Unique Temporal Patterns Research Articles

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.

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques. Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features. Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data. The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI. This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.

Beyond adult models: Tribolium castaneum larval timekeeping reveals unexpected robustness and insights into circadian clock.

Circadian rhythms are self-sustained endogenous oscillations that are found in all living organisms. In insects, circadian rhythms control a wide variety of behavioral and physiological processes, including feeding, locomotion, mating, and metabolism. While the role of circadian rhythms in adult insects is well-understood, it is largely unexplored in larvae. This study investigates the potential for larval synchronized activity in the red flour beetle (Tribolium castaneum), a species exhibiting solitary and aggregation phases. We hypothesized that, similar to adults, larvae would exhibit a daily activity pattern governed by an endogenous circadian clock. We further predicted that the transition between the solitary and gregarious phases extends to unique temporal activity patterns. Our results revealed unique timekeeper gene expression in larvae, leading to a distinct daily rhythm characterized by nocturnal activity. Cues indicating on potential cannibalism did not change daily activity peak. However, the absence of these cues significantly reduced the proportion of rhythmic larvae and led to higher variation in peak activity, highlighting the crucial role of social interactions in shaping their rhythmicity. This study sheds light on the evolution and function of larval synchronization in group-living insects, offering novel insights into this complex behavior.

IEat: automatic wearable dietary monitoring with bio-impedance sensing

Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people’s dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol

The Industrial Internet of Things (IIoT) and Smart Manufacturing environments face significant challenges in machine identity authentication, crucial for ensuring data integrity and cybersecurity. Traditional methods like One-Class Support Vector Machines (OCSVM) often fall short in sensitivity, specificity, and explainability. This research introduces a novel Functional Data Analysis (FDA)-based model that leverages unique temporal patterns in machine-generated data to authenticate machine identities, adapting to equipment wear and tear. Through a comprehensive comparative analysis, the FDA model demonstrates superior performance and robustness in industrial settings. Additionally, we propose integrating this FDA-based approach with the MQTT 5.0 protocol, enhancing data security in IIoT communications. This work not only advances IIoT cybersecurity but also supports digital twins and predictive maintenance, contributing to a more secure and efficient Smart Manufacturing ecosystem.

Region and layer-specific expression of GABAA receptor isoforms and KCC2 in developing cortex.

γ-Aminobutyric acid (GABA) type A receptors (GABAARs) are ligand-gated Cl-channels that mediate the bulk of inhibitory neurotransmission in the mature CNS and are targets of many drugs. During cortical development, GABAAR-mediated signals are significantly modulated by changing subunit composition and expression of Cl-transporters as part of developmental processes and early network activity. To date, this developmental evolution has remained understudied, particularly at the level of cortical layer-specific changes. In this study, we characterized the expression of nine major GABAAR subunits and K-Cl transporter 2 (KCC2) in mouse somatosensory cortex from embryonic development to postweaning maturity. We evaluated expression of α1-5, β2-3, γ2, and δ GABAAR subunits using immunohistochemistry and Western blot techniques, and expression of KCC2 using immunohistochemistry in cortices from E13.5 to P25 mice. We found that embryonic cortex expresses mainly α3, α5, β3, and γ2, while expression of α1, α2, α4, β2, δ, and KCC2 begins at later points in development; however, many patterns of nuanced expression can be found in specific lamina, cortical regions, and cells and structures. While the general pattern of expression of each subunit and KCC2 is similar to previous studies, we found a number of unique temporal, regional, and laminar patterns that were previously unknown. These findings provide much needed knowledge of the intricate developmental evolution in GABAAR composition and KCC2 expression to accommodate developmental signals that transition to mature neurotransmission.

The application of PTR-MS and non-targeted analysis to characterize VOCs emitted from a plastic recycling facility fire.

