Signatures Of Processes Research Articles

Neural dynamics of vicarious physical pain processing reflect impaired empathy toward sexually objectified versus non-sexually objectified women.

Sexually objectified women are perceived as dehumanized. This may affect the behavioral and neural responses underlying the observer's empathic reactions for their physical pain, although this hypothesis still lacks empirical support. In the present study, we measured the electrophysiological activity of 30 participants (14 females and 16 males), in an empathy for physical pain paradigm in which pictures of sexualized and non-sexualized women were presented in painful and non-painful situations. The behavioral results revealed that sexualized women were evaluated as experiencing less pain than non-sexualized women. Neural evidence corroborated this finding showing that the perception of vicarious physical pain is lacking for sexualized women in both event-related potentials (ERPs) and brain oscillation domains. Specifically, the P2 component and the event-related synchronization/desynchronization (ERS/ERD) on the mu frequency band differed between painful and non-painful stimulation exclusively when women were not sexualized. Our results provide the first evidence that the neurophysiological responses to the vicarious experience of physical pain are dampened or even absent for sexualized women. These findings expand our understanding of the neurophysiological signatures of empathic processes and highlight the detrimental effect of a sexual-objectification bias in everyday contexts.

Temporal and spatial shifts in the chemical composition of urban coastal rainwaters of Kuwait: The role of air mass trajectory and meteorological variables

The rainwater chemistry encompasses the signatures of geogenic and anthropogenic processes along the regional air mass movement apart from the local sources. The predominance of dust events and anthropogenic emissions in arid regions facilitate new particle formation. Further, rain events of different seasons depict moisture sources from diverse regions reflecting variation in the regional geochemistry with respect to seasons. Hence, to characterize the geochemical composition of rainwater, the study has focused on an integrated approach by considering regional transport, meteorological components and possible local sources. A total of 74 rainwater samples were collected from 27 rain events in 2018, 2019, and 2022, representing urban coastal areas of Kuwait predominantly of Ca-SO4-HCO3 type. The average pH and electrical conductivity of the rainwater were 7.18 and 140 μS/cm, respectively. The sea salt fractions calculated relative to Kuwait seawater ranged from 25.6 to >100 %, with higher values attributed to anthropogenic sources. Sea salt fraction, ion ratios, principal component analysis and factor scores revealed the terrestrial and anthropogenic sources apart from marine contributions. In addition, new particle formation and aerosols contributed to the rainwater chemistry involving SOx, NOx, and photochemical reactions during higher relative humidity and lesser wind speed. The HYSPLIT reflected that the moisture sources were largely from western regions of the study area, and those of December and January events had long-distance travel across the Azores high originating from northeast America. The trajectories of the November events are observed to originate from the Caspian/Black Sea region in the northeastern part of Kuwait with a relatively shorter distance of travel. The rainfall samples had higher ionic concentrations, and saturated with aragonite and calcite minerals in a few locations specifically after the dust events, while the subsequent rain events were less polluted.

EndoC-βH5 cells are storable and ready-to-use human pancreatic beta cells with physiological insulin secretion

ObjectivesReadily accessible human pancreatic beta cells that are functionally close to primary adult beta cells are a crucial model to better understand human beta cell physiology and develop new treatments for diabetes. We here report the characterization of EndoC-βH5 cells, the latest in the EndoC-βH cell family. MethodsEndoC-βH5 cells were generated by integrative gene transfer of immortalizing transgenes hTERT and SV40 large T along with Herpes Simplex Virus-1 thymidine kinase into human fetal pancreas. Immortalizing transgenes were removed after amplification using CRE activation and remaining non-excized cells eliminated using ganciclovir. Resulting cells were distributed as ready to use EndoC-βH5 cells. We performed transcriptome, immunological and extensive functional assays. ResultsReady to use EndoC-βH5 cells display highly efficient glucose dependent insulin secretion. A robust 10-fold insulin secretion index was observed and reproduced in four independent laboratories across Europe. EndoC-βH5 cells secrete insulin in a dynamic manner in response to glucose and secretion is further potentiated by GIP and GLP-1 analogs. RNA-seq confirmed abundant expression of beta cell transcription factors and functional markers, including incretin receptors. Cytokines induce a gene expression signature of inflammatory pathways and antigen processing and presentation. Finally, modified HLA-A2 expressing EndoC-βH5 cells elicit specific A2-alloreactive CD8 T cell activation. ConclusionsEndoC-βH5 cells represent a unique storable and ready to use human pancreatic beta cell model with highly robust and reproducible features. Such cells are thus relevant for the study of beta cell function, screening and validation of new drugs, and development of disease models.

