Temporal Response Patterns Research Articles

Disabled people, arts festivals and emotional timescapes: An exploratory case study

ABSTRACT This exploratory study contributes to the leisure studies literature in three main ways: through foregrounding the emotionality of intellectually disabled people, an under-represented group within the context of events; through focusing on an arts festival, a form of event overlooked in comparison to other commonly studied types of events; and finally, through the testing of a new methodological framework – Brown’s emotional timescapes for events, itself adapted from Maguire and Geiger’s work on servicescapes. In this New Zealand-based study, we explore the emotions experienced by attendees, performers, volunteers, support workers and event organisers. Data from 20 interviews, participant observation and autoethnography was deductively analysed with a focus on expressions of emotion. We crafted a narrative for each individual, allowing us to identify the influence of the eventscape and map temporal patterns of emotional responses across the festival timescape. Findings show that participants experienced strong positive emotions. This was influenced by elements of the eventscape. In particular, interaction with others (including audience reaction) played a significant role in emotional timescapes. While the emotional timescape model was useful for broadly representing intensity of emotion over the duration of an event, we also identified limitations and propose a way to expand Brown’s theory.

An implemented predictive coding model of lexico-semantic processing explains the dynamics of univariate and multivariate activity within the left ventromedial temporal lobe during reading comprehension.

During language comprehension, the larger neural response to unexpected versus expected inputs is often taken as evidence for predictive coding-a specific computational architecture and optimization algorithm proposed to approximate probabilistic inference in the brain. However, other predictive processing frameworks can also account for this effect, leaving the unique claims of predictive coding untested. In this study, we used MEG to examine both univariate and multivariate neural activity in response to expected and unexpected inputs during word-by-word reading comprehension. We further simulated this activity using an implemented predictive coding model that infers the meaning of words from their orthographic form. Consistent with previous findings, the univariate analysis showed that, between 300-500ms, unexpected words produced a larger evoked response than expected words within a left ventromedial temporal region that supports the mapping of orthographic word-forms onto lexical and conceptual representations. Our model explained this larger evoked response as the enhanced lexico-semantic prediction error produced when prior top-down predictions failed to suppress activity within lexical and semantic "error units". Critically, our simulations showed that, despite producing minimal prediction error, expected inputs nonetheless reinstated top-down predictions within the model's lexical and semantic "state" units. Two types of multivariate analyses provided evidence for this functional distinction between state and error units within the ventromedial temporal region. First, within each trial, individual voxels within this region produced unique temporal patterns between 300-500ms that that resembled the temporal patterns produced within a pre-activation time-window before the expected input became available. First, within each trial, the same left ventromedial voxels that produced a larger response between 300-500ms to unexpected inputs, reinstated unique pre-activated item-specific temporal patterns in response to expected inputs within this same time window. Second, across trials, and again within the same 300-500ms time window and left ventromedial temporal region, pairs of expected words produced spatial patterns that were more similar to one another than the spatial patterns produced by pairs of expected and unexpected words, regardless of specific item. Together, these findings provide compelling evidence that the left ventromedial temporal lobe employs predictive coding to infer the meaning of incoming words from their orthographic form during reading comprehension.

Spatial and temporal distribution patterns of ants (Hymenoptera: Formicidae) in Gobi Desert ecosystems, Northwest China

Ants (Hymenoptera: Formicidae) represent the most critical arthropod community in desert ecosystems, where interactions among vegetation, soil, and climate dictate ant assemblages. Nonetheless, our understanding of how various factors influence desert ant assemblages across different spatial and temporal scales remains limited. Therefore, this study aims to analyse the temporal and spatial distribution patterns of desert ants and to determine the effects of precipitation and temperature variations on ant assemblages. To achieve this, we continuously monitored the monthly dynamics of ants at 72 uniform 8m×8m grid points in the Gobi Desert ecosystem of Northwest China from 2015 to 2020 using pitfall traps. The results indicated that Messor desertus and Cataglyphis aenescens were the dominant ant species, with significant annual and monthly variations in the number of individuals captured from 2015 to 2020. In 2020, monthly captures of M. desertus exhibited a bimodal pattern, peaking in November, whereas those of C. aenescens exhibited a unimodal pattern, peaking in June. Annual data revealed that population size was significantly positively correlated spatially at a distance of 24 m. Semi-variance analysis and Moran's I indicated that structural factors predominantly controlled the ant assemblages at a small scale from 2015 to 2020. Annual catches of desert ants tended to decrease with increasing annual precipitation, while an opposite trend was rising average annual temperatures. In conclusion, variations in annual and monthly precipitation and temperature influenced the temporal response patterns of desert ants, thereby altering their spatial assemblages.

