characterized by the pseudo-second-order kinetic model.
Expensive Multiobjective Optimization Based on Information Transfer Surrogate

Objective value estimation based on computationally efficient surrogate models is widely used to reduce the computational cost in solving expensive multiobjective optimization problems (MOPs). However, due to the scarcity of training data and the lack of data sharing between training tasks in a surrogate-based system, the estimation effectiveness of the surrogate models might not be satisfactory. In this study, we present a novel surrogate methodology based on information transfer to deal with this problem. Particularly, in the proposed framework, the objectives of an MOP that may have little apparent similarity or correlation are linearly mapped to a number of related tasks. Afterward, the related tasks are used to train a multitask Gaussian process (MTGP). MTGP expands the training data leading to more confident learning of the parameters of the model. The predicted values of the objective functions can be obtained by a reverse mapping from the learned MTGP model. In this way, the computational burden of the expensive objective functions of an MOP can be substantially reduced while maintaining good estimation accuracy. MTGP facilitates mutual information transfer across tasks, avoids learning from scratch for new tasks, and captures the underlying structural information between tasks. The proposed surrogate approach is merged into MOEA/D to address MOPs. Experimental tests under various scenarios indicate that the resultant algorithm outperforms other state-of-the-art surrogate-based multiobjective optimization algorithms.
T Cells Chemotaxis Migration Studies with a Multi-Channel Microfluidic Device

Immune surveillance is dependent on lymphocyte migration and targeted recruitment. This can involve different modes of cell motility ranging from random walk to highly directional environment-guided migration driven by chemotaxis. This study protocol describes a flow-based microfluidic device to perform quantitative multiplex cell migration assays with the potential to investigate in real time the migratory response of T cells at the population or single-cell level. The device also allows for subsequent in situ fixation and direct fluorescence analysis of the cells in the microchannel.
Using a graph rewriting system for databases

A major portion of a database management system deals with maintaining graph-like structures using a variety of data structures and algorithms. In this paper we describe a method to partly automate their construction using a rewrite system for colored graphs. Our approach provides both a formal basis for the description of the DBMS semantics and it will improve the exploitation of the potential parallelism in a DBMS. 1986 CR Categories: 0.2. 1 [Software Engineering]: Requirements/Specification languages 0.3. 2 [Programming Languages]: Language classification nonprocedural very high-level languages, 0.3. 3 [Programming Languages]: Language constructs concurrent programming and control structures H.2. 0 [Database Management]: Physical design access methods. 1980 Mathematics Subject Classification: 69021, 69042, 69043, 69H22.
Image Reconstruction Analysis for Positron Emission Tomography With Heterostructured Scintillators

The concept of structure engineering has been proposed for exploring the next generation of radiation detectors with improved performance. A TOF-PET geometry with heterostructured scintillators with a pixel size of <inline-formula> <tex-math notation="LaTeX">$3.0 \times 3.1 \times 15\,\,\mathrm {mm^{3}}$ </tex-math></inline-formula> was simulated using Monte Carlo. The heterostructures consisted of alternating layers of BGO as a dense material with high stopping power and plastic (EJ232) as a fast light emitter. The detector time resolution was calculated as a function of the deposited and shared energy in both materials on an event-by-event basis. While sensitivity was reduced to 32% for 100-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> thick plastic layers and 52% for <inline-formula> <tex-math notation="LaTeX">$50~\mathrm {\mu m}$ </tex-math></inline-formula>, the coincidence time resolution (CTR) distribution improved to 204 ± 49 and 220±41 ps, respectively, compared to 276 ps that we considered for bulk BGO. The complex distribution of timing resolutions was accounted for in the reconstruction. We divided the events into three groups based on their CTR and modeled them with different Gaussian TOF kernels. On an NEMA IQ phantom, the heterostructures had better contrast recovery in early iterations. On the other hand, BGO achieved a better contrast-to-noise ratio (CNR) after the 15th iteration due to the higher sensitivity. The developed simulation and reconstruction methods constitute new tools for evaluating different detector designs with complex time responses.
Bias and variance of treatment effect estimators using propensity-score matching

Observational studies are an indispensable complement to randomized clinical trials (RCT) for comparison of treatment effectiveness. Often RCTs cannot be carried out due to the costs of the trial, ethical questions and rarity of the outcome. When noncompliance and missing data are prevalent, RCTs become more like observational studies. The main problem is to adjust for the selection bias in the observational study. One increasingly used method is propensity-score matching. Compared to traditional multi-covariate matching methods, matching on the propensity score alleviates the curse of dimensionality. It allows investigators to balance multiple covariate distributions between treatment groups by matching on a single score. This thesis focuses on the large sample properties of the matching estimators of the treatment effect. The first part of this thesis deals with problems of the analytic supports of the logit propensity score and various matching methods. The second part of this thesis focuses on the matching estimators of additive and multiplicative treatment effects. We derive the asymptotic order of the biases and asymptotic distributions of the matching estimators. We also derive the large sample variance estimators for the treatment effect estimators. The methods and theoretical results are applied and checked in a series of simulation studies. The third part of this thesis is devoted to a comparison between propensity-score matching and multiple linear regression using simulation. iii TABLE OF CONTENTS LIST OF TABLES . vi LIST OF FIGURES . xix CHAPTER
Kernel Projection Algorithm for Large—Scale SVM Problems