Gravitational Waves from Rapid Structure Formation on Microscopic Scales before Matter-Radiation Equality.

The existence of scalar fields can be probed by observations of stochastic gravitational waves. Scalar fields mediate attractive forces, usually stronger than gravity, on the length scales shorter than their Compton wavelengths, which can be non-negligible in the early Universe, when the horizon size is small. These attractive forces exhibit an instability similar to the gravitational instability, only stronger. They can, therefore, lead to the growth of structures in some species. We identify a gravitational waves signature of such processes and show that it can be detected by future gravitational waves experiments.

Uncertainty of Low‐Degree Space Gravimetry Observations: Surface Processes Versus Earth's Core Signal

AbstractSpace gravity measurements have been mainly used to study the temporal mass variations at the Earth's surface and within the mantle. Nevertheless, mass variations due to the Earth's core might be observable in the gravity field variations as measured by Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On satellites. Earth's core dynamical processes inferred from geomagnetic field measurements are characterized by large‐scale patterns associated with low spherical harmonic degrees of the potential fields. To study these processes, the use of large spatial and inter‐annual temporal filters is needed. To access gravity variations related to the Earth's core, surface effects must be corrected, including hydrological, oceanic or atmospheric loading (Newtonian attraction and mass redistribution). However, these corrections for surface processes add errors to the estimates of the residual gravity field variations enclosing deep Earth's signals. As our goal is to evaluate the possibility to detect signals of core origin embedded in the residual gravity field variations, a quantification of the uncertainty associated with gravity field products and geophysical models used to minimize the surface process signatures is necessary. Here, we estimate the dispersion for GRACE solutions as about 0.34 cm of equivalent water height (EWH) or 20% of the total signal. Uncertainty for hydrological models is as large as 0.89–2.10 cm of EWH. We provide estimates of Earth's core signals whose amplitudes are compared with GRACE gravity field residuals and uncertainties. The results presented here underline how challenging is to get new information about the dynamics of the Earth's core via high‐resolution, high‐accuracy gravity data.

Impact of acute psychosocial stress on attentional control in humans. A study of evoked potentials and pupillary response

Psychosocial stress has increased considerably in our modern lifestyle, affecting global mental health. Deficits in attentional control are cardinal features of stress disorders and pathological anxiety. Studies suggest that changes in the locus coeruleus-norepinephrine system could underlie the effects of stress on top-down attentional control. However, the impact of psychosocial stress on attentional processes and its underlying neural mechanisms are poorly understood. This study aims to investigate the effect of psychosocial stress on attentional processing and brain signatures. Evoked potentials and pupillary activity related to the oddball auditory paradigm were recorded before and after applying the Montreal Imaging Stress Task (MIST). Electrocardiogram (ECG), salivary cortisol, and subjective anxiety/stress levels were measured at different experimental periods. The control group experienced the same physical and cognitive effort but without the psychosocial stress component. The results showed that stressed subjects exhibited decreased P3a and P3b amplitude, pupil phasic response, and correct responses. On the other hand, they displayed an increase in Mismatch Negativity (MMN). N1 amplitude after MIST only decreased in the control group. We found that differences in P3b amplitude between the first and second oddball were significantly correlated with pupillary dilation and salivary cortisol levels. Our results suggest that under social-evaluative threat, basal activity of the coeruleus-norepinephrine system increases, enhancing alertness and decreasing voluntary attentional resources for the cognitive task. These findings contribute to understanding the neurobiological basis of attentional changes in pathologies associated with chronic psychosocial stress.