On April 11th, 2023, the My Way Trading (MWT) recycling facility in Richmond, Indiana caught fire, mandating the evacuation of local residents and necessitating the U.S. Environmental Protection Agency (EPA) to conduct air monitoring. The EPA detected elevated levels of plastic combustion-related air pollutants, including hydrogen cyanide and benzene. We aimed to identify these and other volatile organic compounds (VOCs) present as well as to identify the potential hazard of each compound for various human health effects. To identify the VOCs, we conducted air monitoring at sites within and bordering the evacuation zone using proton transfer reaction mass spectrometry (PTR-MS) and non-targeted analysis (NTA). To facilitate risk assessment of the emitted VOCs, we used the EPA Hazard Comparison Dashboard. We identified 46 VOCs, within and outside the evacuation zone, with average detection levels above local background levels measured in Middletown, OH. Levels of hydrogen cyanide and 4 other VOCs were at least 1.8-fold higher near the incidence site in comparison to background levels and displayed unique temporal and spatial patterns. The 46 VOCs identified had the highest hazardous potential for eye and skin irritation, with approximately 45% and 39%, respectively, of the VOCs classified as high and very high hazards for these endpoints. Notably, all detected VOC levels were below the hazard thresholds set for single VOC exposures; however, hazard thresholds for exposure to VOC mixtures are currently unclear. This study serves as a proof-of-concept that PTR-MS coupled with NTA can facilitate rapid identification and hazard assessment of VOCs emitted following anthropogenic disasters. Furthermore, it demonstrates that this approach may augment future disaster responses to quantify additional VOCs present in complex combustion mixtures.

Temporal regulation of the Mediator complex during muscle proliferation, differentiation, regeneration, aging, and disease.

Genesis of skeletal muscle relies on the differentiation and fusion of mono-nucleated muscle progenitor cells into the multi-nucleated muscle fiber syncytium. The temporally-controlled cellular and morphogenetic changes underlying this process are initiated by a series of highly coordinated transcription programs. At the core, the myogenic differentiation cascade is driven by muscle-specific transcription factors, i.e., the Myogenic Regulatory Factors (MRFs). Despite extensive knowledge on the function of individual MRFs, very little is known about how they are coordinated. Ultimately, highly specific coordination of these transcription programs is critical for their masterfully timed transitions, which in turn facilitates the intricate generation of skeletal muscle fibers from a naïve pool of progenitor cells. The Mediator complex links basal transcriptional machinery and transcription factors to regulate transcription and could be the integral component that coordinates transcription factor function during muscle differentiation, growth, and maturation. In this study, we systematically deciphered the changes in Mediator complex subunit expression in skeletal muscle development, regeneration, aging, and disease. We incorporated our in vitro and in vivo experimental results with analysis of publicly available RNA-seq and single nuclei RNA-seq datasets and uncovered the regulation of Mediator subunits in different physiological and temporal contexts. Our experimental results revealed that Mediator subunit expression during myogenesis is highly dynamic. We also discovered unique temporal patterns of Mediator expression in muscle stem cells after injury and during the early regeneration period, suggesting that Mediator subunits may have unique contributions to directing muscle stem cell fate. Although we observed few changes in Mediator subunit expression in aging muscles compared to younger muscles, we uncovered extensive heterogeneity of Mediator subunit expression in dystrophic muscle nuclei, characteristic of chronic muscle degeneration and regeneration cycles. Taken together, our study provides a glimpse of the complex regulation of Mediator subunit expression in the skeletal muscle cell lineage and serves as a springboard for mechanistic studies into the function of individual Mediator subunits in skeletal muscle.

Temporal patterns of sleep latency in central hypersomnia and attention deficit hyperactivity disorder: a cluster analysis exploration using Multiple Sleep Latency Test.