Unraveling the impact of dog‐friendly spaces on urban–wildland pumas and other wildlife

As the most widespread large carnivore on the planet, domestic dogs Canis lupus familiaris can pose a major threat to wildlife, even within protected areas (PAs). Growing human presence in PAs, coupled with increasing pet dog ownership underscores the urgency to understand the influence of dogs on wildlife activity and health. This knowledge can mitigate the adverse repercussions of recreation, optimizing PA management. Drawing on five years (2017–2021) of data from 101 camera traps in the San Francisco Bay Area, California, United States, we measured the spatiotemporal responses of puma Puma concolor, bobcat Lynx rufus, coyote Canis latrans, and mule deer Odocoileus hemionus towards domestic dogs. Additionally, using six years (2017–2022) of community science data, we explored the impacts of PA dog policies on puma sightings outside park boundaries. Puma responses provide insights into broader ecological impacts, while analyses of bobcat, coyote, and mule deer offer a comprehensive understanding of species responses to dog‐friendly spaces. Because dogs can be perceived as predators or competitors by wildlife, we anticipated shifts in spatial and temporal activity patterns in response to dogs. Wildlife responses included avoidance (bobcat, puma) or spatial overlap (mule deer) for areas with more dogs, and no effect (coyote). Mule deer may benefit from a “human shield” provided by people with dogs, while pumas and bobcats appeared more sensitive, and coyotes more adaptable. Dog policies influenced puma and mule deer temporal activity, with increased nocturnal activity in dog‐friendly PAs. Bobcat temporal activity was less variable in dog‐friendly PAs and coyote activity was similar between treatments. Outside PAs, puma sightings increased with human disturbance. Our study underscores the trade‐offs between recreation and wildlife conservation, emphasizing the need to quantify the ecological impacts of dogs. This understanding is vital for informing conservation strategies and promoting coexistence between dogs, wildlife, and protected environments.

Drought-tolerant peanut (Arachis hypogaea L.) varieties can mitigate negative impacts of climate change on yield in the Southeastern U.S.

Rising temperatures and varying precipitation patterns in future climates pose huge challenges to the peanut (Arachis hypogaea L.) production in Southeastern U.S. (SEUS), which was characterized by hot summers and mild winters. This will require genetic improvement of peanut varieties for adapting to future climates for higher yields. Recently, several novel peanut varieties were found to exhibit higher yield and maintain photosynthesis during drought. Thus, this study aimed to investigate the spatial and temporal patterns of peanut yield response to future climate changes for peanut varieties with and without the drought tolerant trait of maintaining photosynthesis (MPh) under drought in the SEUS and evaluate the relative importance of yield related traits for enhancing yield advantage of peanut under future climate conditions. A modified version of the CROPGRO-Peanut model incorporating the drought tolerant factor was developed and validated using field trail data collected from distinct experimental sites under both rainfed and irrigated conditions. Yield was then simulated for the historical baseline period (1990–2021) and future (mid-century/2050s, late-century/2080s) climate scenarios across the SEUS. The results showed that peanut yield in the Northern SEUS was expected to benefit from climate change in the 2050s (RCP4.5 and RCP8.5), but Middle SEUS and Southern SEUS would experience high probability of yield loss in the future. Comparing to the reference variety (C4), the drought tolerant peanut varieties (C1, C2 and C3) with the trait of MPh under drought could enhance the yield gains due to future climates in the 2050s (RCP4.5 and RCP8.5) in Northern SEUS (yield increases from 5% to 10% for C1-C3, and 2% to 3% for C4) and mitigate the negative impacts of climate change in the 2080s (RCP4.5 and RCP8.5) in the Middle (yield changes from −26% to −1% for C1-C3, and −37% to −11% for C4) and Southern SEUS (yield changes from −24% to 1% for C1-C3, and −35% to −7% for C4). The yield advantage of the drought tolerant peanut varieties in future climates was strongly associated with the higher water use efficiency (WUE), harvest index (HI), seed size, seed number, and biomass with significant P values (p < 0.001). These findings are important for local breeding and management programs to mitigate the potential impacts of climate change on peanut yield in SEUS.

