julilili42/papers huggingface dataset:
Modelling spin-up episodes in accreting millisecond X-ray pulsars
2021-09-16

ABSTRACT Accreting millisecond X-ray pulsars are known to provide a wealth of physical information during their successive states of outburst and quiescence. Based on the observed spin-up and spin-down rates of these objects, it is possible, among other things, to infer the stellar magnetic field strength and test models of accretion disc flow. In this paper, we consider the three accreting X-ray pulsars (XTE J1751–305, IGR J00291+5934 and SAX J1808.4–3658) with the best available timing data, and model their observed spin-up rates with the help of a collection of standard torque models that describe a magnetically threaded accretion disc truncated at the magnetospheric radius. Whilst none of these models is able to explain the observational data, we find that the inclusion of the physically motivated phenomenological parameter ξ, which controls the uncertainty in the location of the magnetospheric radius, leads to an enhanced disc-integrated accretion torque. These ‘new’ torque models are compatible with the observed spin-up rates as well as the inferred magnetic fields of these objects provided that ξ ≈ 0.1−0.5. Our results are supplemented with a discussion of the relevance of additional physics effects that include the presence of a multipolar magnetic field and general relativistic gravity.
Nonlinear stability of soliton solutions for massive tensor-multiscalar theories
2021-09-16

The aim of this paper is to study the stability of soliton-like static\\nsolutions via non-linear simulations in the context of a special class of\\nmassive tensor-multi-scalar-theories of gravity whose target space metric\\nadmits Killing field(s) with a periodic flow. We focused on the case with two\\nscalar fields and maximally symmetric target space metric, as the simplest\\nconfiguration where solitonic solutions can exist. In the limit of zero\\ncurvature of the target space these solutions reduce to the\\nstandard boson stars, while for significant deviations can be\\nobserved, both qualitative and quantitative. By evolving these solitonic\\nsolutions in time, we show that they are stable for low values of the central\\nscalar field while instability kicks in with the increase of .\\nSpecifically, in the stable region, the models oscillate with a characteristic\\nfrequency related to the fundamental mode. Such frequency tends to zero with\\nthe approach of the unstable models and eventually becomes imaginary when the\\nsolitonic solutions lose stability. As expected from the study of the\\nequilibrium models, the change of stability occurs exactly at the maximum mass\\npoint, which was checked numerically with a very good accuracy.\\n
The Leaf Microbiota of Arabidopsis Displays Reproducible Dynamics And Patterns Throughout The Growing Season
2021-09-16

Abstract Background: Leaves are primarily responsible for the plant's photosynthetic activity. Thus, changes in the leaf microbiota, which includes deleterious and beneficial microbes, can have far reaching effects on plant fitness and productivity. Identifying the processes and microorganisms that drive these changes over a plant’s lifetime is, therefore, crucial. In this study we analyzed the temporal dynamics in the leaf microbiota of Arabidopsis thaliana, integrating changes in both, composition and microbe-microbe interactions via the study of microbial networks. Results: Field-grown Arabidopsis were used to monitor leaf bacterial, fungal and oomycete communities throughout the plant’s growing season (extending from November to March) over three consecutive years. Our results revealed the existence of conserved temporal patterns, with microbial communities and networks going through a stabilization phase of decreased diversity and variability at the beginning of the plant’s growing season. Despite a high turnover in these communities, we identified 19 'core' taxa persisting on Arabidopsis leaves across time and plant generations. With the hypothesis these microbes could be playing key roles in the structuring of leaf microbial communities, we conducted a time-informed microbial network analysis which showed core taxa are not necessarily highly connected network 'hubs' and 'hubs' alternate with time. Conclusions: Our study shows that leaf microbial communities exhibit reproducible dynamics and patterns, suggesting the possibility of predicting those patterns to drive microbial communities towards desired states.
Performance Errors Influence Voluntary Task Choices
2021-09-16

Humans adjust their behavior after they committed an error, but it is unclear whether and how error commissions influence voluntary task choices. In the present article, we review different accounts on effects of errors in the previous trial (transient error effects) and overall error probabilities (sustained error effects) on behavioral adaptation. Based on this review, we derived five statistical models how errors might influence voluntary task choices. We analyzed the data of three experiments in which participants voluntarily selected one of two tasks before each trial whereby task difficulty, and concomitantly error probability, increased successively for the selected/performed tasks. Model comparison suggested that choice behavior was best explained by a combination of error probability of the performed task, error probability of the alternative task, and whether the previous response was correct or incorrect. The results revealed that participants were most likely to switch tasks in situations where the error probability of the performed task was high, the error probability of the alternative task was low, and after an error on the previous trial. We conclude that task selection processes are influenced by transient and sustained error effects.
Chemical Proportionality within Molecular Networks
2021-09-17

