Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Markovianity of the damped Jaynes-Cummings model with detuning

The degree of non-Markovian behavior of a damped Jaynes-Cummings model with detuning is investigated. Our attention is focused on the effects of the detuning and the width of the Lorentzian spectral density on the degree of non-Markovian behavior. It is found that an increase of the detuning can make the information exchange between the qubit and the reservoir more rapid, and this leads to an increase in the degree of non-Markovianity for some cases, while an increase of the spectral width always leads to a decrease in the degree of non-Markovianity.
Measurements of aerosol charging states in Helsinki, Finland

In the first stage, papers that pass a rapid access peer-review are immediately published on the Atmospheric Chemistry and Physics Discussions (ACPD) website. They are then subject to Interactive Public Discussion, during which the referees' comments (anonymous or attributed), additional short comments by other members of the scientific community (attributed) and the authors' replies are also published in ACPD. In the second stage, the peer-review process is completed and, if accepted, the final revised papers are published in ACP. To ensure publication precedence for authors, and to provide a lasting record of scientific discussion, ACPD and ACP are both ISSN-registered, permanently archived and fully citable.
Temperature and depth dependence of the spatial distribution of snow crab

The cascading effects of rapid climate change is a reality with which all biota are challenged. In this context, we examine the spatiotemporal probability of occurrence of snow crab as a means to express viable habitat. This is attempted for three demographic components, morphometrically mature males and females and immature adolescent crab in the Scotian Shelf region of the northwest Atlantic, Canada. We use a robust approach, known as Conditional AutoRegressive models, to define viable habitat. Further, we focus upon viable habitat, conditioned on the marginal influence of temperature and depth as they are known to be important constraints on snow crab. We observe some niche partitioning in terms of depth and temperature. We also note declines in viable habitat marginal to depth and temperature since 2010 for all demographic groups. This population representing the southern-most distribution of snow crab in the northwest Atlantic are vulnerable to degradation of viable habitat attributable to rapid climate change. One-Sentence Summary Rapid climate change and a decadal scale change in the viable habitat of snow crab of the Scotian Shelf ecosystem.
Greengenes2 enables a shared data universe for microbiome studies

16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR amplification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.
A superoxide reductase contributes to Clostridioides difficile resistance to oxygen

Clostridioides difficile causes a serious diarrheal disease and is a common healthcare-associated bacterial pathogen. Although it has a major impact on human health, mechanistic details of C. difficile intestinal colonization remain undefined. C. difficile is highly sensitive to oxygen and requires anaerobic conditions for in vitro growth. However, the mammalian gut is not entirely devoid of oxygen, and C. difficile tolerates moderate oxidative stress in vivo. The C. difficile genome encodes several antioxidant proteins, including a predicted superoxide reductase (SOR) that is upregulated upon exposure to antimicrobial peptides. The goal of this study was to establish SOR enzymatic activity and assess its role in protecting C. difficile against oxygen exposure. Insertional inactivation of sor rendered C. difficile more sensitive to superoxide indicating that SOR contributes to antioxidant defense. Heterologous C. difficile sor expression in Escherichia coli conferred protection against superoxide-dependent growth inhibition, and the corresponding cell lysates showed superoxide scavenging activity. Finally, a C. difficile SOR mutant exhibited global proteome changes under oxygen stress when compared to its parent strain. Collectively, our data establish the enzymatic activity of C. difficile SOR, confirm its role in protection against oxidative stress, and demonstrate its broader impacts on the vegetative cell proteome. Importance Clotridioides difficile is an important pathogen strongly associated with healthcare settings and capable of causing severe diarrheal disease. While considered a strict anaerobe in vitro, C. difficile has been shown to tolerate low levels of oxygen in its mammalian host. Among other well-characterized antioxidant proteins, the C. difficile genome includes a predicted superoxide reductase (SOR), an understudied component of antioxidant defense in pathogens. The significance of the research reported herein is the characterization of the enzymatic activity of the putative SOR protein, including confirmation of its role in protection of C. difficile against oxidative stress. This furthers our understanding of C. difficile pathogenesis and presents a potential new avenue for targeted therapies.
Anti-Biofilm Potential of Nanonized Eugenol against Pseudomonas aeruginosa