Midbrain sensitivity to auditory motion studied with dichotic sweeps of broadband noise

Many neurons in the central nucleus of the inferior colliculus (IC) show sensitivity to interaural time differences (ITDs), which is thought to be relayed from the brainstem. However, studies with interaural phase modulation of pure tones showed that IC neurons have a sensitivity to changes in ITD that is not present at the level of the brainstem. This sensitivity has been interpreted as a form of sensitivity to motion.A new type of stimulus is used here to study the sensitivity of IC neurons to dynamic changes in ITD, in which broad- or narrowband stimuli are swept through a range of ITDs with arbitrary start-ITD, end-ITD, speed, and direction. Extracellular recordings were obtained under barbiturate anesthesia in the cat. We applied the same analyses as previously introduced for the study of responses to tones.We find effects of motion which are similar to those described in response to interaural phase modulation of tones. The size of the effects strongly depended on the motion parameters but was overall smaller than reported for tones. We found that the effects of motion could largely be explained by the temporal response pattern of the neuron such as adaptation and build-up. Our data add to previous evidence questioning true coding of motion at the level of the IC.

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Temporal Trends in SARS-CoV-2 Antibody Levels Among COVID-19 Patients in Kerala During the First Wave and Pre-vaccination Period.

The SARS-CoV-2 virus interacts with host cells through the S1 domain of its spike protein. This study measures the IgG immune response to this domain in COVID-19 patients from Kerala, India, and explores its association with various health factors. A cohort of 258 COVID-19 patients was analyzed for IgG antibodies targeting the S1 spike protein domain. The temporal pattern of the IgG response and its correlation with hospitalization needs, intensive care, and pre-existing conditions such as diabetes, hypertension, and coronary artery disease were assessed. A significant IgG response (76.4%) was detected, indicating robust immune activation post-infection. The IgG levels peaked between two tofour and four to eight weeks post-infection, with a notable increase at 12 weeks, hinting at possible secondary exposure or an immune memory response. No correlation was found between IgG levels and the presence of diabetes mellitus, hypertension, or coronary artery disease. However, higher IgG responses correlated with the severity of the infection, as seen in patients requiring hospitalization or intensive care. The IgG response to the S1 spike protein domain serves as a potential marker of immune activation in COVID-19. It reflects the body's defense mechanism against the virus and may predict disease severity and outcomes. The findings suggest that IgG levels could be indicative of the viral load, inflammatory response, and possibly the likelihood of protection against reinfection.

Monitoring drought impacts on street trees using remote sensing - Disentangling temporal and species-specific response patterns with Sentinel-2 imagery

Healthy street trees provide important ecosystem services to cities, but are under increasing stress from urbanisation and climate change, including drought. Traditional field observations are limited in their ability to provide city-wide and regular monitoring of the drought response of street trees, which is essential for their effective management. To overcome this, we propose a novel workflow using freely available remote sensing imagery from Sentinel-2 to identify temporal and species-specific drought response patterns of street trees. We evaluate the workflow on a sample of 2514 mature street trees of the seven most common street tree species in Leipzig, Germany. For each tree, we generate time series of eight vegetation indices from 2017 to 2022, namely enhanced vegetation index (EVI), normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), each using the 10 m and 20 m resolution near-infrared band (EVI-10, EVI-20, NBR-10, NBR-20, NDVI-10, NDVI-20), as well as red edge normalised difference vegetation index (RENDVI) and red-green vegetation index (RGVI). They form the basis for a correlation analysis with the meteorological drought indicator standardised precipitation evapotranspiration index (SPEI) and for annual growing season integrals, which we subtract from those of the base year 2017. We use boxplots and statistical hypothesis testing to examine differences between and within tree species and years. The results show positive relationships between the eight vegetation indices and the SPEI, with the NBR-20 having the highest correlation coefficients. We also find significant differences in the drought response of several tree species, with Quercus robur being the most drought responsive and Platanus x acerifolia being the least. While most tree species have significantly smaller growing season integrals in the drought years 2018 and 2020 than in the non-drought years 2017 and 2021, the effects are not as pronounced in the drought years 2019 and 2022. Uncertainties arise, for example, from spectral signal variations caused by adjacent land use. Nevertheless, the proposed workflow holds promise for incorporation into holistic urban green management solutions and mixed-method approaches for further research into the causes and consequences of drought-induced damage to street trees.

