Individual Signals Research Articles

Challenges in the determination of reactive oxygen species evolving during membrane water electrolysis for in situ ozone production

The treatment of ultrapure water with electrochemically produced O3 is a common means for disinfection yet leads to the formation of a variety of reactive oxygen species (ROS). The present study draws a comprehensive comparison between three commonly used photometric and fluorometric assays for ROS analysis and quantifies the individual signal responses for dissolved O3, ·OH and H2O2, respectively, to account for cross-sensitivities. By calibrating all combinations of assays and analytes, we developed a quantification procedure to reliably determine the actual ROS composition in ultrapure water environments for different operation conditions of a membrane water electrolyzer with PbO2 anodes down to concentrations of 0.97 μg L−1. While the ·OH formation rate can be described linearly over the observed current density range, substantial O3 evolution is only found for current densities of 0.75 A cm−2 and above (up to 3.7 μmol h−1 for J = 1.25 A cm−2). The formation of H2O2 is only observed when an organic carbon source is introduced into the solution. We further quantify the interference of H2O2 with the reading of the oxidation-reduction potential as a common water parameter and elaborate on its validity to monitor the peroxone process when both H2O2 and O3 are present simultaneously.

1-Ethyl-3-methylimidazolium Acetate as a Reactive Solvent for Elemental Sulfur and Poly(sulfur nitride).

We investigate the reactive dissolution process of poly(sulfur nitride) (SN)x in the ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate [EMIm][OAc] in comparison to the process of elemental sulfur in the same IL. It has been known from the literature that during the reaction of S8 with [EMIm][OAc], the respective thione is formed via a radical mechanism. Here, we present new results on the kinetics of the formation of the respective imidazole thione (EMImS) via the hexasulfur dianion [S6]2- and the trisulfur radical anion [S3]•-. We can show that [S6]2- is formed first, which dissociates then to [S3]•-. Also, long-term stable radicals occur, which are necessary side products provided in a reaction scheme. During the reaction of [EMIm][OAc] with (SN)x chains, two further products can be identified, one of which is the corresponding imine. The reactions are followed by time-resolved NMR spectroscopic methods that showed the corresponding product distributions and allowed the assignment of the individual signals. In addition, continuous-wave (CW) EPR and UV/vis spectroscopic measurements show the course of the reactions. Another significant difference in both reactions is the formation of a long-term stable radical in the sulfur-IL system, which remains active over 35 days, while for the (SN)x-IL system, we can determine a radical species only with the spin trap 5,5-dimethyl-1-pyrrolin-N-oxide, which indicates the existence of short-living radicals. Since the molecular dynamics are restricted based on the EPR spectra, these radicals must be large.

Unsymmetric Chiral Ligands for Large Metallo-Macrocycles: Selectivity of Orientational Self-Sorting.

Nature uses various chiral and unsymmetric building blocks to form substantial and complex supramolecular assemblies. In contrast, the majority of organic ligands used in metallosupramolecular chemistry are symmetric and achiral. Here we extend the group of unsymmetric chiral bile acids used as a scaffold for organic bispyridyl ligands by employing chenodeoxycholic acid (CDCA), an epimer of the previously used ursodeoxycholic acid (UDCA). The epimerism, flexibility, and bulkiness of the ligands leads to large structural differences in coordination products upon reaction with Pd(NO3)2. The UDCA-bispyridyl ligand self-assembles quantitatively into a single crown-like Pd3L6 complex, whereas the CDCA ligand provides a mixture of coordination complexes of general formula PdnL2n, i.e., Pd2L4, Pd3L6, Pd4L8, Pd5L10, and even Pd6L12 containing an impressive 120 chiral centers. The coordination products were studied by a combination of analytical methods, with ion-mobility mass spectrometry (IM-MS) providing valuable details on their structure and allowed an effective separation of m/z 1461 to individual signals according to the arrival time distribution, thereby revealing four different ions of [Pd3L6(NO3)3]3+, [Pd4L8(NO3)4]4+, [Pd5L10(NO3)5]5+, and [Pd6L12(NO3)6]6+. The structures of all the complexes were modelled using DFT calculations. Finally, the challenges and conclusions in determining the specific structural identity of these unsymmetric species are discussed.

