Individual Signals Research Articles

The impact of signaling pathways on the desmosome ultrastructure in pemphigus

IntroductionThe autoantibody-driven disease pemphigus vulgaris (PV) impairs desmosome adhesion in the epidermis. In desmosomes, the pemphigus autoantigens desmoglein 1 (Dsg1) and Dsg3 link adjacent cells. Dsgs are clustered by plaque proteins and linked to the keratin cytoskeleton by desmoplakin (Dp). The aim of this study was to identify the impact of several PV-related signaling pathways on desmosome ultrastructure.MethodsSTED microscopy, Dispase-based dissociation assay.ResultsAs observed using STED microscopy, pemphigus autoantibodies (PV-IgG) reduced desmosome number, decreased desmosome size, increased plaque distance and thickness and caused loss of adhesion. Decreased desmosome number, increased plaque distance and thickness and loss of adhesion correlate with features found for newly assembled immature desmosomes, observed after Ca2+ depletion and repletion. This was paralleled by plaque asymmetry, keratin filament retraction and fragmentation of Dsg1 and Dsg3 immunostaining. Inhibition of each individual signaling pathway investigated here prevented the loss of adhesion and ameliorated keratin retraction. In addition, inhibition of p38MAPK or PLC completely rescued all parameters of desmosomes ultrastructure and increased desmosome number under basal conditions. In contrast, inhibition of MEK1/2 was only partially protective for desmosome size and plaque thickness, whereas inhibition of Src or increase of cAMP decreased desmosome size but increased the desmosome number even in the presence of PV-IgG. DiscussionAlterations of the desmosomal plaque ultrastructure are closely related to loss of adhesion and regulated differently by signaling pathways involved in pemphigus pathogenesis. This insight may allow identification of novel treatment options targeting specific steps of desmosome turn-over in the future.

Development of a non-invasive heart rate measurement method for sea turtles with dense keratinous scutes through effective electrode placement

Measuring the heart rate of sea turtles is important for understanding their physiological adaptations to the environment. Non-invasive methods to measure the electrocardiogram (ECG) of sea turtles have been developed by attaching electrodes to their carapace. However, this method has only been applicable to sea turtles with sparse keratin on their shell surfaces, such as loggerhead turtles, and it is difficult to detect heartbeats in sea turtles with dense keratinous scutes, including green sea turtles. Here, we explored the electrode placements on the plastron that can be applied to ECG measurement in green turtles. ECG signals were checked using a handheld ECG monitor at three sets of electrode placement on the plastron. When ECG signals could be detected, they were measured in the water tanks for several days to confirm the clarity of the ECG signals. Of the 29 green turtles, when the negative electrode was placed near the neck area of the plastron, clear ECG signals were obtained in nine individuals (39.1%), whereas ECG signals were not detected at any placements in four individuals (17.4%). Furthermore, in the water tank experiments, continuous ECG signals were successfully recorded by attaching a negative electrode near the neck: almost noiseless clear ECG signals even during moving in seven out of ten individuals and slightly weak and noisy signals in other individuals. The measured heart rate of ten individuals during resting was 8.6 ± 2.9 (means ± s.d.) beats min−1 and that during moving was 12.2 ± 4.7 beats min−1, similar to those reported in a previous study involving the insertion of electrodes inside the body. Therefore, for measuring the ECG of green turtles, the negative electrode should be placed closer to the neck, and the positive and earth electrodes should be placed to the lower left of the plastron. Although the selection of suitable individuals for measurements is required, this heart rate measurement method will contribute to a better understanding of the physiological status of sea turtles with dense keratinous scutes, including green turtles.

Harnessing individual nitrogen-vacancy centers with a compact and portable confocal microscope

Abstract Recent advancements in quantum technology have highlighted the potential of nitrogen-vacancy (NV) centers in diamond. However, fully realizing this potential requires addressing challenges related to the size, complexity, and cost of current optical systems used for NV center manipulation. In this work, we present a compact and portable confocal setup specifically designed for the efficient detection and control of single NV centers. Our system facilitates optical initialization and readout of individual NV center photoluminescence signals, enabling coherent spin control and nanoscale-resolution magnetic field sensing.

Evaluation of objective methods for analyzing ipsilateral motor evoked potentials in stroke survivors with chronic upper extremity motor impairment.