Excessive daytime sleepiness (EDS) is a crucial symptom that diminishes the quality of life. The primary causes of EDS are central hypersomnia, including narcolepsy type 1 (NT1), type 2 (NT2), and idiopathic hypersomnia (IH). EDS is often associated with other psychiatric disorders, particularly attention deficit hyperactivity disorder (ADHD). The Multiple Sleep Latency Test (MSLT) is the standard assessment tool for EDS. Although the MSLT yields numerous parameters, most are not employed in clinical practice. In this study, we leveraged novel MSLT parameters to discern central hypersomnia and ADHD presence. Our analysis focused on sleep latency variability and employed cluster analysis to identify unique temporal patterns. We examined the MSLT data from 333 patients; of these, 200 (aged 14-54, mean: 24.9 ± 8.1, years; 114 females) met the inclusion criteria comprising comprehensive data an Apnea-Hypopnea Index (AHI) below 5, and no prior diagnosis of sleep apnea syndrome. We employed a time-course cluster approach that specifically targeted sleep latency variability during the MSLT. Considering both multiple clustering quality evaluations and the study's objectives, we identified 9 distinct clusters. Clusters 1 and 3 predominantly had MSLT-positive results; Cluster 2 was entirely MSLT-positive; Clusters 4, 5, 6, 8, and 9 were mainly MSLT-negative; and Cluster 7 had mixed results. The diagnosis of hypersomnia varied notably among Clusters 1, 2, 3, and 7, with Cluster 2 demonstrating a pronounced tendency towards NT1 and NT2 diagnoses (p < 0.005). However, no significant correlation was observed between ADHD diagnoses and specific sleep latency patterns in any cluster. Our study highlights the value of time-course clustering in understanding sleep latency patterns of patients with central hypersomnia.

Biphasic Npas4 expression promotes inhibitory plasticity and suppression of fear memory consolidation in mice

Long-term memories are believed to be encoded by unique transcriptional signatures in the brain. The expression of immediate early genes (IEG) promotes structural and molecular changes required for memory consolidation. Recent evidence has shown that the brain is equipped with mechanisms that not only promote, but actively constrict memory formation. However, it remains unknown whether IEG expression may play a role in memory suppression. Here we uncovered a novel function of the IEG neuronal PAS domain protein 4 (Npas4), as an inducible memory suppressor gene of highly salient aversive experiences. Using a contextual fear conditioning paradigm, we found that low stimulus salience leads to monophasic Npas4 expression, while highly salient learning induces a biphasic expression of Npas4 in the hippocampus. The later phase requires N-methyl-D-aspartate (NMDA) receptor activity and is independent of dopaminergic neurotransmission. Our in vivo pharmacological and genetic manipulation experiments suggested that the later phase of Npas4 expression restricts the consolidation of a fear memory and promote behavioral flexibility, by facilitating fear extinction and the contextual specificity of fear responses. Moreover, immunofluorescence and electrophysiological analysis revealed a concomitant increase in synaptic input from cholecystokinin (CCK)-expressing interneurons. Our results demonstrate how salient experiences evoke unique temporal patterns of IEG expression that fine-tune memory consolidation. Moreover, our study provides evidence for inducible gene expression associated with memory suppression as a possible mechanism to balance the consolidation of highly salient memories, and thereby to evade the formation of maladaptive behavior.

Fisher information and the Cramér-Rao lower bound in single-pixel localization microscopy with spatiotemporally modulated illumination.

Single-pixel imaging, the concept that an image can be captured via a single-pixel detector, is a cost-effective yet powerful technique to reduce data acquisition duration without sacrificing image resolution when properly structured illumination patterns are introduced. Normally, the image reconstruction process is subject to the diffraction limit. Here, we study the possibility of exploiting the information contained in the illumination patterns to enable a form of single-pixel localization microscopy (SPLM) for super-resolution. This concept is inspired by coherent holographic image reconstruction by phase transfer (CHIRPT) microscopy. CHIRPT microscopy is a single-pixel imaging technique that uses structured illumination that is spatiotemporally modulated (STM) so that a unique temporal modulation pattern is imparted to each point within a large illumination volume. The fluorescent light emitted by molecules contains the same temporal modulations as the illumination patterns at the locations of the molecules. By recording a portion of the total emitted fluorescent power, the signal may be numerically processed to form an image. Unique temporal modulation patterns that excite fluorescent probes at each point can also be used to localize individual molecules by matching their particular temporal light emission patterns to the measured temporal signal. This paper evaluates the feasibility of SPLM with STM illuminations used in and inspired by CHIRPT microscopy via the information content its data carry about the emitter location(s). More specifically, we provide the mathematical formalism of Fisher information (FI) and the Cramér-Rao lower bound (CRLB) associated with the location parameters of the emitter(s). The FI and CRLB are then numerically evaluated under different experimental assumptions to assess the effects of experimental parameters on localization precision. Last, we compare the single-pixel CRLB to that from camera-based single-molecule localization microscopy in the localization of a single emitter. We show that SPLM has several distinguishing characteristics that provide certain advantages, such as relatively constant CRLB over a very large illumination volume and improved CRLB for 3D localization due to the information coupling introduced by simultaneous modulations of the transverse axes.

