Slope Of Function Research Articles

Does Social Support Moderate the Relationship Between Frailty and Functional Ability Trajectory Among Community-Dwelling Older Adults?

Functional ability is the important prerequisite to live independently and achieve aging in place, which depends on the complex interaction of intrinsic and extrinsic factors. Identifying the trends and influencing factors of functional ability would contribute to the accurate assessment and intervention of geriatric health. This study aimed to disentangle the moderating effect of 3 types of social support, namely objective support, subjective support, and support utilization, on the relationship between frailty and functional ability trajectories. This was a secondary analysis using data from a prospective 3-wave study with a sample of 777 Chinese community-dwelling older adults. Social support was assessed using the Social Support Rating scale. Frailty was assessed using the FRAIL scale. Functional ability was measured by the Lawton Instrumental Activities of Daily Living scale. Latent growth curve models were implemented to test their relationships. Objective support but not subjective support or support utilization moderated on the relationship between frailty and functional ability slope. Functional ability decline over time was buffered by objective support among robust individuals but exacerbated among (pre)frail individuals. The moderating effect of social support on the relationship between frailty and functional ability trajectory varies by support types, which reminded that social support may be a promising intervention target to maintain functional independence for frail individuals, opening up a new perspective on social support in the field of disability prevention. Effective interventions should particularly address objective support in conjunction with empowering the frail older population to optimize the trajectory of functional ability.

INSPIRE: INvestigating Stellar Population In RElics – VI. The low-mass end slope of the stellar initial mass function and chemical composition

ABSTRACT The INSPIRE project has built the largest sample of ultra-compact massive galaxies (UCMGs) at 0.1 < z < 0.4 and obtained their star formation histories (SFHs). Due to their preserved very old stellar populations, relics are the perfect systems to constrain the earliest epochs of mass assembly in the Universe and the formation of massive early-type galaxies. The goal of this work is to investigate whether a correlation exists between the degree of relicness (DoR), quantifying the fraction of stellar mass formed at z > 2, and the other stellar population parameters. We use the Full-Index-Fitting method to fit the INSPIRE spectra to single stellar population (SSP) models. This allows us to measure, for the first time, the slope of the IMF, as well as stellar metallicity [M/H], [Mg/Fe], [Ti/Fe], and [Na/Fe] ratios, and study correlations between them and the DoR. Similarly to normal-sized galaxies, UCMGs with larger stellar masses have overall higher metallicities. We found a correlation between the IMF slope and the DoR, that, however, breaks down for systems with a more extended SFH. An even stronger dependency is found between the IMF and the fraction of mass formed at high-z. At equal velocity dispersion and metallicity, galaxies with a higher DoR have a larger dwarf-to-giant ratio, i.e. a bottom heavy IMF, than that of low-DoR counterparts. This might indicate that the cosmic epoch and therefore different formation scenarios influence the fragmentation of the star formation cloud and hence might be the explanation for IMF variations detected in massive ETGs.

Brain reserve in midlife is associated with executive function changes across 12 years

We examined how brain reserve in midlife, measured by brain-predicted age difference scores (Brain-PADs), predicted executive function concurrently and longitudinally into early old age, and whether these associations were moderated by young adult cognitive reserve or APOE genotype. 508 men in the Vietnam Era Twin Study of Aging (VETSA) completed neuroimaging assessments at mean age 56 and six executive function tasks at mean ages 56, 62, and 68 years. Results indicated that greater brain reserve at age 56 was associated with better concurrent executive function (r=.10, p=.040) and less decline in executive function over 12 years (r=.34, p=.001). These associations were not moderated by cognitive reserve or APOE genotype. Twin analysis suggested associations with executive function slopes were driven by genetic influences. Our findings suggest that greater brain reserve allowed for better cognitive maintenance from middle- to old age, driven by a genetic association. The results are consistent with differential preservation of executive function based on brain reserve that is independent of young adult cognitive reserve or APOE genotype.

Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors

In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019–2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model—with random slopes and intercepts by year—, we observed potential "functional changes" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the pol gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases. Significance of the studyThis study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune responses, opening avenues for further research into adaptive changes and responses to environmental shifts in the context of HIV infection. The study's emphasis on molecular characteristics as predictors of pVL adds valuable insights to epidemiological and evolutionary studies of viruses, providing new avenues for understanding and managing HIV infection at the population level.

Landscape influences on thermal sensitivity and predicted spatial variability among brook trout streams in the southeastern USA

AbstractWarming water temperatures as a result of climate change pose a major threat to coldwater organisms. However, the rate of warming is not spatially uniform due to surface‐ground‐water interactions and stream and watershed characteristics. Coldwater habitats that are most resistant to warming serve as thermal refugia and identifying their locations is critical to regional aquatic conservation planning. We quantified the thermal sensitivity of 203 streams providing current and potential habitat for brook trout (Salvelinus fontinalis) across nearly 1000 linear km of their native range in the southern and central Appalachian Mountains region, USA, and characterized their spatial variability with landscape variables available in the National Hydrography Dataset. Using the Bayesian framework, we calculated the maximum slope of the logistic function relating paired weekly mean air temperature and stream temperature as an index of stream thermal sensitivity. Streams differed greatly in thermal sensitivity and those with more resistant water temperature regimes (i.e., thermal refugia) were consistently characterized by southerly latitudes and groundwater input. Landscape variables derived from a principal component analysis explained 16% of the variation in thermal sensitivity, indicating that the existing landscape variables were modestly successful in explaining spatial thermal heterogeneity. Using our model and spatial interpolation, we predicted thermal sensitivity at 8695 stream segments potentially suitable for brook trout in the study region. Thermal refugia were more common southward presumably due to higher elevations, but elsewhere they were also clustered at finer spatial scales. Our analysis informs prioritizing habitat conservation and restoration of this native salmonid and other aquatic organisms that depend on coldwater habitats in a warming world.

Transcranial Alternating Current Stimulation over Frontal Eye Fields Mimics Attentional Modulation of Visual Processing.

Attentional control over sensory processing has been linked to neural alpha oscillations and related inhibition of cerebral cortex. Despite the wide consensus on the functional relevance of alpha oscillations for attention, precise neural mechanisms of how alpha oscillations shape perception and how this top-down modulation is implemented in cortical networks remain unclear. Here, we tested the hypothesis that alpha oscillations in frontal eye fields (FEFs) are causally involved in the top-down regulation of visual processing in humans (male and female). We applied sham-controlled, intermittent transcranial alternating current stimulation (tACS) over bilateral FEF at either 10 Hz (alpha) or 40 Hz (gamma) to manipulate attentional preparation in a visual discrimination task. Under each stimulation condition, we measured psychometric functions for contrast perception and introduced a novel linear mixed modeling approach for statistical control of neurosensory side effects of the electric stimulation. tACS at alpha frequency reduced the slope of the psychometric function, resulting in improved subthreshold and impaired superthreshold contrast perception. Side effects on the psychometric functions were complex and showed large interindividual variability. Controlling for the impact of side effects on the psychometric parameters by using covariates in the linear mixed model analysis reduced this variability and strengthened the perceptual effect. We propose that alpha tACS over FEF mimicked a state of endogenous attention by strengthening a fronto-occipitoparietal network in the alpha band. We speculate that this network modulation enhanced phasic gating in occipitoparietal cortex leading to increased variability of single-trial psychometric thresholds, measurable as a reduction of psychometric slope.