Objective:Ipsilateral motor evoked potentials (iMEPs) are believed to represent cortically evoked excitability of uncrossed brainstem-mediated pathways. In the event of extensive injury to (crossed) corticospinal pathways, which can occur following a stroke, uncrossed ipsilateral pathways may serve as an alternate resource to support the recovery of the paretic limb. However, iMEPs, even in neurally intact people, can be small, infrequent, and noisy, so discerning them in stroke survivors is very challenging. This study aimed to investigate the inter-rater reliability of iMEP features (presence/absence, amplitude, area, onset, and offset) to evaluate the reliability of existing methods for objectively analyzing iMEPs in stroke survivors with chronic upper extremity motor impairment.
Approach:Two investigators subjectively measured iMEP features from thirty-two stroke participants with chronic upper extremity motor impairment. Six objective methods based on standard deviation (SD) and mean consecutive differences (MCD) were used to measure the iMEP features from the same 32 participants. IMEP analysis used both trial-by-trial (individual signal) and average-signal analysis approaches. Inter-rater reliability of iMEP features and agreement between the subjective and objective methods were analyzed (percent agreement-PA and intraclass correlation coefficient-ICC).
Main results:Inter-rater reliability was excellent for iMEP detection (PA> 85%), amplitude, and area (ICC> 0.9). Of the six objective methods we tested, the 1SD method was most appropriate for identifying and analyzing iMEP amplitude and area (ICC> 0.9) in both trial-by-trial and average signal analysis approaches. None of the objective methods were reliable for analyzing iMEP onset and offset. Results also support using the average-signal analysis approach over the trial-by-trial analysis approach, as it offers excellent reliability for iMEP analysis in stroke survivors with chronic upper extremity motor impairment.
Significance:Findings from our study have relevance for understanding the role of ipsilateral pathways that typically survive unilateral severe white matter injury in people with stroke.&#xD.

Identification of the EBF1/ETS2/KLF2-miR-126-Gene Feed-Forward Loop in Breast Carcinogenesis and Stemness.

MicroRNA (miR)-126 is frequently downregulated in malignancies, including breast cancer (BC). Despite its tumor-suppressive role, the mechanisms underlying miR-126 deregulation in BC remain elusive. Through silencing experiments, we identified Early B Cell Factor 1 (EBF1), ETS Proto-Oncogene 2 (ETS2), and Krüppel-Like Factor 2 (KLF2) as pivotal regulators of miR-126 expression. These transcription factors were found to be downregulated in BC due to epigenetic silencing or a "poised but not transcribed" promoter state, impairing miR-126 expression. Gene Ontology analysis of differentially expressed miR-126 target genes in the Cancer Genome Atlas: Breast Invasive Carcinoma (TCGA-BRCA) cohort revealed their involvement in cancer-related pathways, primarily signal transduction, chromatin remodeling/transcription, and differentiation/development. Furthermore, we defined interconnections among transcription factors, miR-126, and target genes, identifying a potential feed-forward loop (FFL) crucial in maintaining cellular identity and preventing the acquisition of stemness properties associated with cancer progression. Our findings propose that the dysregulation of the EBF1/ETS2/KLF2/miR-126 axis disrupts this FFL, promoting oncogenic transformation and progression in BC. This study provides new insights into the molecular mechanisms of miR-126 downregulation in BC and highlights potential targets for therapeutic intervention. Further research is warranted to clarify the role of this FFL in BC, and to identify novel therapeutic strategies aimed at modulating this network as a whole, rather than targeting individual signals, for cancer management.

Cogwheel phase cycling in population-detected optical coherent multidimensional spectroscopy.

An integral procedure in every coherent multidimensional spectroscopy experiment is to suppress undesired background signals. For that purpose, one can employ a particular phase-matching geometry or phase cycling, a procedure that was adapted from nuclear magnetic resonance (NMR) spectroscopy. In optical multidimensional spectroscopy, phase cycling has been usually carried out in a "nested" fashion, where pulse phases are incremented sequentially with linearly spaced increments. Another phase-cycling approach that was developed for NMR spectroscopy is "cogwheel phase cycling," where all pulse phases are varied simultaneously in increments defined by so-called "winding numbers." Here we explore the concept of cogwheel phase cycling in the context of population-based coherent multidimensional spectroscopy. We derive selection rules for resolving and extracting fourth-order and higher-order nonlinear signals by cogwheel phase cycling and describe how to perform a numerical search for the winding numbers for various population-detected 2D spectroscopy experiments. We also provide an expression for a numerical search for nested phase-cycling schemes and predict the most economical schemes of both approaches for a wide range of nonlinear signals. The signal selectivity of the technique is demonstrated experimentally by acquiring rephasing and nonrephasing fourth-order signals of a laser dye by both phase-cycling approaches. We find that individual nonlinear signal contributions are, in most cases, captured with fewer steps by cogwheel phase cycling compared to nested phase cycling.