Support Vector Machine (SVM) has become a very effective method in sta-tistical machine learning and it has proved that training SVM is to solve Nearest Point pairProblem (NPP) between two disjoint closed convex sets. Later Keerthi pointed out that it isdifficult to apply classical excellent geometric algorithms directly to SVM and so designed anew geometric algorithm for SVM. In this article, a new algorithm for geometrically solvingSVM, Kernel Projection Algorithm, is presented based on the theorem on fixed-points of pro-jection mapping. This new algorithm makes it easy to apply classical geometric algorithmsto solving SVM and is more understandable than Keerthi's. Experiments show that the newalgorithm can also handle large-scale SVM problems. Geometric algorithms for SVM, such asKeerthi's algorithm, require that two closed convex sets be disjoint and otherwise the algo-rithms are meaningless. In this article, this requirement will be guaranteed in theory by usingthe theoretic result on universal kernel functions.
The Antivirulence Activity of Umbelliferone and Its Protective Effect against A. hydrophila-Infected Grass Carp

A. hydrophila is an important pathogen that mainly harms aquatic animals and has exhibited resistance to a variety of antibiotics. Here, to seek an effective alternative for antibiotics, the effects of umbelliferone (UM) at sub-MICs on A. hydrophila virulence factors and the quorum-sensing system were studied. Subsequently, RNA sequencing was employed to explore the potential mechanisms for the antivirulence activity of umbelliferone. Meanwhile, the protective effect of umbelliferone on grass carp infected with A. hydrophila was studied in vivo. Our results indicated that umbelliferone could significantly inhibit A. hydrophila virulence such as hemolysis, biofilm formation, swimming and swarming motility, and their quorum-sensing signals AHL and AI-2. Transcriptomic analysis showed that umbelliferone downregulated expression levels of genes related to exotoxin, the secretory system (T2SS and T6SS), iron uptake, etc. Animal studies demonstrated that umbelliferone could significantly improve the survival of grass carps infected with A. hydrophila, reduce the bacterial load in the various tissues, and ameliorate cardiac, splenic, and hepatopancreas injury. Collectively, umbelliferone can reduce the pathogenicity of A. hydrophila and is a potential drug for treating A. hydrophila infection.
NCache: A Machine-Learning Cache Management Scheme for Computational SSDs

Inside a solid-state disk (SSD), cache stores frequently accessed data to shorten the user-I/O response time and reduce the number of read/write operations in flash memory, thereby improving SSD performance and lifetime. Most existing cache schemes anchor in the spatiotemporal locality of I/O requests in workloads. In the face of a long-time workload, high performance and hit rate often get lost in these caching schemes. Flash memory-aware caching schemes trade hit ratio to prolong SSD lifetime. In this article, we advocate for a machine-learning-based caching scheme named NCache to optimize both hit ratio and SSD performance. In NCache, we construct a machine learning (i.e., ML) model to predict whether data are reaccessed before being evicted from the cache. The cache replacement scheme preferentially evicts data that would not be accessed in the cache. The cache space is conserved for valid data that are likely to be repeatedly accessed. A pipelined scheme is implemented to accelerate the ML model, alleviating the time-cost of NCache. A double-linked list boosts the data addressing and cache replacement process. NCache is orthogonal to the existing caching schemes within the flash translation layer. The results validate NCache under a handful of real-world enterprise traces. Taking prn_0 as an example, NCache reduces the response time of LRU, clean first LRU (CFLRU), GCaR_LRU, GCaR_CFLRU, and LCR by up to 15% with an average of 6.4%. The erase count is slashed by 16% at the maximum. Importantly, NCache is adroit at optimizing write amplification by up to 15.9%.
Structural Testing by Torsion of Scalable Wind Turbine Blades

In life service, the wind turbine blades are subjected to compound loading: torsion, bending, and traction, all these resulting in the occurrence of normal and tangential stresses. At some points, the equivalent stresses, due to overlapping effects provided by normal and shear stresses, can have high values, close to those for which the structure can reach to the failure point. If the effects of erosion and clashes with foreign bodies are added, the structure of the blade may lose its integrity. Considering both the complex shape of the blade and internal structure used, the mechanical behavior of the blade, such as the rigidity and resistance along the length of the blade, are usually determined with some uncertainty. This paper presents the results obtained in the non-destructive tests at static torsion of a scalable wind turbine blade. The objective of the paper was to determine the variation of the equivalent stress in the most stressed points of the blade, in relation to the torques applied. To determine the points with the highest stress, a finite element analysis was performed on the scalable wind turbine blade. Electrotensiometric transducers were mounted at different points of the blade, determining the main stresses in the respective points, as well as their variation during the torsion test, by subsequent calculations. The determinations were performed by applying the torque in both senses, in relation to the blade axis, thus concluding the values of the equivalent stress in the two cases.