Nonlinear time effects of vegetation response to climate change: Evidence from Qilian Mountain National Park in China

Vegetation responses to climate change are typically nonlinear with varied time effects, yet current research lacks comprehensiveness and precise definitions, hindering a deeper understanding of the underlying mechanisms. This study focuses on the mountain-type Qilian Mountain National Park (QMNP), investigating the characteristics and patterns of these nonlinear time effects using a generalized additive model (GAM) based on MODIS-NDVI, growing season temperature, and precipitation data. The results show that 1) The time effects of climate change on vegetation exhibit significant spatial variations, differing across vegetation types and topographic conditions. Accounting for optimal time effects can increase the explanatory power of climate on vegetation change by 6.8 %. Precipitation responses are mainly characterized by time-lag and time-accumulation effects, notably in meadows and steppes, while temperature responses are largely cumulative, especially in steppes. The altitude and slope significantly influence the pattern of vegetation response to climate, particularly in areas with high altitudes and steep slopes. 2) There is a significant nonlinear relationship between vegetation growth and both precipitation and temperature, with the nonlinear relationship between precipitation and vegetation being stronger than that with temperature, particularly in the western and central regions of the park. Different vegetation types exhibit significant variations in their response to climate change, with deserts and steppes being more sensitive to precipitation. 3) Precipitation is the primary driver of vegetation change in the QMNP, particularly for high-elevation vegetation and herbaceous vegetation. The complex temporal patterns of vegetation response to climate change in the QMNP not only deepen the understanding of the intricate relationship between regional vegetation and climate variability but also provide a methodological reference for global studies on vegetation responses to climate change.

Imaging Findings Post-Stereotactic Radiosurgery for Vestibular Schwannoma: A Primer for the Radiologist.

Noninvasive tumor control of vestibular schwannomas through stereotactic radiosurgery allows high rates of long-term tumor control and has been used primarily for small- and medium-sized vestibular schwannomas. The posttreatment imaging appearance of the tumor, temporal patterns of growth and treatment response, as well as extratumoral complications can often be both subtle or confusing and should be appropriately recognized. Herein, the authors present an imaging-based review of expected changes as well as associated complications related to radiosurgery for vestibular schwannomas.

High-resolution kinetics of herbivore-induced plant volatile transfer reveal clocked response patterns in neighboring plants.

Volatiles emitted by herbivore-attacked plants (senders) can enhance defenses in neighboring plants (receivers), however, the temporal dynamics of this phenomenon remain poorly studied. Using a custom-built, high-throughput proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) system, we explored temporal patterns of volatile transfer and responses between herbivore-attacked and undamaged maize plants. We found that continuous exposure to natural blends of herbivore-induced volatiles results in clocked temporal response patterns in neighboring plants, characterized by an induced terpene burst at the onset of the second day of exposure. This delayed burst is not explained by terpene accumulation during the night, but coincides with delayed jasmonate accumulation in receiver plants. The delayed burst occurs independent of day:night light transitions and cannot be fully explained by sender volatile dynamics. Instead, it is the result of a stress memory from volatile exposure during the first day and secondary exposure to bioactive volatiles on the second day. Our study reveals that prolonged exposure to natural blends of stress-induced volatiles results in a response that integrates priming and direct induction into a distinct and predictable temporal response pattern. This provides an answer to the long-standing question of whether stress volatiles predominantly induce or prime plant defenses in neighboring plants, by revealing that they can do both in sequence.

High-resolution kinetics of herbivore-induced plant volatile transfer reveal clocked response patterns in neighboring plants

Volatiles emitted by herbivore-attacked plants (senders) can enhance defenses in neighboring plants (receivers), however, the temporal dynamics of this phenomenon remain poorly studied. Using a custom-built, high-throughput proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) system, we explored temporal patterns of volatile transfer and responses between herbivore-attacked and undamaged maize plants. We found that continuous exposure to natural blends of herbivore-induced volatiles results in clocked temporal response patterns in neighboring plants, characterized by an induced terpene burst at the onset of the second day of exposure. This delayed burst is not explained by terpene accumulation during the night, but coincides with delayed jasmonate accumulation in receiver plants. The delayed burst occurs independent of day:night light transitions and cannot be fully explained by sender volatile dynamics. Instead, it is the result of a stress memory from volatile exposure during the first day and secondary exposure to bioactive volatiles on the second day. Our study reveals that prolonged exposure to natural blends of stress-induced volatiles results in a response that integrates priming and direct induction into a distinct and predictable temporal response pattern. This provides an answer to the long-standing question of whether stress volatiles predominantly induce or prime plant defenses in neighboring plants, by revealing that they can do both in sequence.