This study dealt with nanonization of eugenol, a major phytochemical present in basil leaf, which has pharmacological potential as an anti-bacterial agent. Eugenol nanoparticle (ENP) was synthesized by simple ultrasonic cavitation method through emulsification of hydrophobic eugenol into hydrophilic gelatin. Thus, the nanonization process made the water-insoluble eugenol to water-soluble nano-eugenol, making the nano-form bioavailable. The average size of the ENPs was 20-30 nm. Entrapment efficiency of eugenol within gelatin cap was about 80% of the eugenol, that was used as precursor in the nanonization reaction. In vitro release of eugenol from gelatin cap was slow and sustained over a period of five days. The ENP had higher anti-biofilm potency than eugenol for both formation and eradication of biofilm, formed by clinically relevant pathogen Pseudomonas aeruginosa. Minimal biofilm inhibitory concentration and minimal biofilm eradication concentration of ENPs were 2.0 and 4.0 mM respectively. In addition, the measurement of P. aeruginosa biofilm biomass, biofilm pellicle formation, biofilm thickness, amount of biofilm-forming extra-polymeric substance, cell surface hydrophobicity, cell swarming and twitching efficiencies, cellular morphology and biofilm formation in catheter demonstrated that the anti-biofilm efficacy of nano-eugenol was 30-40% higher than that of bulk eugenol. Thus, ENP can be used as a potential drug against pneumonia, a chronic infection in lung caused by P. aeruginosa, which is difficult to treat with antibiotics, due to natural intrinsic resistance of biofilm-formed cells to most antibiotics. The overall actions of ENP have been presented in the figure 1. Highlights Nano-formulation of eugenol, an important phytochemical, by ultrasonic cavitation method, which was simple, time-saving, low-cost and eco-friendly. Nanonization made water-insoluble eugenol into water-soluble form with enhanced therapeutic efficacy. The eugenol nanoparticle (ENP) could inhibit formation of biofilm as well as facilitate eradication of pre-formed biofilm of P. aeruginosa. Biofilm formation was found to be prevented significantly on ENP-coated catheter. Nano-eugenol may be used as a potential drug against bacterial diseases, caused by pseudomonal biofilm, which are difficult to treat by antibiotics. Nano-formulated eugenol may also be used as an effective anti-fouling agent for biomedical devices like contact lens, pace-maker, materials for organ transplantation etc. to prevent bacterial colonization.
Decoding Neural Patterns for the Processing of Fearful Faces under Different Visual Awareness Conditions: A Multivariate Pattern Analysis

Mixed findings have been reported for the nonconscious processing of fearful faces. Here, we used multivariate pattern analysis on electroencephalography data from three backward masking experiments to decode the conscious and nonconscious processing of fearful faces. Three groups of participants were shown pairs of faces that were presented either subliminally (16 ms) or supraliminally (266 ms) and were required to complete tasks where the face stimuli were either task-relevant (Experiment 1) or task-irrelevant (Experiments 2 and 3). We decoded the neural activity to examine the temporal dynamics of visual awareness, and to investigate whether the presence and location of a fearful face were processed when levels of awareness varied. The results reveal that the spatial location of fearful faces can be decoded from neural patterns only when they are consciously seen and relevant to participants’ task. Nevertheless, the processing of the mere presence of fearful faces can occur in the absence of visual awareness, and the related neural patterns can be generalised to the conscious, non-spatial processing of fearful faces. Additionally, the flexibility of spatial attention seems to modulate the processing of fearful faces.
Applying Unet for extraction of vascular metrics from T1-weighted and T2-weighted MRI

We apply deep learning to the problem of segmenting the arterial system from T1w and T2w images. We use the freely available 7-Tesla ‘forrest’ dataset from OpenNeuro, (which contains TOF, T1w, and T2w) and use supervised learning with T1w or T2w as input, and TOF segmentation as ground truth, to train a Unet architecture capable of segmenting arteries and quantifying arterial diameters from T1w or T2w images alone. We demonstrate arterial segmentations from both T1w and T2w images, and show that T2w images have sufficient vessel contrast to estimate arterial diameters comparable to those estimated from TOF. We then apply our Unet to T2w images from a separate dataset (IXI) and show our model generalizes to images acquired at different field strength. We consider this work proof-of-concept that arterial segmentations can be derived from MRI sequences with poor contrast between arteries and surrounding tissue (T1w and T2w), due to the ability of deep convolutional networks to extract complex features based on local image intensity. Future work will focus on improving the generalizability of the network to non-forrest datasets, with the eventual goal of leveraging the entire pre-existing corpus of neuroimaging data for study of human cerebrovasculature.
Flame structure from a kinetic model for chemical reactions

Steady one-dimensional flame structure is investigated in a binary mixture made up by two components of the same chemical species undergoing binary irreversible exothermic reactive encounters. A kinetic model at the Boltzmann level, accounting for chemical transitions as well as for mechanical collisions, is proposed and its main features are analyzed. In the case of slow chemical reactions and collision dominated regime, the model is the starting point for a consistent derivation, via suitable asymptotic expansion of Chapman-Enskog type, of reactive Navier-Stokes equations at the fluid-dynamic scale. The resulting set of ordinary differential equations is investigated in the frame of the qualitative theory of dynamical systems, and numerical results are presented and briefly commented on for illustrative purposes.
Integrated multi-channel nano-engineered optical hydrogen and temperature sensor detection systems for launch vehicles