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here, we report the <u>Chem</u>ical <u>Pro</u>portionality (ChemProp) contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series (e.g., temporal or spatial relationships), which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of (biosynthetically) related compounds. We tested the ChemProp workflow on a ground truth data set of a defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism, and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking (GNPS) environment.
Neuropsychiatric Disorders and COVID-19: What We Know So Far
2021-09-17

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) affects the central nervous system (CNS), which is shown in a significant number of patients with neurological events. In this study, an updated literature review was carried out regarding neurological disorders in COVID-19. Neurological symptoms are more common in patients with severe infection according to their respiratory status and divided into three categories: (1) CNS manifestations; (2) cranial and peripheral nervous system manifestations; and (3) skeletal muscle injury manifestations. Patients with pre-existing cerebrovascular disease are at a higher risk of admission to the intensive care unit (ICU) and mortality. The neurological manifestations associated with COVID-19 are of great importance, but when life-threatening abnormal vital signs occur in severely ill COVID-19 patients, neurological problems are usually not considered. It is crucial to search for new treatments for brain damage, as well as for alternative therapies that recover the damaged brain and reduce the inflammatory response and its consequences for other organs. In addition, there is a need to diagnose these manifestations as early as possible to limit long-term consequences. Therefore, much research is needed to explain the involvement of SARS-CoV-2 causing these neurological symptoms because scientists know zero about it.
Acute relaxation during pregnancy leads to a reduction in maternal electrodermal activity and self-reported stress levels
2021-09-17

Abstract Background Prenatal maternal stress can have adverse effects on birth outcomes and fetal development. Relaxation techniques have been examined as potential countermeasures. This study investigates different relaxation techniques and their effect on self-reported stress levels and physiological stress levels in pregnant women. Methods In this cross-sectional study, 38 pregnant women in their 30th to 40th gestational week were assigned to one of three, 20-min lasting relaxation groups: listening to music ( N = 12), following a guided imagery (N = 12) or resting (N = 12). The intervention, i.e., acute relaxation (music, guided imagery or resting) took place once for each study participant. Study inclusion criteria were age over 18 years, German speaking, singleton and uncomplicated pregnancy during the 30th and 40th week of gestation. The stress levels were determined during the study. Current stress level during the study was assessed by a visual analogue scale. Chronic stress levels were assessed by the Trier Inventory of Chronic Stress and the Pregnancy Distress questionnaire. Multivariate analyses of covariance were performed and dependent measures included stress levels as well as physiological measures, i.e., cardiovascular activity (electrocardiogram) and skin conductance levels. Results All three forms of relaxation led to reduced maternal stress which manifested itself in significantly decreased skin conductance, F(3,94) = 18.011, p = .001, η p 2 = .365, and subjective stress levels after the interventions with no significant group difference. Post-intervention stress ratings were further affected by gestational age, with less subjective relaxation in women later in gestation, F (1, 34)=4.971, p = .032, η p 2 = .128. Conclusion Independent of relaxation technique, single, 20-min relaxation intervention (music, guided imagery or resting) can significantly reduce maternal stress. Notably, women at an earlier stage in their pregnancy reported higher relaxation after the intervention than women later in gestation. Hence, gestational age may influence perceived stress levels and should be considered when evaluating relaxation or stress management interventions during pregnancy. Trial registration Not applicable.
Bonding strength of 3D printed silicone and titanium retention magnets for maxillofacial prosthetics application
2021-09-17

The application of both used primers may be an acceptable technical option for the anchorage of retention titanium magnets in silicone facial prostheses, produced additively in a fully digital workflow.
Distinct and Convergent Beneficial Effects of Estrogen and Insulin on Cognitive Function in Healthy Young Men
2021-09-17

Abstract Context Systematic investigations into the cognitive impact of estradiol and insulin in male individuals are sparse, and it is unclear whether the 2 hormones interact to benefit specific cognitive functions in humans. Objective We investigated the acute effect of estradiol and insulin and of their combined administration on divergent (creative) and convergent (arithmetical) thinking as well as short-term and working verbal memory in healthy young men. Methods According to a 2 × 2 design, 2 groups of men (each n = 16) received a 3-day transdermal estradiol (100 μg/24 h) or placebo pretreatment and on 2 separate mornings were intranasally administered 160 IU regular human insulin and, respectively, placebo before completing a battery of cognitive tests; we also determined relevant blood parameters. Results Estrogen compared with placebo treatment induced a 3.5-fold increase in serum estradiol and suppressed serum testosterone concentrations by 70%. Estrogen in comparison to placebo improved creative performance, that is, verbal fluency and flexibility, but not arithmetical thinking, as well as verbal short-term memory, but not visuospatial memory. The combination of estrogen and insulin enhanced recognition discriminability at delayed verbal memory recall; insulin alone remained without effect. Conclusion Estrogen specifically enhances core aspects of creativity and verbal memory in young male individuals; delayed recognition memory benefits from the combined administration of estradiol and insulin. Our results indicate that insulin’s acute cognitive impact in young men is limited and not robustly potentiated by estradiol. Estradiol per se exerts a beneficial acute effect on creative and verbal performance in healthy young men.
AHK5 mediates ETR1-initiated multistep phosphorelay in Arabidopsis
2021-09-17