A novel multi-omics data analysis of dose-dependent and temporal changes in regulatory pathways due to chemical perturbation: a case study on caffeine

Comprehensive analysis of multi-omics data can reveal alterations in regulatory pathways induced by cellular exposure to chemicals by characterizing biological processes at the molecular level. Data-driven omics analysis, conducted in a dose-dependent or dynamic manner, can facilitate comprehending toxicity mechanisms. This study introduces a novel multi-omics data analysis designed to concurrently examine dose-dependent and temporal patterns of cellular responses to chemical perturbations. This analysis, encompassing preliminary exploration, pattern deconstruction, and network reconstruction of multi-omics data, provides a comprehensive perspective on the dynamic behaviors of cells exposed to varying levels of chemical stimuli. Importantly, this analysis is adaptable to any number of omics layers, including site-specific phosphoproteomics. We implemented this analysis on multi-omics data obtained from HepG2 cells exposed to a range of caffeine doses over varying durations and identified six response patterns, along with their associated biomolecules and pathways. Our study demonstrates the effectiveness of the proposed multi-omics data analysis in capturing multidimensional patterns of cellular response to chemical perturbation, enhancing understanding of pathway regulation for chemical risk assessment.

Temporal Tracking of Insulin Action on the Cell Surface of Proteins at a Resolution of Ten Seconds.

The ligand-receptor signaling occurring on the cell surface governs cell growth, proliferation, and survival via rapidly triggering a cascade of events. Here, we for the first time report an in situ perturbation-free and rapid surface proteomic profiling at a temporal resolution of ten seconds. By this innovation, about 1022 cell surface-associated proteins were reproducibly identified and quantified. It is noteworthy that, upon a model ligand insulin stimulus, a few rapid-responding proteins at 10 s to 2 min were identified, e.g., CNNM3. Moreover, temporal response patterns were established for the members of GLUT4 storage vesicles (GSVs; responsible for glucose transportation) and confirmed with five known GSV proteins. This pattern was then exploited to uncover seven new regulatory proteins (LDLR, HFE, ECE1, MRC2, CORO1C, CPD, and BST2). Collectively, we showed a powerful surface proteomic tool to decipher rapid signaling of cell-surface proteins and to uncover new subunits involved in rapidly trafficking vesicles.

Temporal consistency of neurovascular components on awakening: preliminary evidence from electroencephalography, cerebrovascular reactivity, and functional magnetic resonance imaging.

Sleep inertia (SI) is a time period during the transition from sleep to wakefulness wherein individuals perceive low vigilance with cognitive impairments; SI is generally identified by longer reaction times (RTs) in attention tasks immediately after awakening followed by a gradual RT reduction along with waking time. The sluggish recovery of vigilance in SI involves a dynamic process of brain functions, as evidenced in recent functional magnetic resonance imaging (fMRI) studies in within-network and between-network connectivity. However, these fMRI findings were generally based on the presumption of unchanged neurovascular coupling (NVC) before and after sleep, which remains an uncertain factor to be investigated. Therefore, we recruited 12 young participants to perform a psychomotor vigilance task (PVT) and a breath-hold task of cerebrovascular reactivity (CVR) before sleep and thrice after awakening (A1, A2, and A3, with 20 min intervals in between) using simultaneous electroencephalography (EEG)-fMRI recordings. If the NVC were to hold in SI, we hypothesized that time-varying consistencies could be found between the fMRI response and EEG beta power, but not in neuron-irrelevant CVR. Results showed that the reduced accuracy and increased RT in the PVT upon awakening was consistent with the temporal patterns of the PVT-induced fMRI responses (thalamus, insula, and primary motor cortex) and the EEG beta power (Pz and CP1). The neuron-irrelevant CVR did not show the same time-varying pattern among the brain regions associated with PVT. Our findings imply that the temporal dynamics of fMRI indices upon awakening are dominated by neural activities. This is the first study to explore the temporal consistencies of neurovascular components on awakening, and the discovery provides a neurophysiological basis for further neuroimaging studies regarding SI.