Temporal multiscale modeling of biochemical regulatory networks: Calcium-regulated hepatocyte lipid and glucose metabolism

Hepatocyte lipid and glucose metabolism is regulated not only by major hormones like insulin and glucagon but also by many other factors, including calcium ions. Recently, mitochondria-associated membrane (MAM) dysfunction combined with incorrect IP3-receptor regulation has been shown to result in abnormal calcium signaling in hepatocytes. This dysfunction could further lead to hepatic metabolism pathology. However, the exact contribution of MAM dysfunction, incorrect IP3-receptor regulation and insulin resistance to the calcium-insulin-glucagon interplay is not understood yet. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network with a detailed focus on the model construction details besides the biological aspect. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network. We focus on the model construction details, model validation, and predictions. We describe the dynamic regulation of signaling processes by sigmoid Hill function. In particular, we study the effect of both the Hill function slope and the distance between Hill function extremes on metabolic processes in hepatocytes as a model of nonspecific insulin dysfunction. We also address the significant time difference between characteristic time of glucose hepatic processing and a typical calcium oscillation period in hepatocytes. Our modeling results show that calcium signaling dysfunction results in an abnormal increase in postprandial glucose levels, an abnormal glucose decrease in fasting, and a decreased amount of stored glycogen. An insulin dysfunction of glucose phosphorylation, glucose dephosphorylation, and glycogen breakdown also cause a noticeable effect. We also get some insight into the so-called hepatic insulin resistance paradox, confirming the hypothesis regarding indirect insulin action on hepatocytes via dysfunctional adipocyte lipolysis.

Highly-mass-loaded hot galactic winds are unstable to cool filament formation

Abstract As cool clouds are entrained by a hot supersonic galactic wind, they may be shredded by hydrodynamical instabilities and incorporated into the hot flow. One-dimensional steady-state calculations show how cool cloud entrainment affects the bulk thermodynamics and kinematics of the hot gas: mass-loading decelerates the hot flow and changes its entropy. Here, we investigate the stability of mass-loaded hot winds using both perturbation analysis and 3D time-dependent radiative hydrodynamical simulations. We show that mass-loading is stable over a broad range of parameters and that the 1D time-steady analytic solutions exactly reproduce the 3D time-dependent calculations, provided that the flow does not decelerate sufficiently to become subsonic. For higher values of the mass-loading, the flow develops a second sonic point, with the first being at the edge of the wind-driving region. Strong deceleration increases the wind density and the flow becomes radiative, undergoing a thermal instability to form elongated dense cometary filaments. We explore the mass-loading parameters required to trigger this behavior. For certain approximations, we can derive analytic criteria. In general a mass-loading rate similar to the initial hot mass outflow rate is required. In this sense, the destruction of small cool clouds by a hot flow may ultimately spontaneously generate fast cool filaments, as observed in starburst winds. Lastly, we find that the kinematics of filaments is sensitive to the slope of the mass-loading function. Filaments move faster than the surrounding wind if mass-loading is over long distances whereas filaments move slower than their surroundings if mass-loading is abrupt.

A Consistent Explanation for the Unusual Initial Mass Function and Star Formation Rate in the Central Molecular Zone (CMZ)

We examine various physical processes that may explain the shallow high-mass slope of the initial mass function (IMF), as well as the low star formation rate (SFR) in star-forming molecular clouds (MCs) in the Central Molecular Zone (CMZ). We show that the strong tidal field and shear experienced by the CMZ have opposite effects on the collapse of density fluctuations and cannot explain these properties. Similarly, we show that the intense magnetic field in the CMZ provides a negligible pressure support and, for the high densities at play, should not modify the probability density function of the turbulent gas flow, thus affecting negligibly the IMF. However, we show that, in contrast to the MCs in the Galactic disk, the ones in the CMZ experience only one single episode of turbulence cascade. Indeed, their rather short lifetime, due to their high mean densities, is similar to one typical turbulence crossing time. Consequently, according to the Hennebelle–Chabrier theory of star formation, within this “single turbulence cascade episode,” the cloud experiences one single field of turbulence-induced density fluctuations, leading eventually to gravitationally unstable cores. As shown in Hennebelle & Chabrier (2013), this yields a shallower IMF than usual and leads to the correct observed slope for the CMZ star-forming clouds. Similarly, this single large-scale turbulence event within the cloud lifetime yields a 5–6 times lower SFR than under usual conditions, in agreement with the observed values. Therefore, we suggest that this “single turbulence cascade” scenario can explain both the shallow IMF and low SFR of clouds in the CMZ.