Summary Plants, like other sessile organisms, need to sense many different signals, and in response to them, modify their developmental programs to be able to survive in a highly changing environment. The multistep phosphorelay (MSP) in plants is a good candidate for a response mechanism that integrates multiple signal types both environmental and intrinsic in origin. Recently, ethylene was shown to control MSP activity via the histidine kinase (HK) activity of ETHYLENE RESPONSE 1 (ETR1) 1,2 , but the underlying molecular mechanism still remains unclear. Here we show that although ETR1 is an active HK, its receiver domain (ETR1 RD ) is structurally and functionally unable to accept the phosphate from the phosphorylated His in the ETR1 HK domain (ETR1 HK ) to initiate the phosphorelay to ARABIDOPSIS HISTIDINE-CONTAINING PHOSPHOTRANSMITTERs (AHPs), the next link downstream members in MSP signaling. Instead, ETR1 interacts with another HK ARABIDOPSIS HISTIDINE KINASE 5 (AHK5) and transfers the phosphate from ETR1 HK through the receiver domain of AHK5 (AHK5 RD ), and subsequently to AHP1, AHP2 and AHP3, independently of the HK activity of AHK5. We show that AHK5 is necessary for ethylene-initiated, but not cytokinin-initiated, MSP signaling in planta and that it thus mediates hormonal control of root growth.
Classifying the Unknown: Identification of Insects by Deep Open-set Bayesian Learning
2021-09-17

Insects represent a large majority of biodiversity on Earth, yet only 20% of the estimated 5.5 million insect species are currently described (1). While describing new species typically requires specific taxonomic expertise to identify morphological characters that distinguish it from other potential species, DNA-based methods have aided in providing additional evidence of separate species (2). Machine learning (ML) is emerging as a potential new approach in identifying new species, given that this analysis may be more sensitive to subtle differences humans may not process. Existing ML algorithms are limited by image repositories that do not include undescribed species. We developed a Bayesian deep learning method for the open-set classification of species. The proposed approach forms a Bayesian hierarchy of species around corresponding genera and uses deep embeddings of images and barcodes together to identify insects at the lowest level of abstraction possible. To demonstrate proof of concept, we used a database of 32,848 insect instances from 1,040 described species split into training and test data. The test data included 243 species not present in the training data. Our results demonstrate that using DNA sequences and images together, insect instances of described species can be classified with 96.66% accuracy while achieving accuracy of 81.39% in identifying genera of insect instances of undescribed species. The proposed deep open-set Bayesian model demonstrates a powerful new approach that can be used for the gargantuan task of identifying new insect species.
Lifelong musical activity is associated with multi-domain cognitive and brain benefits in older adults
2021-09-17

1 A bstract Regular musical activity as a highly-stimulating lifestyle activity is proposed to be protective against age-related cognitive decline and Alzheimer’s disease (AD). This study investigated associations between lifelong regular musical instrument playing, late-life cognitive abilities and brain morphology in older adults. We show that musical activity over the life course is associated with better global cognition, working memory, executive functions, language, and visuospatial abilities accounting for reserve proxies. Playing music is not significantly associated with gray matter volume in regions most affected by aging and AD. Selectively in the musically active participants, multi-domain cognitive abilities were enhanced with preserved gray matter volume in frontal and temporal regions. Our correlational findings suggest that playing a musical instrument may improve the recruitment of existing brain resources to facilitate late-life cognitive capacities. We propose that engaging in regular musical activity could serve as a low-threshold multimodal enrichment strategy that may promote cognitive resilience in advanced age.
Force Mapping Study of Actinoporin Effect in Membranes Presenting Phase Domains
2021-09-18