Surface roughness prediction through GAN-synthesized power signal as a process signature

Predicting machined surface roughness is critical for estimating a part’s performance characteristics such as susceptibility to fatigue and corrosion. Prior studies have indicated that power consumed at the tool-chip interface may represent an indicator for the surface integrity of the machining process. However, no quantitative association has been reported between the machining power and surface roughness due to a lack of data to develop predictive models. This paper presents a data synthesis method to address this gap. Specifically, a conditional generative adversarial network (CGAN) is developed to synthesize power signals associated with varying process parameter combinations. The quality of the synthesized signals is evaluated against experimentally measured power signals by examining the consistency in: 1) the spatial pattern of the signals induced by the cutting process as shown in the frequency domain, and 2) the temporal pattern as shown in the clustering of the synthesized and measured signals corresponding to the same parameter combination. The synthesized signals are then used to augment the measured signals and develop a convolutional neural network (CNN) for predicting the machined surface roughness. Experiments performed using H13 tool steel have shown that data augmentation by CGAN has effectively reduced the error of the surface roughness prediction from 58 %, when no synthetic data is used for CNN training, to 9.1 % when 250 synthetic samples are used. The results demonstrate the effectiveness of CGAN as a data augmentation method and CNN for mapping machining power to surface roughness.

The role of metabolic perturbations in mediating the effects of ambient air pollution on lung cancer in the cancer prevention studies.

10541 Background: Exposure to ambient air pollution is an established risk factor for lung cancer. Investigation on the underlying mechanisms of air pollution carcinogenicity remains challenging due to the complex composition of air pollution and a lack of sensitive biomarkers to accurately measure exposure and corresponding response. To address the knowledge gaps, we applied high-resolution metabolomics to identify metabolic signatures of exogenous air pollution exposures and endogenous processes involved in lung carcinogenesis. Methods: A total of 683 matched lung cancer and control pairs within the established Cancer Prevention Study-II (CPS-II) Nutrition and CPS-3 cohorts were included in this study. The plasma metabolome was profiled with ultrahigh-performance liquid chromatography-tandem mass spectrometry. Assessment of exposure to six ambient air pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10), fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone (O3), was conducted using spatiotemporally-resolved models based on residential address at blood draw and calculated based on yearly average levels. We conducted metabolome-wide association studies with multiple linear regression models to assess associations of ambient air pollution and lung cancer with a meet-in-the-middle approach. Models controlled for potential confounders and covariates including age at blood draw, sex, race, body mass index, alcohol consumption, smoking status, passive smoke exposure, vegetable and fruit intake, and educational level. Metabolites significant at the FDR < 0.2 level in the air pollution model were analyzed in the lung cancer model. High-dimensional mediation analysis was used as a secondary analysis to compare results from the meet-in-the-middle analysis. Results: Among 1,204 metabolic features extracted from the blood samples, seven features were significantly associated with air pollution exposure and lung cancer incidence at the FDR < 0.2 level in the meet-in-the-middle analysis. Six features were significant at the p < 0.01 level through high-dimensional mediation analysis. All confirmed metabolites are enriched within peptide, lipid, and amino acid pathways. Gamma-glutamylglutamine and gamma-glutamylmethionine were statistically significantly associated with CO, NO2, and PM10 exposure and lung cancer incidence in both analyses. Conclusions: This is the largest prospective metabolomics study examining biological perturbations associated with air pollution exposure and lung cancer incidence. Peptide metabolism may play an important role in mediating the association between air pollution and lung cancer. Findings from this study will lay the foundation for future studies to further understand the biological mechanisms underlying the carcinogenicity of air pollution.

HiggsTools: BSM scalar phenomenology with new versions of HiggsBounds and HiggsSignals