Exploring NGC 2345: A Comprehensive Study of a Young Open Cluster through Photometric and Kinematic Analysis

We conducted a photometric and kinematic analysis of the young open cluster NGC 2345 using CCD UBV data from 2 m Himalayan Chandra Telescope, Gaia Data Release 3, Two Micron All-Sky Survey, and the Photometric All-Sky Survey data sets. We found 1732 most probable cluster members with membership probability higher than 70%. The fundamental and structural parameters of the cluster are determined based on the cluster members. The mean proper motion of the cluster is estimated to be μαcosδ = −1.34 ± 0.20 and μ δ = 1.35 ± 0.21 mas yr−1. Based on the radial density profile, the estimated radius is ∼12.′8 (10.37 pc). Using color–color and color–magnitude diagrams, we estimate the reddening, age, and distance to be 0.63 ± 0.04 mag, 63 ± 8 Myr, and 2.78 ± 0.78 kpc, respectively. The mass function slope for main-sequence stars is determined as 1.2 ± 0.1. The mass function slope in the core, halo, and overall region indicates a possible hint of mass segregation. The cluster’s dynamical relaxation time is 177.6 Myr, meaning ongoing mass segregation, with complete equilibrium expected in 100–110 Myr. Apex coordinates are determined as −40.°89 ± 0.12, − 44.°99 ± 0.15. The cluster’s orbit in the Galaxy suggests early dissociation into field stars due to its close proximity to the Galactic disk.

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.

Do US states’ responses to COVID-19 restore investor sentiment? Evidence from S&P 500 financial institutions

This paper specifically investigates the effects of US government emergency actions on the investor sentiment–financial institution stock returns relationship. Despite attempts by many studies, the literature still provides no answers concerning this nexus. Using a new firm-specific Twitter investor sentiment (TS) metric and performing a panel smooth transition regression for daily data on 66 S&P 500 financial institutions from January 1 to December 31, 2020, we find that TS acts asymmetrically, nonlinearly, and time varyingly according to the pandemic situation and US states’ responses to COVID-19. In other words, we uncover the nexus between TS and financial institution stock returns and determine that it changes with US states’ reactions to COVID-19. With a permissive government response (the first regime), TS does not impact financial institution stock returns; however, when moving to a strict government response (the overall government response index exceeds the 63.59 threshold), this positive effect becomes significant in the second regime. Moreover, the results show that the slope of the transition function is high, indicating an abrupt rather than a smooth transition between the first and second regimes. The results are robust and have important policy implications for policymakers, investment analysts, and portfolio managers.

Effects of quality of initial exposure stimuli on speech intelligibility

Listeners can use lexical information to drive adaptation to talker-specific speech characteristics with a small amount of exposure. This suggests that adaptation may be strongest when listeners are initially exposed to speech in a condition that facilitates word recognition. This was tested by examining the intelligibility of second-language (L2) speech-in-noise when stimuli were initially presented in Quiet versus low signal-to-noise ratio (SNR) conditions. Recordings of 10 L2 English talkers were tested. For each talker, 120 sentences were mixed with speech-shaped noise ranging from −4 dB to 8 dB in steps of 2 dB and in Quiet (15 sentences at each SNR). These stimuli were presented from lowest to highest SNR (forward; −4 dB to Quiet) in one group of L1 English listeners (n = 10/talker) and from highest to lowest SNR (reverse; Quiet to −4 dB) in the other (n = 10/talker). The results showed that early exposure to a clean signal led to better speech perception. At lower SNRs, word recognition accuracy in the reverse order was significantly higher than the forward order, resulting in a steeper slope of psychometric function of intelligibility in the forward versus reverse condition. The quality of initial speech input may influence talker adaptation, particularly in adverse listening conditions.