Equinatoxin II (EqtII) and Fragaceatoxin C (FraC) are pore-forming toxins (PFTs) from the actinoporin family that have enhanced membrane affinity in the presence of sphingomyelin (SM) and phase coexistence in the membrane. However, little is known about the effect of these proteins on the nanoscopic properties of membrane domains. Here, we used combined confocal microscopy and force mapping by atomic force microscopy to study the effect of EqtII and FraC on the organization of phase-separated phosphatidylcholine/SM/cholesterol membranes. To this aim, we developed a fast, high-throughput processing tool to correlate structural and nano-mechanical information from force mapping. We found that both proteins changed the lipid domain shape. Strikingly, they induced a reduction in the domain area and circularity, suggesting a decrease in the line tension due to a lipid phase height mismatch, which correlated with proteins binding to the domain interfaces. Moreover, force mapping suggested that the proteins affected the mechanical properties at the edge, but not in the bulk, of the domains. This effect could not be revealed by ensemble force spectroscopy measurements supporting the suitability of force mapping to study local membrane topographical and mechanical alterations by membranotropic proteins.
Conceptual representation of real‐world surface material: Early integration with linguistic‐labels indicated in the N400‐component
2021-09-18

Abstract Research in perception in the visual and auditory domains has traditionally focused on investigating highly controlled artificial stimulus material. However, a key feature of our perceptual system is the ease with which the input of a wide set of naturalistic co‐occurring information is dealt with. This study investigated whether, during perception of real‐world surface material, a conceptual representation is built that has the potential to interact with a linguistic description of the material directly. Short sentences were presented (e.g., This surface is smooth ) followed by a matching or mismatching picture of a real‐world surface material. The results showed early cross‐modal integration effects during material surface perception in an N400‐like potential, originating approximately 280 ms after stimulus presentation. Overall, these findings suggest a rather early influence of linguistic information on material perception, suggesting that in line with object representation, real‐world materials are represented in the brain in a format that allows interaction with non‐visual information.
Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving
2021-09-19

Collective perception in Vehicle-to-Everything (V2X) communications allows vehicles to exchange preprocessed sensor data with other traffic participants. It is currently standardized by ETSI as a second generation V2X communication service. The use of collective perception as a communication service for future fully autonomous driving requires a thorough evaluation and validation. Most of the previous work on collective perception has considered largescale-simulations with a focus on communications. However, the perception pipeline used for collective perception is equally important and must not be neglected or over-simplified. Also, to study collective perception in detail, large-scale field testing is practically infeasible. In this paper we extend an existing simulation framework with a realistic model for V2X communications and sensor-data based processing delays. The result is a simulation framework that incorporates the entire collective perception pipeline, which enables to comprehensively study sensor-based perception. We demonstrate the capabilities of this enhanced framework by analyzing the delay of each component involved in the perception pipeline. This allows a detailed insight in end-to-end delays and the age of information within the environmental model of autonomous vehicles.
The Atlas of Lane Changes: Investigating Location-Dependent Lane Change Behaviors Using Measurement Data from a Customer Fleet
2021-09-19

The prediction of surrounding traffic participants behavior is a crucial and challenging task for driver assistance and autonomous driving systems. Today's approaches mainly focus on modeling dynamic aspects of the traffic situation and try to predict traffic participants behavior based on this. In this article we take a first step towards extending this common practice by calculating location-specific a-priori lane change probabilities. The idea behind this is straight forward: The driving behavior of humans may vary in exactly the same traffic situation depending on the respective location. E.g. drivers may ask themselves: Should I pass the truck in front of me immediately or should I wait until reaching the less curvy part of my route lying only a few kilometers ahead? Although, such information is far away from allowing behavior prediction on its own, it is obvious that today's approaches will greatly benefit when incorporating such location-specific a-priori probabilities into their predictions. For example, our investigations show that highway interchanges tend to enhance driver's motivation to perform lane changes, whereas curves seem to have lane change-dampening effects. Nevertheless, the investigation of all considered local conditions shows that superposition of various effects can lead to unexpected probabilities at some locations. We thus suggest dynamically constructing and maintaining a lane change probability map based on customer fleet data in order to support onboard prediction systems with additional information. For deriving reliable lane change probabilities a broad customer fleet is the key to success.
Augmenting Image Data Sets With Water Spray Caused by Vehicles on Wet Roads
2021-09-19

Adverse weather conditions challenge object detection neural networks, because they are mostly trained on clean data sets that were taken in good weather. But autonomous vehicles rely on accurate detection and classification of other road users for safe and reliable operation. Capturing new data sets in rough weather is time consuming and expensive. We therefore propose a method to augment existing image data sets with physically realistic water spray swirled up by vehicles—one of the most influential disturbances for even human drivers on wet roads in or after heavy rain. Using a wide range of newly augmented images, we evaluate the influence on an established convolutional neural network object detection and showcase the potential of training with these new augmented data sets.