Launch vehicles and other satellite users need launch services that are highly reliable, less complex, easier to test, and cost effective. Being a very small molecule, hydrogen is prone to leakage through seals and micro-cracks. Hydrogen detection in space application is very challenging; public acceptance of hydrogen fuel would require the integration of a reliable hydrogen safety sensor. For detecting leakage of cryogenic fluids in spaceport facilities, launch vehicle industry and aerospace agencies are currently relying heavily on the bulky mass spectrometers, which fill one or more equipment racks, and weigh several hundred kilograms. Therefore, there is a critical need for miniaturized sensors and instruments suitable for use in space applications. This paper describes a novel multi-channel integrated nano-engineered optical sensor to detect hydrogen and monitor the temperature. The integrated optic sensor is made of multi-channel waveguide elements that measure hydrogen concentration in real Time. Our sensor is based on the use of a high index waveguide with a Ni/Pd overlay to detect hydrogen. When hydrogen is absorbed into the Ni/Pd alloy there is a change in the absorption of the material and the optical signal in the waveguide is increased. Our design uses a thin alloy (few nanometers thick) overlay which facilitates the absorption of the hydrogen and will result in a response time of approximately few seconds. Like other Pd/Pd-Ni based sensors the device response varies with temperature and hence the effects of temperature variations must be taken into account. One solution to this problem is simultaneous measurement of temperature in addition to hydrogen concentration at the same vicinity. Our approach here is to propose a temperature sensor that can easily be integrated on the same platform as the hydrogen sensor reported earlier by our group. One suitable choice of material system is silicon on insulator (SOI). Here, we propose a micro ring resonators (MRR) based temperature sensor designed on SOI that measures temperature by monitoring the output optical power.
High permittivity thin film nanolaminates

Thin (∼10 nm) films comprising of Ta2O5–HfO2, Ta2O5–ZrO2, and ZrO2–HfO2 nanolaminates were deposited and characterized for possible gate dielectric applications. These films were deposited on silicon substrates using atomic layer deposition. The dielectric constants of these films were in 12–14 range and the leakage currents in 2.6×10−8–4.2×10−7 A/cm2 range at 1 MV/cm electric field. It was found that as these films were made thinner, the value of their dielectric constant dropped compared to their bulk values. The dominant leakage current mechanism at low electric fields was determined to be Schottky emission, whereas Poole–Frenkel emission dominated at higher fields.
Maternal microbes and early brain development in mouse

The complex symbiotic relationship between the mammalian body and gut microbiome plays a critical role in the health outcomes of offspring later in life. The gut microbiome modulates virtually all physiological functions through direct or indirect interactions to maintain physiological homeostasis. Previous studies indicate a link between maternal/early-life gut microbiome, brain development, and behavioral outcomes relating to social cognition. Here we present direct evidence of the role of the gut microbiome in brain development. Through magnetic resonance imaging (MRI), we investigated the impact of the gut microbiome on brain organization and structure using germ-free (GF) mice and conventionalized mice, with the gut microbiome reintroduced after weaning. We found broad changes in brain volume in GF mice that persist despite the reintroduction of gut microbes at weaning. These data suggest a direct link between the maternal gut or early-postnatal microbe and their impact on brain developmental programming.
The visual surface brightness relation and the absolute magnitudes of RR Lyrae stars. I - Theory

A theoretical relation analogous to the Barnes-Evans relation between stellar surface brightness and V-R color is derived which is applicable to the temperatures and gravities appropriate to RR Lyrae stars. Values of the visual surface brightness and V-R colors are calculated for model stellar atmospheres with effective temperatures between 6000 and 8000 K, log surface gravities from 2.2 to 3.5, and A/H anbundance ratios from -0.5 to -3.0. The resulting relation is found to be in reasonable agreement with the empirical relation of Barnes, Evans and Moffet (1978), with, however, small sensitivities to gravity and metal abundance. The relation may be used to derive stellar angular diameters from (V,R) photometry and to derive radii, distances, and absolute magnitudes for variable stars when combined with a radial velocity curve. The accuracies of the radii and distances (within 10%) and absolute magnitudes (within 0.25 magnitudes) compare favorably with those of the Baade-Wesselink method currently in use.
A NOVEL SEGREGATED ALGORITHM FOR INCOMPRESSIBLE FLUID FLOW AND HEAT TRANSFER PROBLEMS—CLEAR (COUPLED AND LINKED EQUATIONS ALGORITHM REVISED) PART I: MATHEMATICAL FORMULATION AND SOLUTION PROCEDURE