Temporal pattern of humoral immune response in mild cases of COVID-19.

Understanding the humoral response pattern of coronavirus disease 2019 (COVID-19) is one of the essential factors to better characterize the immune memory of patients, which allows understanding the temporality of reinfection, provides answers about the efficacy and durability of protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and consequently helps in global public health and vaccination strategy. Among the patients who became infected with SARS-CoV-2, the majority who did not progress to death were those who developed the mild COVID-19, so understanding the pattern and temporality of the antibody response of these patients is certainly relevant. To investigate the temporal pattern of humoral response of specific immunoglobulin G (IgG) in mild cases of COVID-19. Blood samples from 191 COVID-19 real-time reverse transcriptase-polymerase chain reaction (RT-qPCR)-positive volunteers from the municipality of Toledo/ Paraná/Brazil, underwent two distinct serological tests, enzyme-linked immunosorbent assay, and detection of anti-nucleocapsid IgG. Blood samples and clinicoepidemiological data of the volunteers were collected between November 2020 and February 2021. All assays were performed in duplicate and the manufacturers' recommendations were strictly followed. The data were statistically analyzed using multiple logistic regression; the variables were selected by applying the P < 0.05 criterion. Serological tests to detect specific IgG were performed on serum samples from volunteers who were diagnosed as being positive by RT-qPCR for COVID-19 or had disease onset in the time interval from less than 1 mo to 7 mo. The time periods when the highest number of participants with detectable IgG was observed were 1, 2 and 3 mo. It was observed that 9.42% of participants no longer had detectable IgG antibodies 1 mo only after being infected with SARS-CoV-2 and 1.57% were also IgG negative at less than 1 mo. At 5 mo, 3.14% of volunteers were IgG negative, and at 6 or 7 mo, 1 volunteer (0.52%) had no detectable IgG. During the period between diagnosis by RT-qPCR/symptoms onset and the date of collection for the study, no statistical significance was observed for any association analyzed. Moreover, considering the age category between 31 and 59 years as the exposed group, the P value was 0.11 for the category 31 to 59 years and 0.32 for the category 60 years or older, showing that in both age categories there was no association between the pair of variables analyzed. Regarding chronic disease, the exposure group consisted of the participants without any comorbidity, so the P value of 0.07 for the category of those with at least one chronic disease showed no association between the two variables. A temporal pattern of IgG response was not observed, but it is suggested that immunological memory is weak and there is no association between IgG production and age or chronic disease in mild COVID-19.

Real-Time Monitoring of Miniaturized Thermal Food Processing by Advanced Mass Spectrometric Techniques

Mass spectrometry is a popular and powerful analytical tool to study the effects of food processing. Industrial sampling, real-life sampling, or challenging academic research on process-related volatile and aerosol research often demand flexible, time-sensitive data acquisition by state-of-the-art mass analyzers. Here, we show a laboratory-scaled, miniaturized, and highly controllable setup for the online monitoring of aerosols and volatiles from thermal food processing based on dielectric barrier discharge ionization (DBDI) mass spectrometry (MS). We demonstrate the opportunities offered by the setup from a foodomics perspective to study emissions from the thermal processing of wheat bread rolls at 210 °C by Fourier transformation ion cyclotron resonance MS. As DBDI is an emerging technology, we compared its ionization selectivity to established atmospheric pressure ionization tools: we found DBDI preferably ionizes saturated, nitrogenous compounds. We likewise identified a sustainable overlap in the selectivity of detected analytes with APCI and electrospray ionization (ESI). Further, we dynamically recorded chemical fingerprints throughout the thermal process. Unsupervised classification of temporal response patterns was used to describe the dynamic nature of the reaction system. Compared to established tools for real-time MS, our setup permits one to monitor chemical changes during thermal food processing at ultrahigh resolution, establishing an advanced perspective for real-time mass spectrometric analysis of food processing.

Evaluating phenotypes associated with heat tolerance and identifying moderate and severe heat stress thresholds in lactating sows housed in mechanically or naturally ventilated barns during the summer under commercial conditions.