Comparative Transcriptome Analysis Identifies Key Defense Genes and Mechanisms in Mulberry (Morus alba) Leaves against Silkworms (Bombyx mori).

As a consequence of long-term coevolution and natural selection, the leaves of mulberry (Morus alba) trees have become the best food source for silkworms (Bombyx mori). Nevertheless, the molecular and genomic basis of defense response remains largely unexplored. In the present study, we assessed changes in the transcriptome changes of mulberry in response to silkworm larval feeding at 0, 3, and 6 h. A total of 4709 (up = 2971, down = 1738) and 3009 (up = 1868, down = 1141) unigenes were identified after 3 and 6 h of silkworm infestation, respectively. MapMan enrichment analysis results show structural traits such as leaf surface wax, cell wall thickness and lignification form the first physical barrier to feeding by the silkworms. Cluster analysis revealed six unique temporal patterns of transcriptome changes. We predicted that mulberry promoted rapid changes in signaling and other regulatory processes to deal with mechanical damage, photosynthesis impairment, and other injury caused by herbivores within 3-6 h. LRR-RK coding genes (THE1, FER) was predicted participated in perception of cell wall perturbation in mulberry responding to silkworm feeding. Ca2+ signal sensors (CMLs), ROS (OST1, SOS3), RBOHD/F, CDPKs, and ABA were part of the regulatory network after silkworm feeding. Jasmonic acid (JA) signal transduction was predicted to act in silkworm feeding response, 10 JA signaling genes (such as OPR3, JAR1, and JAZ1) and 21 JA synthesis genes (such as LOX2, AOS, and ACX1) were upregulated after silkworm feeding for 3 h. Besides, genes of "alpha-Linolenic acid metabolism" and "phenylpropanoid biosynthesis" were activated in 3 h to reprogram secondary metabolism. Collectively, these findings provided valuable insights into silkworm herbivory-induced regulatory and metabolic processes in mulberry, which might help improve the coevolution of silkworm and mulberry.

Temporal Pattern Coding in Ionic Memristor‐Based Spiking Neurons for Adaptive Tactile Perception

AbstractBiological neurons encode signals through firing voltage spike trains having unique temporal patterns, enabling efficient information representation and processing. Realization of these rich neuronal firing characteristics in a single electronic device, without circuitry and software assistance, promise compact and functional neuromorphic hardware for advanced artificial intelligence applications. Here, a Pt/Co3O4‐x/ITO‐based ionic memristor is reported that can faithfully produce voltage spike trains exhibiting diverse temporal patterns of biological neurons, under electric current stimulation. The spiking behaviors stem from the redistribution of ions in the device, governed by the current induced electric field and Joule heating effects. Tonic, phasic, burst, and adaptive firing patterns of neurons are demonstrated. Particularly, the adaptive firing characteristics allow the memristor to reduce the response to invariant current stimulation and to respond to current changes with enhanced sensitivity, implementing neuronal adaptive coding function. Integrating such memristors with pressure sensors yields an artificial tactile sensory system that can adaptively perceive small pressure variations in the presence of strong static pressure backgrounds, enabling accurate identification of touched objects in ever‐changing environments. This work opens up an avenue toward advanced neuromorphic hardware for smart neural prosthetics and bionic robotics applications.

Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.

A Large-scale Analysis of Hundreds of In-memory Key-value Cache Clusters at Twitter

Modern web services use in-memory caching extensively to increase throughput and reduce latency. There have been several workload analyses of production systems that have fueled research in improving the effectiveness of in-memory caching systems. However, the coverage is still sparse considering the wide spectrum of industrial cache use cases. In this work, we significantly further the understanding of real-world cache workloads by collecting production traces from 153 in-memory cache clusters at Twitter, sifting through over 80 TB of data, and sometimes interpreting the workloads in the context of the business logic behind them. We perform a comprehensive analysis to characterize cache workloads based on traffic pattern, time-to-live (TTL), popularity distribution, and size distribution. A fine-grained view of different workloads uncover the diversity of use cases: many are far more write-heavy or more skewed than previously shown and some display unique temporal patterns. We also observe that TTL is an important and sometimes defining parameter of cache working sets. Our simulations show that ideal replacement strategy in production caches can be surprising, for example, FIFO works the best for a large number of workloads.

Correlative Channel-Aware Fusion for Multi-View Time Series Classification

Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views. Existing methods for MVTSC mainly aim to fuse multi-view information at an early stage, e.g., by extracting a common feature subspace among multiple views. However, these approaches may not fully explore the unique temporal patterns of each view in complicated time series. Additionally, the label correlations of multiple views, which are critical to boosting, are usually under-explored for the MVTSC problem. To address the aforementioned issues, we propose a Correlative Channel-Aware Fusion (C$^2$AF) network. First, C$^2$AF extracts comprehensive and robust temporal patterns by a two-stream structured encoder for each view, and derives the intra-view/inter-view label correlations with a concise correlation matrix. Second, a channel-aware learnable fusion mechanism is implemented through CNN to further explore the global correlative patterns. Our C$^2$AF is an end-to-end framework for MVTSC. Extensive experimental results on three real-world datasets demonstrate the superiority of our C$^2$AF over the state-of-the-art methods. A detailed ablation study is also provided to illustrate the indispensability of each model component.

Motor Chunking in Internally Guided Sequencing.

Motor skill learning involves the acquisition of sequential motor movements with practice. Studies have shown that we learn to execute these sequences efficiently by chaining several elementary actions in sub-sequences called motor chunks. Several experimental paradigms, such as serial reaction task, discrete sequence production, and m × n task, have investigated motor chunking in externally specified sequencing where the environment or task paradigm provides the sequence of stimuli, i.e., the responses are stimulus driven. In this study, we examine motor chunking in a class of more realistic motor tasks that involve internally guided sequencing where the sequence of motor actions is self-generated or internally specified. We employ a grid-navigation task as an exemplar of internally guided sequencing to investigate practice-driven performance improvements due to motor chunking. The participants performed the grid-sailing task (GST) (Fermin et al., 2010), which required navigating (by executing sequential keypresses) a 10 × 10 grid from start to goal position while using a particular type of key mapping between the three cursor movement directions and the three keyboard buttons. We provide empirical evidence for motor chunking in grid-navigation tasks by showing the emergence of subject-specific, unique temporal patterns in response times. Our findings show spontaneous chunking without pre-specified or externally guided structures while replicating the earlier results with a less constrained, internally guided sequencing paradigm.