A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

Active Cluster Wake Mixing

In recent years, the relevance of the interaction between neighboring wind farms has grown steadily. As one farm extracts energy from the wind, a downstream one can systematically experience lower wind speeds which threatens the economic viability of the farm. Significant progress has been made in understanding these farm-farm wake interactions, but we still lack methodologies to mitigate their undesired effects. In this study, we introduce Active Cluster Wake Mixing (ACWM). This novel method aims to accelerate the recovery of the cluster wake using dynamic control actions: By exciting the thrust of the individual turbines depending on their relative location, we generate non-uniform patterns of energy extraction. Phase offsets between the individual excitation signals propagate these regions through the wind farm. This results in large-scale velocity gradients inside the farm, which also affect the flow in the cluster wake region. An in-depth exploration and optimization of ACWM requires significant computational effort. Therefore, we compare three different wind farm modeling approaches in Large Eddy Simulations (LES) that differ in their computational costs regarding their suitability for further exploration of ACWM. For this purpose, we use an unoptimized ACWM scheme with two different excitation frequencies. For the first time ever we successfully show that ACWM manipulates the flow inside the wind farm with favorable effects on the wake velocity. We also demonstrate that the modeling of cluster wakes is challenging and has a significant effect on the potential gain.

Individual NMDA receptor GluN2 subunit signaling domains differentially regulate the postnatal maturation of hippocampal excitatory synaptic transmission and plasticity but not dendritic morphology.

N-methyl-d-aspartate receptors (NMDARs) at hippocampal excitatory synapses undergo a late postnatal shift in subunit composition, from an initial prevalence of GluN2B subunit incorporation to a later predominance of GluN2A. This GluN2B to GluN2A shift alters NMDAR calcium conductance dynamics and intracellular molecular signaling that are individually regulated by distinct GluN2 signaling domains and temporally align with developmental alterations in dendritic and synaptic plasticity. However, the impacts of individual GluN2B to GluN2A signaling domains on neuronal development remain unknown. Ionotropic and intracellular signaling domains of GluN2 subunits were separated by creating chimeric GluN2 subunits that were expressed in two transgenic mouse lines. Western blot and immunoprecipitation revealed that roughly one third of native synaptic NMDARs were replaced by transformed NMDARs without altering total synaptic NMDAR content. Schaffer collateral synaptic strength was transiently increased in acutely prepared hippocampal slices at just over 3 weeks of age in animals overexpressing the GluN2B carboxy terminus. Long-term potentiation (LTP) induction following lower frequency stimulation was regulated by GluN2 ionotropic signaling domains in an age-dependent manner and LTP maintenance was enhanced by overexpression of the GluN2B CTD in mature animals. After higher frequency stimulation, the induction and maintenance of LTP were increased in young adult animals overexpressing the GluN2B ionotropic signaling domains but reduced in juveniles just over 3 weeks of age. Confocal imaging of green fluorescent protein (GFP)- labeled CA1 pyramidal neurons revealed no alterations in dendritic morphology or spine density in mice expressing chimeric GluN2 subunits. These results illustrate how individual GluN2 subunit signaling domains do or do not control physiological and morphological development of hippocampal excitatory neurons and better clarify the neurobiological factors that govern hippocampal maturation. SIGNIFICANCE STATEMENT: A developmental reduction in the magnitude of hippocampal long-term synaptic potentiation (LTP) and a concomitant improvement in spatial maze performance coincide with greater incorporation of GluN2A subunits into synaptic NMDARs. Corroborating our prior discovery that overexpression of GluN2A-type ionotropic signaling domains enables context-based navigation in immature mice, GluN2A-type ionotropic signaling domain overexpression reduces LTP induction threshold and magnitude in immature mice. Also, we previously found that GluN2B carboxy terminal domain (CTD) overexpression enhances long-term spatial memory in mature mice and now report that the GluN2B CTD is associated with greater amplitude of LTP after induction in mature mice. Thus, the late postnatal maturation of context encoding likely relies on a shift toward GluN2A-type ionotropic signaling and a reduction in the threshold to induce LTP while memory consolidation and LTP maintenance are regulated by GluN2B subunit CTD signaling.