Cognitive and developmental contributions to psychometric functions on three auditory tasks during adolescence and young adulthood

Auditory perceptual tasks mature at different rates, with temporal tasks having longer developmental courses. We tested 10- to 23-year-olds on frequency discrimination, temporal-interval discrimination, and gap detection using the method of constant stimuli. During task performance, listening effort data were collected via eye-tracker. A cognitive battery was also administered. Thresholds correlated between the frequency and temporal-interval discrimination conditions, which shared the same standard stimulus, but not between other conditions. The slopes of the psychometric functions did not correlate between conditions, suggesting that different or time-varying factors might contribute to slopes on these conditions. Shallower psychometric functions corresponded to poorer thresholds on only the temporal-interval and gap conditions, implying a potential role for attention, engagement, fatigue, or the stability of the stimulus representations on those conditions. Regression modeling indicated a limited role of age: Age predicted thresholds on the temporal-interval discrimination condition and slopes on both the frequency and temporal-interval discrimination conditions. Testing order affected performance on frequency discrimination (threshold) and temporal-interval discrimination (threshold and slope). The measures of listening effort and cognition that predicted thresholds and slopes also differed between those two conditions. On gap detection, neither threshold nor slope was predicted by any variable. [Work funded by NIDCD.]

Response of mineral particles in inland lakes to water optical properties and its influence on chlorophyll-a estimation.

Many chlorophyll-a (Chl-a) remote sensing estimation algorithms have been developed for inland water, and they are proposed always based on some ideal assumptions, which are difficult to meet in complex inland waters. Based on MIE scattering theory, this study calculated the optical properties of mineral particles under different size distribution and refractive index conditions, and the Hydrolight software was employed to simulate remote sensing reflectance in the presence of different mineral particles. The findings indicated that the reflectance is significantly influenced by the slope (j) of particle size distribution function and the imaginary part (n') of the refractive index, with the real part (n) having a comparatively minor impact. Through both a simulated dataset containing 18,000 entries and an in situ measured dataset encompassing 2183 data from hundreds of lakes worldwide, the sensitivities of band ratio (BR), fluorescence baseline height (FLH), and three-band algorithms (TBA) to mineral particles were explored. It can be found that BR showed the best tolerance to mineral particles, followed by TBA. However, when the ISM concentration is less than 30 g m-3, the influence of CDOM cannot be ignored. Additionally, a dataset of over 400 entries is necessary for developing the BR algorithm to mitigate the incidental errors arising from differences in data magnitude. And if the amount of developing datasets is less than 400 but greater than 200, the TBA algorithm is more likely to obtain more stable accuracy.

The effects of variability on context effects and psychometric function slopes in speaking rate normalizationa).

Acoustic context influences speech perception, but contextual variability restricts this influence. Assgari and Stilp [J. Acoust. Soc. Am. 138, 3023-3032 (2015)] demonstrated that when categorizing vowels, variability in who spoke the preceding context sentence on each trial but not the sentence contents diminished the resulting spectral contrast effects (perceptual shifts in categorization stemming from spectral differences between sounds). Yet, how such contextual variability affects temporal contrast effects (TCEs) (also known as speaking rate normalization; categorization shifts stemming from temporal differences) is unknown. Here, stimuli were the same context sentences and conditions (one talker saying one sentence, one talker saying 200 sentences, 200 talkers saying 200 sentences) used in Assgari and Stilp [J. Acoust. Soc. Am. 138, 3023-3032 (2015)], but set to fast or slow speaking rates to encourage perception of target words as "tier" or "deer," respectively. In Experiment 1, sentence variability and talker variability each diminished TCE magnitudes; talker variability also produced shallower psychometric function slopes. In Experiment 2, when speaking rates were matched across the 200-sentences conditions, neither TCE magnitudes nor slopes differed across conditions. In Experiment 3, matching slow and fast rates across all conditions failed to produce equal TCEs and slopes everywhere. Results suggest a complex interplay between acoustic, talker, and sentence variability in shaping TCEs in speech perception.