The codes HiggsBounds and HiggsSignals compare model predictions of BSM models with extended scalar sectors to searches for additional scalars and to measurements of the detected Higgs boson at 125GeV. We present a unification and extension of the functionalities provided by both codes into the new HiggsTools framework. The codes have been re-written in modern C++ with native Python and Mathematica interfaces for easy interactive use. We discuss the user interface for providing model predictions, now part of the new sub-library HiggsPredictions, which also provides access to many cross sections and branching ratios for reference models such as the SM. HiggsBounds now implements experimental limits purely through json data files, can better handle clusters of BSM particles of similar masses (even for complicated search topologies), and features an improved handling of mass uncertainties. Moreover, it now contains an extended list of Higgs-boson pair production searches and doubly-charged Higgs boson searches. In HiggsSignals, the treatment of different types of measurements has been unified, both in the χ2 computation and in the data file format used to implement experimental results. Program summaryProgram title:HiggsToolsCPC Library link to program files:https://doi.org/10.17632/b25smy28cj.1Developer's repository link:https://gitlab.com/higgsbounds/higgstoolsLicensing provisions: GPLv3Programming language:C++, Python, MathematicaJournal reference of previous version: P. Bechtle, O. Brein, S. Heinemeyer, G. Weiglein, K.E. Williams, Comput. Phys. Commun. 182 (2011), 2605-2631Does the new version supersede the previous version?: YesReasons for the new version: This version extends the functionality of the previous versions and is re-written in modern C++.Summary of revisions: List of included Higgs-boson searches and Higgs-boson rate measurements has been expanded.Nature of problem: Determine whether a parameter point of a given model is excluded or allowed by LEP and LHC Higgs boson search results, and whether this model point is in agreement with the LHC Higgs-boson rate measurements.Solution method: Exclusion by Higgs boson searches: The most sensitive channel from LEP and LHC searches is determined and subsequently applied to test this parameter point for each Higgs boson of the model under consideration. The test requires as input, model predictions for the Higgs boson masses, branching ratios and ratios of production cross sections with respect to reference values. Agreement with LHC Higgs-boson rate measurements: A χ2 value is calculated based on the available LHC rate measurements. This calculation requires as input model predictions for the Higgs boson(s) at ∼125 GeV.Additional comments including restrictions and unusual features: Assumes that the narrow width approximation is applicable in the model under consideration and that the model does not predict a significant change to the signature of the background processes or the kinematical distributions of the signal cross sections.

Modelling novelty detection in the thalamocortical loop.

In complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch negativity (MMN) and on the level of single neurons as stimulus-specific adaptation (SSA). Here, we propose a multi-scale recurrent network with synaptic depression to explain how novelty detection can arise in the whisker-related part of the somatosensory thalamocortical loop. The "minimalistic" architecture and dynamics of the model presume that neurons in cortical layer 6 adapt, via synaptic depression, specifically to a frequently presented stimulus, resulting in reduced population activity in the corresponding cortical column when compared with the population activity evoked by a rare stimulus. This difference in population activity is then projected from the cortex to the thalamus and amplified through the interaction between neurons of the primary and reticular nuclei of the thalamus, resulting in rhythmic oscillations. These differentially activated thalamic oscillations are forwarded to cortical layer 4 as a late secondary response that is specific to rare stimuli that violate a particular stimulus pattern. Model results show a strong analogy between this late single neuron activity and EEG-based mismatch negativity in terms of their common sensitivity to presentation context and timescales of response latency, as observed experimentally. Our results indicate that adaptation in L6 can establish the thalamocortical dynamics that produce signatures of SSA and MMN and suggest a mechanistic model of novelty detection that could generalize to other sensory modalities.

Spectroscopic comparisons of two different terrestrial basaltic environments: Exploring the correlation between nitrogen compounds and biomolecular signatures

Life detection in the solar system relies on the unambiguous identification of signatures of life and habitability. Organic molecules are essential to life as we know it, and yet many organic compounds are ubiquitous in the solar system and can be synthesized abiotically; thus, their presence alone is not indicative of life. On Earth, chemical signatures of life's processes are often left behind in minerals through the biologically induced formation of secondary minerals or intermediary organic complexes. In natural rocks biomolecules and organic species often co-occur with minerals, and their overlapping peaks can create difficulties in interpretation. In the process of identifying the minerals and organic species in our basaltic samples we noticed signatures for cyanates co-occurring with organic molecules. Cyanates are an overlooked group of nitrogen compounds in which C is bonded to N (e.g., OCN− or SCN−) that often co-occur with urea and ammonium in environments where microorganisms are present. These compounds are common in many terrestrial and oceanic environments and play an important role in biogeochemical nitrogen cycling. In natural systems, these compounds form as the result of multiple biogeochemical pathways, often from the interaction of microbes with a chemically active environment. These interactions leave behind signatures in the form biotic breakdown products such as urea or ammonium and organic reaction byproducts that are observable with spectroscopic methods. To explore these relationships, we used field-portable Raman spectrometers and laboratory micro-Raman imaging to characterize and compare samples collected from two different terrestrial basaltic environments, a lava tube on Mauna Loa, Hawaii, dominated by the precipitation of sulfate minerals and a geothermal stream at Hveragil, Iceland dominated by the precipitation of carbonate minerals. The Raman (RS) measurements were complemented by laser induced breakdown spectroscopy (LIBS), Long-wave Infrared (IR) LIBS, with the addition of gas chromatograph mass spectrometry (GC–MS) and inductively coupled plasma-mass spectrometry (ICP-MS) to identify cyanate compounds, biomolecules, and other nitrogenous compounds related to the breakdown or production of cyanate in host basalts and secondary precipitates. The RS data suggest that the reason for RS cyanate signatures in the carbonate samples could be due to luminescence artifacts while those detected in the host basalts may be due to hydrolysis chemistry. The cyanate signatures detected in the lava tube samples dominated by sulfates do not seem to be luminescence artifacts but may in fact be evidence of an active microbial nitrogen cycle. Our results inform the spectroscopic detection of cyanates in planetary analog environments and the challenges in their identification. Further work is needed to understand their potential as biosignatures on other planetary bodies.

Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells

Epidermal homeostasis is governed by a balance between keratinocyte proliferation and differentiation with contributions from cell-cell interactions, but conserved or divergent mechanisms governing this equilibrium across species and how an imbalance contributes to skin disease are largely undefined. To address these questions, human skin single-cell RNA sequencing and spatial transcriptomics data were integrated and compared with mouse skin data. Human skin cell-type annotation was improved using matched spatial transcriptomics data, highlighting the importance of spatial context in cell-type identity, and spatial transcriptomics refined cellular communication inference. In cross-species analyses, we identified a human spinous keratinocyte subpopulation that exhibited proliferative capacity and a heavy metal processing signature, which was absent in mouse and may account for species differences in epidermal thickness. This human subpopulation was expanded in psoriasis and zinc-deficiency dermatitis, attesting to disease relevance and suggesting a paradigm of subpopulation dysfunction as a hallmark of the disease. To assess additional potential subpopulation drivers of skin diseases, we performed cell-of-origin enrichment analysis within genodermatoses, nominating pathogenic cell subpopulations and their communication pathways, which highlighted multiple potential therapeutic targets. This integrated dataset is encompassed in a publicly available web resource to aid mechanistic and translational studies of normal and diseased skin.

Photospheric signatures of retraction and reconnection in realistic magnetohydrodynamic simulations

Context. Magnetic flux emergence and cancelling in the quiet Sun is a frequently observed phenomenon. The two possible physical flux-removal mechanisms involved in this cancelling process are retraction and reconnection. Aims. We seek to find distinct observational signatures characterising retraction and reconnection. Methods. We carried out three-dimensional non-grey radiative magnetohydrodynamic (MHD) simulations of convection near the solar surface and the solar photosphere using the STAGGER code, and employing different initial conditions: (1) mixed-polarity simulations with alternating horizontal stripes of opposite vertical magnetic field and separated by a zero field stripe, and (2) flux emergence simulations with continuous injection of magnetic flux from the lower boundary. These initial conditions are meant to represent two different situations in the solar photosphere, namely magnetic flux cancelling in the absence or presence of magnetic flux emergence, respectively. Results. We analyse the observational signatures of magnetic flux-removal processes for flux emergence as well as for mixed-polarity MHD simulations. In the flux emergence simulation, we are able to identify ubiquitous reconnection events anywhere from the solar surface to the upper photosphere. For a few of those reconnection events, we can identify supersonic upflow velocities in the upper photosphere as well as strong temperature enhancements. We also see strong electric currents very close to the locations where reconnection takes place, as well as supersonic horizontal velocities leading to sideways plasma compression. In the mixed-polarity simulations, we only detect observational signatures of magnetic field retraction related to large downflow velocities that appear in between regions where opposing horizontal velocities converge. These horizontal velocities are often supersonic, leading to heating due to shock dissipation. We do not see clear signatures of magnetic reconnection in these mixed-polarity simulations. Conclusions. We suggest that, in the emerging flux regions of the quiet Sun, the main flux-removal process is reconnection, while in regions without flux emergence, retraction is the dominant flux-removal process.