Immunity for counterproductive attentional capture by reward signals among individuals with depressive symptoms

ObjectivesThis study aimed to investigate the characteristics of attentional capture by reward signals in individuals with depression during classical conditioning. MethodsA variant of the additional singleton paradigm was adopted with a high- or low-reward signal as the prominent distracting stimulus. In Experiment 1, 46 participants with depressive symptoms and 46 healthy controls were asked to conduct a keypress response to a visual target. In Experiment 2, 58 participants with depressive symptoms and 58 healthy controls were asked to conduct a fixation response to the visual target. ResultsIn the keypress response task, the presence of high-reward signals slowed down the responses of participants in the control group, whereas the response times of individuals with depression were not significantly affected. In the fixation response task, when the high-reward signal was presented, individuals with depression were more likely to choose the target location as the first saccade destination, compared with healthy controls. In addition, individuals with depression exhibited fewer oculomotor capture by high-reward signals than healthy controls, a trait which was closely linked to the enhanced saccadic inhibition. ConclusionThe results of our study indicated that individuals with depression exhibited an abnormality in attentional capture by reward-related conditioned stimuli during classical conditioning.

Multi-Channel Lightweight Contrast Prediction Coding for Features Extraction of Radar Emitter Signals

This letter presents a novel multi-channel improved contrastive predictive coding (CPC) method to extract signals features. Specifically, to extract low pulse width radar signals features and meet the real-time requirements of signals identification, we design a lightweight encoder to realize the CPC features encoding function. Then, we construct a multi-channel CPC features decoder to mine and extract subtle individual signals features from the perspective of multi-domain and multi-channel information input. Simulation results verify the effectiveness of our proposed method, which can achieve state-of-the-art results in both accuracy and running time compared to the existing optimal methods. All our models and code are available at https://github.com/jn-z/MC-CPC.

Neuronal and oligodendroglial, but not astroglial, tau translates to in vivo tau PET signals in individuals with primary tauopathies

Tau PET has attracted increasing interest as an imaging biomarker for 4-repeat (4R)-tauopathy progressive supranuclear palsy (PSP). However, the translation of in vitro 4R-tau binding to in vivo tau PET signals is still unclear. Therefore, we performed a translational study using a broad spectrum of advanced methodologies to investigate the sources of [18F]PI-2620 tau PET signals in individuals with 4R-tauopathies, including a pilot PET autopsy study in patients. First, we conducted a longitudinal [18F]PI-2620 PET/MRI study in a 4-repeat-tau mouse model (PS19) and detected elevated [18F]PI-2620 PET signals in the presence of high levels of neuronal tau. An innovative approach involving cell sorting after radiotracer injection in vivo revealed higher tracer uptake in single neurons than in the astrocytes of PS19 mice. Regional [18F]PI-2620 tau PET signals during the lifetime correlated with the abundance of fibrillary tau and with autoradiography signal intensity in PSP patients and disease controls who underwent autopsy 2–63 months after tau PET. In autoradiography, tau-positive neurons and oligodendrocytes with a high AT8 density, but not tau-positive astrocytes, were the drivers of [18F]PI-2620 autoradiography signals in individuals with PSP. The high tau abundance in oligodendrocytes at the boundary of gray and white matter facilitated the identification of an optimized frontal lobe target region to detect the tau burden in patients with PSP. In summary, neuronal and oligodendroglial tau constitutes the dominant source of tau PET radiotracer binding in 4-repeat-tauopathies, translating to an in vivo signal.

Direct Determination of Metallic Contamination on and in Wafers Using Laser Ablation Inductively Coupled Plasma Mass Spectrometry