Listeners' convergence towards an artificial agent in a joint phoneme categorization task

This study focuses on inter-individual convergence effects in the perception and categorization of speech sounds. We ask to what extent two listeners can come to establish a shared set of categorization criteria in a phoneme identification task that they accomplish together. Several hypotheses are laid out in the framework of a Bayesian model of speech perception that we have developed to account for how two listeners may each infer the parameters that govern their partner’s responses. In our experimental paradigm, participants were asked to perform a joint phoneme identification task with a partner that, unbeknownst to them, was an artificial agent, whose responses we manipulated along two dimensions, the location of the categorical boundary and the slope of the identification function. Convergence was found to arise for bias but not for slope. Numerical simulations suggested that lack of convergence in slope may stem from the listeners’ prior level of confidence in the variance in VOT for the two phonemic categories. This study sheds new light on perceptual convergence between listeners in the categorization of speech sounds, a phenomenon that has received little attention so far in spite of its central importance for speech communication.

A statistical and multiwavelength photometric analysis of a young embedded open star cluster: IC 1590

ABSTRACT We present a statistical and multiwavelength photometric studies of young open cluster IC 1590. We identified 91 cluster members using Gaia DR3 astrometry data using ensemble-based unsupervised machine learning techniques. From Gaia EDR3 data, we estimate the best-fitting parameters for IC 1590 using the Automated Stellar Cluster Analysis package (asteca) yielding the distance d ∼ 2.87 ± 0.02 kpc, age ∼ 3.54 ± 0.05 Myr, metallicity z ∼ 0.0212 ± 0.003, binarity value of ∼ 0.558, and extinction Av ∼ 1.252 ± 0.4 mag for an Rv value of ∼ 3.322 ± 0.23. We estimate the initial mass function slope of the cluster to be α = 1.081 ± 0.112 for single stars and α = 1.490 ± 0.051 for a binary fraction of ∼ 0.558 in the mass range 1 M⊙ ≤ m (M⊙) ≤ 100 M⊙. The G-band luminosity function slope is estimated to be ∼ 0.33 ± 0.09. We use (J − H) versus (H − Ks) colour–colour diagram to identify young stellar objects (YSOs). We found that all the identified YSOs have ages ≤ 2 Myr and masses ∼ 0.35 – 5.5 M⊙. We also fit the radial surface density profile. Using the galpy, we performed orbit analysis of the cluster. The extinction map for the cluster region has been generated using the PNICER technique, and it is almost similar to the dust structure obtained from the 500 μm dust continuum emissions map of Herschel SPIRE. We finally at the end discussed the star formation scenario in the cluster region.

GIS Interpolation and Mapping of Soil Physicochemical Properties in Deep Medium Black Soils of Established Citrus Orchards

Soil properties are an important factor for orchard establishment, precise nutrient management and sustainable production of fruit crops. Therefore, it is important to assess the spatial distribution of fundamental soil properties in well-established orchards. Hence an attempt has been made to assess the extent of soil properties and its spatial distribution in citrus orchards in medium black soils of Madhya Pradesh. The present study was conducted for the assessment of the spatial distribution of physicochemical properties viz. pH, electrical conductivity (EC) and soil organic carbon (SOC) of citrus orchards in medium-deep black soils of India. Results revealed that soil pH ranged from 6.83-8.84 (mean 7.80), soil EC varied from 0.07-0.34dS m-1 (mean 0.18 dS m-1) and soil organic carbon ranged from 0.13-0.89% (mean 0.47%) in 0-20 cm of surface soil layer. Geostatistical analysis showed that the slope of the prediction function of best-fit model (exponential) for soil pH, EC and SOC was 0.31, 0.22 and 0.77, respectively. The corresponding values of root mean square error (RMSE) were 0.35, 0.03, and 0.14. Interpolation of soil properties indicated that 89.2 % area had soil pH between 7.20 to 8.00, 83.4 % area had soil EC between 0.10 to 0.20 dS m-1, while>90 % area had SOC content ranged from 0.25 to 0.75%. Geo-statistical analysis revealed that spatial dependency was moderate for pH and strong spatial dependency was estimated for EC and SOC content. Based on RMSE and slope of prediction function, an exponential model was best-fit model in ordinary kriging for interpolation of measured soil properties.