River thorium concentrations can record bedrock fracture processes including some triggered by distant seismic events

Fractures are integral to the hydrology and geochemistry of watersheds, but our understanding of fracture dynamics is very limited because of the challenge of monitoring the subsurface. Here we provide evidence that long-term, high-frequency measurements of the river concentration of the ultra-trace element thorium (Th) can provide a signature of bedrock fracture processes spanning neighboring watersheds in Colorado. River Th concentrations show abrupt (subdaily) excursions and biexponential decay with approximately 1-day and 1-week time constants, concentration patterns that are distinct from all other solutes except beryllium and arsenic. The patterns are uncorrelated with daily precipitation records or seasonal trends in atmospheric deposition. Groundwater Th analyses are consistent with bedrock release and dilution upon mixing with river water. Most Th excursions have no seismic signatures that are detectable 50 km from the site, suggesting the Th concentrations can reveal aseismic fracture or fault events. We find, however, a weak statistical correlation between Th and seismic motion caused by distant earthquakes, possibly the first chemical signature of dynamic earthquake triggering, a phenomenon previously identified only through geophysical methods.

EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision.

The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni- and cross-modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri-modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross-modal conditional dependencies. We found modality specific signatures of MMN (~100-200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross-modal signature of mismatch processing in the P3a time range (~300-350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross-modal conditional probability, suggesting sensitivity to multimodal (global) predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross-modal conditional dependencies.

Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing

ABSTRACT We developed and applied a novel approach for shape agnostic detection of multiscale flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ sensor data. Flaws in LPBF range from porosity at the micro-scale (< 100 µm), layer related inconsistencies at the meso-scale (100 µm to 1 mm) and geometry-related flaws at the macroscale (> 1 mm). Existing data-driven models are primarily focused on detecting a specific type of LPBF flaw using signals from one type of sensor. Such approaches, which are trained on data from simple cuboid and cylindrical-shaped coupons, have met limited success when used for detecting multiscale flaws in complex LPBF parts. The objective of this work is to develop a heterogenous sensor data fusion approach capable of detecting multiscale flaws across different LPBF part geometries and build conditions. Accordingly, data from an infrared camera, spatter imaging camera, and optical powder bed imaging camera were acquired across separate builds with differing part geometries and orientations (Inconel 718). Spectral graph-based process signatures were extracted from this heterogeneous thermo-optical sensor data and used as inputs to simple machine learning models. The approach detected porosity, layer-level distortion, and geometry-related flaws with statistical fidelity exceeding 93% (F-score).

Coaxial and synchronous monitoring of molten pool height, area, and temperature in laser metal deposition

Laser Metal Deposition is an additive manufacturing technology that enables to realize of one-of-a-kind large components with a free-form geometry. However, the advantages of laser metal deposition from the economic and sustainability points of view can be attenuated by the presence of defects in the realized components. The process should be real-time monitored to immediately identify the onset of defects or process drifts that may undermine the outcome of the build, also enabling the possibility to immediately act against them with feedback control. Many of the proposed solutions in monitoring the Laser Metal Deposition process exploit the use of only one sensor or more but decoupled ones, which does not provide a comprehensive overview of the process. In this work, a coaxial multi-sensor monitoring system is proposed and exploited to capture the variation of three different process signatures. A custom laser triangulator, a near-infrared camera and a ratio pyrometer for simultaneous monitoring of the molten pool height, molten pool area, and molten pool temperature, respectively, are coaxially integrated. Thanks to the possibility to extract concurrent (in spatial and time domains) signals directly from the workpiece area and in real-time, a comprehensive status of the deposition can be deduced. The influence of the process parameters on the sensed outputs is investigated, and the relationships between the process signatures can be researched. While the molten pool area and its temperature are mostly affected by the laser power, the scan speed is more impactful on the molten pool height. A good linear correlation between the molten pool temperature and the area is assessed, while no correlation with the molten pool height is found, making it almost independently manipulatable.

Long-term biogeographical processes dominate patterns of genetic diversity in a wingless grasshopper despite substantial recent habitat fragmentation.

Low-vagility species may hold strong genetic signatures of past biogeographical processes but are also vulnerable to habitat loss. Flightless grasshoppers of the morabine group were once widespread in southeastern Australia, including Tasmania, but are becoming restricted to remnant patches of vegetation, with local ranges impacted by agriculture and development as well as management. Habitat fragmentation can generate genetically differentiated "island" populations with low genetic variation. However, following revegetation, populations could be re-established, and gene flow increased. Here we characterize single nucleotide polymorphism-based genetic variation in a widespread chromosomal race of the morabine Vandiemenella viatica (race 19) to investigate the genetic health of remnant populations and to provide guidelines for restoration efforts. We update the distribution of this race to new sites in Victoria and Tasmania, and show that V. viatica populations from northern Tasmania and eastern Victoria have reduced genetic variation compared to other mainland populations. In contrast, there was no effect of habitat fragment size on genetic variation. Tasmanian V. viatica populations fell into two groups, one connected genetically to eastern Victoria and the other connected to southwestern Victoria. Mainland populations showed isolation by distance. These patterns are consistent with expectations from past biogeographical processes rather than local recent population fragmentation and emphasize the importance of small local reserves in preserving genetic variation. The study highlights how genomic analyses can combine information on genetic variability and population structure to identify biogeographical patterns within a species, which in turn can inform decisions on potential source populations for translocations.