High-throughput quantitation of serological ceramides/dihydroceramides by LC/MS/MS: Pregnancy baseline biomarkers and potential metabolic messengers

Ceramides and dihydroceramides are sphingolipids that present in abundance at the cellular membrane of eukaryotes. Although their metabolic dysregulation has been implicated in many diseases, our knowledge about circulating ceramide changes during the pregnancy remains limited. In this study, we present the development and validation of a high-throughput liquid chromatography-tandem mass spectrometric method for simultaneous quantification of 16 ceramides and 10 dihydroceramides in human serum within 5 min. by using stable isotope-labeled ceramides as internal standards. This method employs a protein precipitation method for high throughput sample preparation, reverse phase isocratic elusion for chromatographic separation, and Multiple Reaction Monitoring for mass spectrometric detection. To qualify for clinical applications, our assay has been validated against the FDA guidelines for Lower Limit of Quantitation (1 nM), linearity (R2>0.99), precision (imprecision<15 %), accuracy (inaccuracy<15 %), extraction recovery (>90 %), stability (>85 %), and carryover (<0.01 %). With enhanced sensitivity and specificity from this method, we have, for the first time, determined the serological levels of ceramides and dihydroceramides to reveal unique temporal gestational patterns. Our approach could have value in providing insights into disorders of pregnancy.

The Weekend Effect on Urban Bat Activity Suggests Fine Scale Human-Induced Bat Movements.

Simple SummaryOn weekends, people do things differently from weekdays, such as dining at a restaurant, going to a night club, attending a concert or a sporting event, or simply staying up late. These leisure activities in the city can change the environment people live in and can hurt wildlife that also lives in the same city. We recorded bats in the city center and in the city periphery and compared how active bats were. We found that in the city center, bats were less active on weekends than weekdays. The opposite pattern was found in the city periphery. It is possible that bats moved from the city center to the city periphery on weekends. Thus, continuous greenways are important to facilitate bat movements and avoid human–wildlife conflict. City planners can add new parks and/or preserve old-growth vegetation to form the center-to-periphery greenways.In the urban environment, wildlife faces novel human disturbances in unique temporal patterns. The weekend effect describes that human activities on weekends trigger changes in the environment and impact wildlife negatively. Reduced occurrence, altered behaviors, and/or reduced fitness have been found in birds, ungulates, and meso-carnivores due to the weekend effect. We aimed to investigate if urban bat activity would differ on weekends from weekdays. We analyzed year-round bat acoustic monitoring data collected from two sites near the city center and two sites in the residential area/park complex in the city periphery. We constructed generalized linear models and found that bat activity was significantly lower on weekends as compared to weekdays during spring and summer at the site in the open space near the city center. In contrast, during the same seasons, the sites in the city periphery showed increased bat activity on weekends. Hourly bat activity overnight suggested that bats might move from the city center to the periphery on weekends. We demonstrated the behavioral adaptability in urban wildlife for co-existing with human. We recommend that urban planning should implement practices such as adding new greenspaces and/or preserving old-growth vegetation to form continuous greenways from the city center to the city periphery as corridors to facilitate bat movements and reduce possible human-wildlife conflict.

Transcriptome Analysis of Alcohol Drinking in Non-Dependent and Dependent Mice Following Repeated Cycles of Forced Swim Stress Exposure.

Chronic stress is a known contributing factor to the development of drug and alcohol addiction. Animal models have previously shown that repeated forced swim stress promotes escalated alcohol consumption in dependent animals. To investigate the underlying molecular adaptations associated with stress and chronic alcohol exposure, RNA-sequencing and bioinformatics analyses were conducted on the prefrontal cortex (CTX) of male C57BL/6J mice that were behaviorally tested for either non-dependent alcohol consumption (CTL), chronic intermittent ethanol (CIE) vapor dependent alcohol consumption, repeated bouts of forced swim stress alone (FSS), and chronic intermittent ethanol with forced swim stress (CIE + FSS). Brain tissue from each group was collected at 0-h, 72-h, and 168-h following the final test to determine long-lasting molecular changes associated with maladaptive behavior. Our results demonstrate unique temporal patterns and persistent changes in coordinately regulated gene expression systems with respect to the tested behavioral group. For example, increased expression of genes involved in “transmitter-gated ion channel activity” was only determined for CIE + FSS. Overall, our results provide a summary of transcriptomic adaptations across time within the CTX that are relevant to understanding the neurobiology of chronic alcohol exposure and stress.