A novel segregated solution procedure for incompressible fluid flow and heat transfer problems is proposed. The new algorithm is called CLEAR (Coupled and Linked Equations Algorithm Revised). It differs from all SIMPLE-like algorithms in that it solves the improved pressure directly, rather than by adding a correction term, and no term is dropped in the derivation of the pressure equation. Thus the effects of the neighboring velocity values are fully taken into account, and the coupling between velocity and pressure is fully guaranteed, greatly enhancing the convergence rate of the iteration process. Its robustness is improved by introducing a second relaxation factor. The mathematical formulation and the solution procedure of the CLEAR algorithm are described in detail in this article. Comprehensive discussion is conducted to describe the difference between the CLEAR algorithm and all other existing algorithms of the SIMPLE family. In Part II, six numerical application examples with available numerical solutions are provided to show the feasibility of the new algorithm.
CMA-ES-Rosetta: Blackbox optimization algorithm traverses rugged peptide docking energy landscapes

Energy minimization is necessary for virtually all modeling and design tasks and involves traversing extremely rugged energy landscapes. Although the gradient descent based minimization routines in Rosetta have fast runtimes, due to these rugged landscapes, minimization often converges into high-energy local minima. Alternative numerical optimization techniques, such as evolution strategies, are more robust to rugged landscapes and have been shown to be highly successful on a diverse set of problems. Here we explore the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm, as a complementary approach to the default minimizer in Rosetta. We used a benchmark of 26 peptides, from the FlexPepDock Benchmark, to assess the performance of three algorithms in Rosetta, specifically, CMA-ES, Rosetta’s default minimizer, and a Monte Carlo protocol of small backbone perturbations. We test the algorithms’ performance on their ability to dock an idealized peptide to a series of hotspots residues (i.e. constraints) along a native peptide. Of the three methods, CMA-ES was able to find the lowest energy conformation for 23 out of 26 benchmark peptides. The application of CMA-ES allows for an alternative optimization method for macromolecular modeling problems with rough energy landscapes.
Ex-Post Identification of Task Models With Causally Ordered User Interface Logs (Extended Abstract)

Identifying adequate back-office tasks to automate is a significant problem in adopting robotic process automation. Task mining on user interface logs enables departments to detect the underlying task model, but creating these logs is time and resource-consuming. Furthermore, contrary to process mining logs that can be extracted in an ex-post fashion from ERP systems, user interface logs require definitions prior to the recording: abstract activities and mapping to lower-level user interface interactions need to be specified beforehand. To this end, the presented research project proposes a new framework for desktop activity mining that enables interaction recording prior to defining tasks and activities to be mined.
Supporting Sportspeople in Gaining Bodily Insights Through Reflective Feedback

With the proliferation of affordable sensing devices, more and more sportspeople are able to monitor their exercise sessions and gain valuable insights into their exercise form and exertion. Yet, manufactures seldom employ intrinsic motivation of their users as a motivational factor and rather rely on external elements such as gamification. Ultimately users might fail to see the potential benefits of their exercises and rather blindly strive for completing the next app challenge, lacking active reflection about their exercise form. We argue that this aspect is quintessential in acquiring genuine proficiency for a given exercise, yet current sports technology is seldom designed to encourage active reflection from users. In this position paper, we depict how designing for reflective feedback , leveraging mobile and wearable sensing devices, provides users with the means to actively reflect on their exercise form. We envision that this — already emerging form of feedback — will allow users to gain deeper bodily insights and facilitate an inherent understanding of the meaning and purpose of their physical activity. Based on existing research works, we highlight the potential of this approach to generalize well over a diverse set of physical activities and outline future research directions.
A Framework for Implementing Process Mining and Robotic Process Automation in Organizations (Extended Abstract)

Process Mining (PM) is a technique that combines data science and process modeling to discover process models from system execution logs collecting activities and their timestamps, user identifiers, resources, and other parameters. PM is used to understand and improve processes. Robotic Process Automation (RPA) is an emerging technology that allows businesses to automate processes with software robots that replicate repetitive human tasks. RPA helps achieve accuracy, consistency, and efficiency in performing processes. Understanding processes is the key to automating them, but organizations often lack a deep understanding of their as-is processes and often rely on guess work to discover their automation opportunities. Using PM as a key enabler in discovering the processes that can be automated and in monitoring improvements is not an easy task and comes with several challenges. The main focus of this doctoral thesis is on introducing a new framework to help integrate the use of PM and RPA in an organization’s context. The framework consists of several components including methods and tools.