Individual neurophysiological signatures of spontaneous rhythm processing

When sensory input conveys rhythmic regularity, we can form predictions about the timing of upcoming events. Although rhythm processing capacities differ considerably between individuals, these differences are often obscured by participant- and trial-level data averaging procedures in M/EEG research. Here, we systematically assessed neurophysiological variability displayed by individuals listening to isochronous (1.54 Hz) equitone sequences interspersed with unexpected (amplitude-attenuated) deviant tones. Our approach aimed at revealing time-varying adaptive neural mechanisms for sampling the acoustic environment at multiple timescales. Rhythm tracking analyses confirmed that individuals encode temporal regularities and form temporal expectations, as indicated in delta-band (1.54 Hz) power and its anticipatory phase alignment to expected tone onsets. Zooming into tone- and participant-level data, we further characterized intra- and inter-individual variabilities in phase-alignment across auditory sequences. Further, individual modeling of beta-band tone-locked responses showed that a subset of auditory sequences was sampled rhythmically by superimposing binary (strong-weak; S-w), ternary (S-w-w) and mixed accentuation patterns. In these sequences, neural responses to standard and deviant tones were modulated by a binary accentuation pattern, thus pointing towards a mechanism of dynamic attending. Altogether, the current results point toward complementary roles of delta- and beta-band activity in rhythm processing and further highlight diverse and adaptive mechanisms to track and sample the acoustic environment at multiple timescales, even in the absence of task-specific instructions.

X-ray luminosity function of high-mass X-ray binaries: Studying the signatures of different physical processes using detailed binary evolution calculations

Context. Many physical processes taking place during the evolution of binary stellar systems remain poorly understood. The ever-expanding observational sample of X-ray binaries (XRBs) makes them excellent laboratories for constraining binary evolution theory. Such constraints and useful insights can be obtained by studying the effects of various physical assumptions on synthetic X-ray luminosity functions (XLFs) and comparing them with observed XLFs. Aims. In this work we focus on high-mass X-ray binaries (HMXBs) and study the effects on the XLF of various, poorly constrained assumptions regarding physical processes, such as the common-envelope phase, core collapse, and wind-fed accretion. Methods. We used the new binary population synthesis code POSYDON, which employs extensive precomputed grids of detailed stellar structure and binary evolution models, to simulate the entire evolution of binaries. We generated 96 synthetic XRB populations corresponding to different combinations of model assumptions, including different prescriptions for supernova kicks, supernova remnant masses, common-envelope evolution, circularization at the onset of Roche-lobe overflow, and observable wind-fed accretion. Results. The generated HMXB XLFs are feature-rich, deviating from the commonly assumed single power law. We find a break in our synthetic XLF at luminosity ∼1038 erg s−1, similar to observed XLFs. However, we also find a general overabundance of XRBs (up to a factor of ∼10 for certain model parameter combinations) driven primarily by XRBs with black hole accretors. Assumptions about the transient behavior of Be XRBs, asymmetric supernova kicks, and common-envelope physics can significantly affect the shape and normalization of our synthetic XLFs. We find that less well-studied assumptions regarding the circularization of the orbit at the onset of Roche-lobe overflow and criteria for the formation of an X-ray-emitting accretion disk around wind-accreting black holes can also impact our synthetic XLFs and reduce the discrepancy with observations. Conclusions. Our synthetic XLFs do not always agree well with observations, especially at intermediate X-ray luminosities, which is likely due to uncertainties in the adopted physical assumptions. While some model parameters leave distinct imprints on the shape of the synthetic XLFs and can reduce this deviation, others do not have a significant effect overall. Our study reveals the importance of large-scale parameter studies, highlighting the power of XRBs in constraining binary evolution theory.