An accurate understanding of heat stress (HS) temperatures and phenotypes that indicate HS tolerance is necessary to improve swine HS resilience. Therefore, the study objectives were 1) to identify phenotypes indicative of HS tolerance, and 2) to determine moderate and severe HS threshold temperatures in lactating sows. Multiparous (4.10 ± 1.48) lactating sows and their litters (11.10 ± 2.33 piglets/litter) were housed in naturally ventilated (n = 1,015) or mechanically ventilated (n = 630) barns at a commercial sow farm in Maple Hill, NC USA between June 9 and July 24, 2021. In-barn dry bulb temperatures (TDB) and relative humidity were continuously recorded for naturally ventilated (26.38 ± 1.21°C and 83.38 ± 5.40%, respectively) and mechanically ventilated (26.91 ± 1.80°C and 77.13 ± 7.06%, respectively) barns using data recorders. Sows were phenotyped between lactation d 11.28 ± 3.08 and 14.25 ± 3.26. Thermoregulatory measures were obtained daily at 0800, 1200, 1600, and 2000h and included respiration rate, and ear, shoulder, rump, and tail skin temperatures. Vaginal temperatures (TV) were recorded in 10min intervals using data recorders. Anatomical characteristics were recorded, including ear area and length, visual and caliper-assessed body condition scores, and a visually assessed and subjective hair density score. Data were analyzed using PROC MIXED to evaluate the temporal pattern of thermoregulatory responses, phenotype correlations were based on mixed model analyses, and moderate and severe HS inflection points were established by fitting TV as the dependent variable in a cubic function against TDB. Statistical analyses were conducted separately for sows housed in mechanically or naturally ventilated barns because the sow groups were not housed in each facility type simultaneously. The temporal pattern of thermoregulatory responses was similar for naturally and mechanically ventilated barns and several thermoregulatory and anatomical measures were significantly correlated with one another (P < 0.05), including all anatomical measures as well as skin temperatures, respiration rates, and TV. For sows housed in naturally and mechanically ventilated facilities, moderate HS threshold TDB were 27.36 and 26.69°C, respectively, and severe HS threshold TDB were 29.45 and 30.60°C, respectively. In summary, this study provides new information on the variability of HS tolerance phenotypes and environmental conditions that constitute HS in commercially housed lactating sows.

Neural tracking of linguistic and acoustic speech representations decreases with advancing age

Background: Older adults process speech differently, but it is not yet clear how aging affects different levels of processing natural, continuous speech, both in terms of bottom-up acoustic analysis and top-down generation of linguistic-based predictions. We studied natural speech processing across the adult lifespan via electroencephalography (EEG) measurements of neural tracking.Goals: Our goals are to analyze the unique contribution of linguistic speech processing across the adult lifespan using natural speech, while controlling for the influence of acoustic processing. Moreover, we also studied acoustic processing across age. In particular, we focus on changes in spatial and temporal activation patterns in response to natural speech across the lifespan.Methods: 52 normal-hearing adults between 17 and 82 years of age listened to a naturally spoken story while the EEG signal was recorded. We investigated the effect of age on acoustic and linguistic processing of speech. Because age correlated with hearing capacity and measures of cognition, we investigated whether the observed age effect is mediated by these factors. Furthermore, we investigated whether there is an effect of age on hemisphere lateralization and on spatiotemporal patterns of the neural responses.Results: Our EEG results showed that linguistic speech processing declines with advancing age. Moreover, as age increased, the neural response latency to certain aspects of linguistic speech processing increased. Also acoustic neural tracking (NT) decreased with increasing age, which is at odds with the literature. In contrast to linguistic processing, older subjects showed shorter latencies for early acoustic responses to speech. No evidence was found for hemispheric lateralization in neither younger nor older adults during linguistic speech processing. Most of the observed aging effects on acoustic and linguistic processing were not explained by age-related decline in hearing capacity or cognition. However, our results suggest that the effect of decreasing linguistic neural tracking with advancing age at word-level is also partially due to an age-related decline in cognition than a robust effect of age.Conclusion: Spatial and temporal characteristics of the neural responses to continuous speech change across the adult lifespan for both acoustic and linguistic speech processing. These changes may be traces of structural and/or functional change that occurs with advancing age.