Sound feeding signal for gestating sows: Evaluation of an individual learning strategy and its beneficial effects in groups.

Competition for feed in a group of gestating sows leads to aggression around feeding stations, which has a negative impact on their welfare. This study investigates the potential of teaching gestating sows an individual sound signal to reduce aggression resulting from competition for feed access, and thus improve their welfare in a group. A total of 32 sows were used. In a test room, “learning” sows (n = 16) went through 4 individual learning phases (27 days in total) to associate the individual sound signal with an invitation to feed from a one-way feeding station and to discriminate this individual sound signal from other unknown sound signals. After the learning phases, sows were subjected to a 3-day evaluation phase in groups of 4 sows. The "naive" sows (n = 16) were also introduced to the test room individually for 18 days, and in groups of 4 for 3 days without following the learning procedure. Learning sows correctly responded to 100% of their individual sound signal after only 8 days of individual learning, suggesting that they successfully associated the sound signal with feed access. Distinguishing between different sounds was harder as shown by only 18.8% of success after an unknown sound emission at the end of the individual learning phases. Naïve sows reduced the time spent in the feeder compared to learning sows (P < 0.001). On the second day of the group phase, learning sows were less aggressive than naïve sows (P < 0.05). Compared to high-ranking sows, low-ranking sows displayed a reduced number of spontaneous approaches to the feeder during the last individual learning phase (P < 0.001), and higher success rates in the group phase (P < 0.05). The study suggests that, for group-housed sows fed by an individual feeder, teaching sows an individual sound signal can modify their feeding and social behaviors, enhancing their overall well-being during feeding time. Furthermore, the results suggest that this individual learning may be particularly beneficial for low-ranking sows.

How to see stress in chickens: On the way to a Stressed Chicken Scale

For many species, scales are used to classify discomfort and stress (e.g., facial expression/pain scales). Although a significant number of vertebrates used for scientific purposes are chickens, a corresponding scale for birds has not yet been established. We developed a Stressed Chicken Scale (SCS) to investigate whether it is possible to assess discomfort in a chicken by its body posture. A selective review with additional handsearch was conducted to find suitable parameters for visual stress assessment. Seven potential body signals were identified: Tail and head position, eye closure, beak opening, leg and wing position, and plumage fullness (ruffled or fluffed up feathers). The SCS was evaluated for interobserver reliability with veterinary students (n = 20), using randomized pictures of stressed and unstressed chickens in lateral view (n = 80). Observers were able to identify the body signals on the pictures after a brief training session. Agreement scores for interobserver agreement ranged from κ = 0.31 (fair agreement) for eye closure to κ = 0.78 (substantial agreement) for beak opening. We found that the number of body signals displayed in a stressed expression had an impact on observers' overall assessment of the chickens, for example, chickens were more likely to be rated as stressed if more than 4 signals indicative of stress were present. We conclude that the 7 individual body signals can be used to identify discomfort in chickens.

Multi-Parameter Analysis of Nanoplastics in Flow: Taking Advantage of High Sensitivity and Time Resolution Enabled by Stimulated Raman Scattering.