A new analytical technique has been developed for determination of metallic impurities on and in wafers using LA-GED-MSAG-ICP-MS. Up to 300 mm wafer can be analyzed directly without using a small enclosed chamber. Particles generated by a femto-second laser ablation were aspirated together with particle free clean air by an ejector and introduced to the ICP-MS (Inductively Coupled Plasma Mass Spectrometry) via GED (Gas Exchange Device). Particles in air could not be introduced to the plasma of ICP-MS directly because the Ar plasma could not be sustained when air was introduced. The GED exchanged air with Ar efficiently (> 99.98%), the particles came out from GED in Ar gas stream that could be introduced to the plasma of ICP-MS. For quantitation of LA-ICP-MS, MSAG (Metal Standard Aerosol Generation) was used. MSAG could introduce nearly a 100% of aqueous multi-element standard solution to the plasma of ICP-MS, which allowed precise quantitation using the method of standard addition while ablating wafer samples. The technique was used for the following applications:- Metallic contamination mapping of a wafer. Multiple spots (1 cm2/spot) of 300 mm wafer were laser ablated and analyzed by ICP-MS as shown in Fig.1. E6 - E7 atoms/cm2 of detection limit was obtained. The conventional VPD-ICP-MS technique uses a 1 mL scan solution to collect metallic impurities on an entire wafer surface of 300 mm wafer, and the collected solution is analyzed by ICP-MS giving E6 - E7 atoms/cm2 detection limit. It was impossible to get the contamination mapping information at such a low concentration. TRXRF has also been commonly used for the same purpose, but the detection limit is E10 - 12 atoms/cm2.- Particle measurement on a wafer. A spike recovery test of 20 and 50 nm Au nano-particle standard on Si wafer was performed, and individual nano-particle signals were detected by ICP-MS. The number of Au particle recovery was more than 85%. 200 nm SiO2 particles on Si wafer were also detected after the optimization of laser parameters.- Metallic contamination mapping on a wafer bevel. Several metallic contaminants were detected at certain spots of bevel, and E4 atoms of spot contamination could be detected.- Depth profile analysis of an ion implanted wafer. The laser ablation was performed on the same area repeatedly and the profile of As ion implanted wafer could be carried out in atmospheric pressure. This technique was also used for analysis of multi-layer film wafers.This paper explains how the new technique overcame the issues of the conventional LA-ICP-MS technique; why an entire wafer could not be analyzed and why an accurate quantitative analysis was impossible. Figure 1

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium.

To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an 'intracellular protein score' (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

The application of the non-quadratic regularization method for direction finding and adaptive signal processing

The paper considers the issues of detection, spatial localization, and isolation of individual radio signals from their additive mixture received by the elements of an antenna array with digital signal processing. To solve the above issues, the method of non-quadratic regularization is applied in the signal model, which takes into account signal delays on the antenna array elements to a first approximation. Using the Lagrange multiplier method, the adaptive estimates of signals were obtained and a direction finding function was constructed. The function maxima are identified with the directions to the radiation sources. An effective method for iterative solution of the problem in Lp space for any p values is proposed. The simulation results are set out for p = 1, demonstrating the possibility of detecting and estimating the parameters of weak signals. The signal level does not exceed the noise and interference level against the background of interfering intense radiation sources. The proposed method can significantly increase the effectiveness of solving problems of detecting and direction finding signals from stationary objects and tracking mobile objects both in the traditional single-site radio monitoring systems and passive location radio systems.

Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea.

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO2) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO2, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO2, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems.

Simulation of Space Charge Effects in Fourier Transform Quadrupole Ion Traps (FT-QITs).

We present ion dynamics simulations regarding the effect of space charge on the performance of Fourier transform quadrupole ion traps (FT-QITs) with special attention to signal stability, mass resolving power, and sensitivity. Ion trajectory simulations within an idealized QIT geometry are performed by applying a dedicated application (QITSim) using an in-house developed open simulation framework (IDSimF). Image current detection transients are generated by the application and are subsequently transformed into frequency spectra of ion secular motion. Such frequency spectra are the basis for the calculation of mass spectra in FT-QIT instruments. The simulation results are used to assess the extent of space charge induced effects regarding the analytical performance of FT-QIT systems. The simulation results for two Cl+ and seven Xe+ isotope ions exhibit diverse space charge induced phenomena. Most prominently, complete isotope signal fusion, quantitative individual signal suppression, and severe signal distortions are observed even at low absolute numbers of trapped ions. Furthermore, significant shifts of the secular oscillation frequencies occur, even when the signal shape appears to be mostly unaffected and when the frequency separation for trapped ions with different masses is large. This significantly limits the applicability of FT-QIT systems for high resolution mass spectrometry. An increase of the RF amplitude of the trapping field as well as increasing the extent of ion excitation partly mitigate these adverse space charge effects; nevertheless, the usable operational range of the simulated FT-QIT system for analytical applications remains rather narrow.

Leveraging a new data resource to define the response of Cryptococcus neoformans to environmental signals.

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of 5 signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-seq in quadruplicate, yielding 881 RNA-seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP. Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected data set will be a significant resource for the research community.

Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction

Preterm birth prediction is important in prenatal care; however, it remains a significant challenge due to the complex physiological mechanisms involved. This study aimed to explore the feasibility of phase synchronization of multiple oscillatory components across electrohysterography (EHG) and tocodynamometry (TOCO) signals to identify preterm births using advanced machine-learning techniques. Using an open-access EHG dataset, we first assessed the degree of phase synchronization of five specified frequency ranges from 0.08 to 5.0 Hz in three individual EHG signals by constructing two distinct sets of mean phase coherence: the inclusion or exclusion of TOCO signals. We then employed two machine-learning models, XGBoost and TabNet, to classify preterm and term delivery conditions and analyze the predictive potential of these features. The models’ performance was evaluated by considering varying lengths of time windows and the use of overlapping windows. Our results demonstrate the importance of lower-frequency EHG signals and synchronization patterns across the horizontal plane of the abdomen, particularly synchronization between the upper and lower regions of the uterus. Furthermore, we observed a distinctive pattern in the high-frequency band (1.0–2.2 Hz), emphasizing the important role of the lower horizontal regions with other sites in the synchronization process. Interestingly, our findings indicated that TOCO signals, while not substantially enhancing the overall prediction performance, contributed to slightly improved accuracy rates when combined with EHG signals. This study suggests the critical role of EHG signals and their intricate spatiotemporal patterns in predicting preterm birth, providing insights for the development of more accurate and efficient prediction models.

Traffic light detection using ensemble learning by boosting with color-based data augmentation

Recent advancements in deep neural networks have significantly improved the detection and recognition of traffic lights for advanced driver assistance systems (ADAS). Traditional methods often rely on identifying traffic light boxes and then recognizing individual signal lights, which can be problematic due to variations in bulb arrangements across different regions. To address this limitation, we propose a novel traffic light detection method that directly recognizes individual signal lights. Our two-stage approach combines data augmentation and ensemble learning to achieve high detection rates. By learning color characteristics from validation sets, we can effectively identify signal light candidates with a 97.26% accuracy rate. Subsequent classification results in a recognition accuracy of 98.6%, surpassing the performance of existing state-of-the-art traffic light detection algorithms. Code and dataset are available at https://github.com/981124/yolov7_traffic_light_detect.

Leveraging a new data resource to define the response of C. neoformans to environmental signals: How host-like signals drive gene expression and capsule expansion in Cryptococcus neoformans.

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of five signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-Seq in quadruplicate, yielding 881 RNA-Seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP (cAMP). Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected dataset will be a significant resource for the research community.

A novel approach to detecting epileptic patients: complex network-based EEG classification

Abstract Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosing epilepsy by analyzing signals are costly process. Various methods are used to classify epileptic seizures. However, the inadequacy of these approaches in classifying signals makes it difficult to diagnose epilepsy. Complex network science produces effective solutions for analyzing interrelated structures. Using methods based on complex network analysis, it is possible to EEG signals analyze the relationship between signals and perform a classification process. In this study proposes a novel approach for classifying epileptic seizures by utilizing complex network science. In addition, unlike the studies in the literature, classification processes were carried out with lower dimensional signals by using 1-s EEG signals instead of 23.6-s full-size EEG signals. Using the topological properties of the EEG signal converted into a complex network, the classification process has been performed with the Jaccard Index method. The success of the classification process with the Jaccard Index was evaluated using Accuracy, F1 Score, Recall, and K-Fold metrics. In the results obtained, the signals of individuals with epileptic seizures were separated with an accuracy rate of 98.15%.

Temporal dynamics of scent mark composition in field‐experimental lizard populations

Abstract Animal signals are dynamic traits that can undergo considerable spatial and temporal changes and that are influenced by factors such as age, health condition and interactions with both the abiotic and biotic environment. However, much of our understanding of signal changes throughout an individual's lifetime stems from cross‐sectional, often laboratory‐based, studies focused on visual and auditory signals. Longitudinal field investigations of temporal variation in chemical signals, especially in vertebrates, remain rare despite chemical communication being the most ubiquitous form of information exchange in the natural world. To remedy this, we conducted a unique, replicated field experiment to study the temporal signal dynamics in free‐living lizard populations on natural islands. Specifically, we collected scent marks from individually marked lizards across five populations during the spring of two consecutive years and analysed the lipophilic chemical composition of these scent marks. Our findings demonstrate that the overall scent mark composition of individual lizards changed over time, shifting consistently in both direction and magnitude from year to year among individuals and across replicate populations. Similar patterns were observed for the chemical richness and diversity of scent marks. Temporal variation in the relative proportions of three potentially socially relevant signalling compounds in lizard scent marks revealed a more complex pattern: α‐tocopherol remained stable over time, oleic acid decreased and the change in octadecanoic acid proportion was body size‐dependent. Together, our results provide novel insights into how individual vertebrate chemical signals may fluctuate across space and time. We discuss the potential causes of the observed temporal variability and its consequences for chemical signal evolution. Read the free Plain Language Summary for this article on the Journal blog.