Here, we demonstrate the detection of nanoplastics (NPLs) in flow with stimulated Raman scattering (SRS) for the first time. NPLs (plastic particles <1000 nm) have recently been detected in different environmental samples and personal care products. However, their characterization is still an analytical challenge. Multiple parameters, including size, chemical composition, and concentration (particle number and mass), need to be determined. In an earlier paper, online field flow fractionation (FFF)-Raman analysis with optical trapping was shown to be a promising tool for the detection of particles in this size range. SRS, which is based on the enhancement of a vibrational transition by the matching energy difference of two laser beams, would allow for much more sensitive detection and, hence, much shorter acquisition times compared to spontaneous Raman microspectroscopy (RM). Here, we show the applicability of SRS for the flow-based analysis of individual, untrapped NPLs. It was possible to detect polyethylene (PE), polystyrene (PS), and poly(methyl methacrylate) (PMMA) beads with diameters of 100-5000 nm. The high time resolution of 60.5 μs allows us to detect individual signals per particle and to correlate the number of detected particles to the injected mass concentration. Furthermore, due to the high time resolution, optically trapped beads could be distinguished from untrapped beads by their peak shapes. The SRS wavenumber settings add chemical selectivity to the measurement. Whereas optical trapping is necessary for the flow-based detection of particles by spontaneous RM, the current study demonstrates that SRS can detect particles in a flow without trapping. Additionally, the mean particle size could be estimated using the mean width (duration) and intensity of the SRS signals.

Not All Arms of IgM Are Equal: Following Hinge-Directed Cleavage by Online Native SEC-Orbitrap-Based CDMS.

Immunoglobulins M (IgM) are key natural antibodies produced initially in humoral immune response. Due to their large molecular weights and extensive glycosylation loads, IgMs represent a challenging target for conventional mass analysis. Charge detection mass spectrometry (CDMS) may provide a unique approach to tackle heterogeneous IgM assemblies, although this technique can be quite laborious and technically challenging. Here, we describe the use of online size exclusion chromatography (SEC) to automate buffer exchange and sample introduction, and demonstrate its adaptability with Orbitrap-based CDMS. We discuss optimal experimental parameters for online SEC-CDMS experiments, including ion activation, choice of column, and resolution. Using this approach, CDMS histograms containing hundreds of individual ion signals can be obtained in as little as 5 min from single injections of <1 μg of sample. To demonstrate the unique utility of online SEC-CDMS, we performed real-time kinetic monitoring of pentameric IgM digestion by the protease IgMBRAZOR, which cleaves specifically in the hinge region of IgM. Several digestion intermediates corresponding to processive losses of F(ab')2 subunits could be mass-resolved and identified by SEC-CDMS. Interestingly, we find that for the J-chain linked IgM pentamer, cleavage of one of the F(ab')2 subunits is much slower than the other four F(ab')2 subunits, which we attribute to the symmetry-breaking interactions of the J-chain within the pentameric IgM structure. The online SEC-CDMS methodologies described here open new avenues into the higher throughput automated analysis of heterogeneous, high-mass protein assemblies by CDMS.

Hepatotoxicity assessment of innovative nutritional supplements based on olive-oil formulations enriched with natural antioxidants.

This study focuses on the assessment of extra virgin olive-oil and olive fruit-based formulations enriched with natural antioxidants as potential nutritional supplements for alleviating symptoms and long-term consequences of illnesses whose molecular pathophysiology is affected by oxidative stress and inflammation, such as Alzheimer's disease (AD). Besides evaluating cell viability and proliferation capacity of human hepatocellular carcinoma HepG2 cells exposed to formulations in culture, hepatotoxicity was also considered as an additional safety measure using quantitative real-time PCR on RNA samples isolated from the cell cultures and applying approaches of targeted molecular analysis to uncover potential pathway effects through gene expression profiling. Furthermore, the formulations investigated in this work contrast the addition of natural extract with chemical forms and evaluate the antioxidant delivery mode on cell toxicity. The results indicate minimal cellular toxicity and a significant beneficial impact on metabolic molecular pathways in HepG2 cell cultures, thus paving the way for innovative therapeutic strategies using olive-oil and antioxidants in dietary supplements to minimize the long-term effects of oxidative stress and inflammatory signals in individuals being suffered by disorders like AD. Overall, the experimental design and the data obtained support the notion of applying innovative molecular methodologies and research techniques to evidently advance the delivery, as well as the scientific impact and validation of nutritional supplements and dietary products to improve public health and healthcare outcomes.

Dual Sensing Signal Decoupling Based on Thermoelectric Polymer Aerogels for Precise Temperature and Pressure Recognition

AbstractThe capability to emulate skin‐like temperature and pressure sensing is fundamental for next‐generation artificial intelligence products. However, detecting temperature and pressure simultaneously with a single sensor without signal interference is challenging. Herein, a novel PCC aerogel sensor composed of PEDOT:PSS, CNTs, and CNF via directional freezing technology is developed. The PCC sensor can decouple temperature and pressure stimuli into individual voltage and resistance signals. It exhibits high‐precision temperature sensing capabilities, boasting an exceptionally high Seebeck coefficient of 30.4 µV K‐1 and the ability to detect temperature variations as low as 0.1 K. PCC sensors show excellent sensitivity and fast response times for detecting static and dynamic pressures, as well as high stability after 1000 cycles. Its maximum pressure sensitivity can reach 159.1% kPa−1, and the lowest detection limit is 10 Pa. Additionally, its excellent thermoelectric properties also enable to generating thermopower from human skin for self‐powered pressure sensing. A 3×3 PCC sensor array has been proposed to simulate the unique features of human skin in temperature and pressure recognition. This work provides a scalable manufacturing strategy for multi‐functional tactile sensors.

Abstract PO5-25-03: Immune cell infiltrate profiles in primary Triple Negative Breast Cancer and co-relation with treatment response and survival outcomes

Abstract Introduction Tumor microenvironment (TME) in breast cancer is a heterogeneous landscape with multiple cell types, each modulating the tumor progression and the disease prognosis. These cell types include lymphocytes, RBCs, stromal cells, macrophages, cancer-associated fibroblasts (CAFs), and cancer stem cells. Each cell type in the TME influences tumor progression and clinical outcomes for patients in a good or bad way, depending on the interaction with tumor cells. Tumor-infiltrating lymphocytes (TILs) are shown to be prognostic for therapy response in many cancers in the western cohorts. Our recent study showed that a higher proportion of Indian TNBC patients had intra-tumoral TILs compared to Western cohorts, and these are predictive of better response to NACT and longer disease-free survival in patients, making it a distinct finding from Western reports. In the follow-up study, we are characterizing infiltrating immune cell subtypes and their comparative association with treatment response in TNBC patients. Results from preliminary analysis and comparative analysis between responders and non-responders will be presented here. We speculate that the pro- and anti-tumorigenic immune cell population and its spatial information would help us understand TME and its precise role in response to therapy. Methods A retrospective cohort of breast cancer patients from a single one-surgeon breast cancer clinic with a uniform treatment strategy was evaluated for this study. FFPE primary tissue samples with variable TILs scores (moderate to high), taken from patients who showed complete response to the NACT treatment (n=6) and non-responding group (n=8) determined by H&amp;E stained slides from our cohort, were further stained for immune cells (Pan-B cell, Pan-T cells, Pan-macrophages) and T cells (CD4; Helper T cell marker, CD8; Cytotoxic T cell marker), FOXP; Regularly CD4+ cell marker) and PanCK (Pan-tumor marker) and counterstained with DAPI for multiplex imaging. Multispectral images for these slides were obtained using the Leica Aperio Versa system. Parameters such as individual cell types of identification and scoring, spatial proximity to tumor cells, and individual cell types will be determined by HALO software using the High plex module. Results A total of 14 TNBC samples were stained for the T-cell subtypes panel and Immune cell subtype s panel to determine the TIME. DAPI stain was used for nuclear counting based the parameters such as threshold intensity, nuclear size, roundness, and aggressiveness. The cytoplasmic size was selected as 2um beyond the outside nuclear boundary. Using Halo-Indica software, around 50000-300000 cells/tissue were evaluated across all samples from mIF whole slide scans of biopsy samples. Cell count was validated by cross-checking individual ROIs and manual cell count using Fiji. A Highplex module was used to identify individual signal positivity for each marker based on threshold intensity and cell completeness percent. Preliminary data showed a higher proportion of CD8+ cells and macrophages population are spatially closer to the tumor cells in Responders compared to non-responders. Conclusion Immune profiles are distinct among responding and non-responding patients in the Indian TNBC cohort. The association of these profiles with long-term survival in patients and various clinical and pathological parameters will help us determine the clinical relevance of these profiles and identify distinct immune subgroups as prognostic in Indian TNBCs. Citation Format: Pooja Vaid, Devaki Kelkar, LS Shashidhara, C B Koppiker, Madhura Kulkarni. Immune cell infiltrate profiles in primary Triple Negative Breast Cancer and co-relation with treatment response and survival outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-25-03.

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network

Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either make it easier to understand or increase its quality so that it may be used in subsequent processing. Speech separation on overlapping audio data is important for many speech-processing tasks, including natural language processing, automatic speech recognition, and intelligent personal assistants. New speech separation algorithms are often built on a deep neural network (DNN) structure, which seeks to learn the complex relationship between the speech mixture and any specific speech source of interest. DNN-based speech separation algorithms outperform conventional statistics-based methods, although they typically need a lot of processing and/or a larger model size. This study presents a new end-to-end speech separation network called ESC-MASD-Net (effective speaker separation through convolutional multi-view attention and SuDoRM-RF network), which has relatively fewer model parameters compared with the state-of-the-art speech separation architectures. The network is partly inspired by the SuDoRM-RF++ network, which uses multiple time-resolution features with downsampling and resampling for effective speech separation. ESC-MASD-Net incorporates the multi-view attention and residual conformer modules into SuDoRM-RF++. Additionally, the U-Convolutional block in ESC-MASD-Net is refined with a conformer layer. Experiments conducted on the WHAM! dataset show that ESC-MASD-Net outperforms SuDoRM-RF++ significantly in the SI-SDRi metric. Furthermore, the use of the conformer layer has also improved the performance of ESC-MASD-Net.

Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period

Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period

Decoding Stress with Computer Vision-Based Approach Using Audio Signals for Psychological Event Identification during COVID-19

Interpreting psychological events can be costly and quite complex. It is simple to translate such experiences into a person's spoken and nonverbal cues. The suggested model investigates a computer vision-based method for using an individual's audio signal to identify stressful psychological events. Different people's input speech signals are recorded and compared to the common questionnaire. A series of inquiries pertaining to the second stage of COVID-19 events are included in the questionnaire set. Through additional processing, these speech signals are converted into frequency components by means of the Fast Fourier transformation (FFT) method. A long short-term memory module processes each frequency component and produces temporal information from each frequency band. The features of speech signals are extracted into the temporal frames by this module. The VGG 16 algorithm is used to further classify each temporal frame into stress and un-stress classes. A classifier with 16 layers of architecture is called VGG 16. A feed-forward convolutional neural network called VGG 16 is used to divide the vast array of speech signal features into classes: stressed and unstressed. The proposed model attempts to recognize speech signals as stress indicators. A standard set of questionnaires with a series of interrogation-style questions has been used to develop the stress symptoms in an individual's mind. The audio signals generated by each person's responses are recorded and subsequently analyzed for stress and un-stress classes. The proposed model was able to identify stress in speech signals with 98% accuracy. The time and cost implications of the suggested model are relevant. Medical research is typically costly and time-consuming.LSTM; VGG 16; CNN model; data preprocessing; speech signal.

Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects.

Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug-target-adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data-driven biological plausibility assessment in the context of challenging post-marketing DDI surveillance.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

A decomposition algorithm for streak camera data

We describe a novel reconstruction algorithm for time-resolved images obtained using a streak camera. This algorithm operates by decomposing a recorded image into a set of individual photoelectron-induced signals, thereby providing a powerful method for streak camera image reconstruction. This deconstruction allows for a standard statistical analysis of the resulting image. We demonstrate the effectiveness of this technique by analyzing the temporal spacing between the emitted fs-long laser pulse and its succeeding first, second, and third reflections within a thick glass captured by a streak image.