sRNAfrag: a pipeline and suite of tools to analyze fragmentation in small RNA sequencing data.

Fragments derived from small RNAs such as small nucleolar RNAs are biologically relevant but remain poorly understood. To address this gap, we developed sRNAfrag, a modular and interoperable tool designed to standardize the quantification and analysis of small RNA fragmentation across various biotypes. The tool outputs a set of tables forming a relational database, allowing for an in-depth exploration of biologically complex events such as multi-mapping and RNA fragment stability across different cell types. In a benchmark test, sRNAfrag was able to identify established loci of mature microRNAs solely based on sequencing data. Furthermore, the 5' seed sequence could be rediscovered by utilizing a visualization approach primarily applied in multi-sequence-alignments. Utilizing the relational database outputs, we detected 1411 snoRNA fragment conservation events between two out of four eukaryotic species, providing an opportunity to explore motifs through evolutionary time and conserved fragmentation patterns. Additionally, the tool's interoperability with other bioinformatics tools like ViennaRNA amplifies its utility for customized analyses. We also introduce a novel loci-level variance-score which provides insights into the noise around peaks and demonstrates biological relevance by distinctly separating breast cancer and neuroblastoma cell lines after dimension reduction when applied to small nucleolar RNAs. Overall, sRNAfrag serves as a versatile foundation for advancing our understanding of small RNA fragments and offers a functional foundation to further small RNA research. Availability: https://github.com/kenminsoo/sRNAfrag.

A polarization scheme that resolves cross-peaks with transient absorption and eliminates diagonal peaks in 2D spectroscopy.

Two-dimensional (2D) optical spectroscopy contains cross-peaks that are helpful features for determining molecular structure and monitoring energy transfer, but they can be difficult to resolve from the much more intense diagonal peaks. Transient absorption (TA) spectra contain transitions similar to cross-peaks in 2D spectroscopy, but in most cases they are obscured by the bleach and stimulated emission peaks. We report a polarization scheme, <0°,0°,+θ2(t2),-θ2(t2)>, that can be easily implemented in the pump-probe beam geometry, used most frequently in 2D and TA spectroscopy. This scheme removes the diagonal peaks in 2D spectroscopies and the intense bleach/stimulated emission peaks in TA spectroscopies, thereby resolving the cross-peak features. At zero pump-probe delay, θ2 = 60° destructively interferes two Feynman paths, eliminating all signals generated by field interactions with four parallel transition dipoles, and the intense diagonal and bleach/stimulated emission peaks. At later delay times, θ2(t2) is adjusted to compensate for anisotropy caused by rotational diffusion. When implemented with TA spectroscopy or microscopy, the pump-probe spectrum is dominated by the cross-peak features. The local oscillator is also attenuated, which enhances the signal two times. This overlooked polarization scheme reduces spectral congestion by eliminating diagonal peaks in 2D spectra and enables TA spectroscopy to measure similar information given by cross-peaks in 2D spectroscopy.

Observation of In-Gap States in a Two-Dimensional CrI2/NbSe2 Heterostructure.

Low-dimensional magnetic structures coupled with superconductors are promising platforms for realizing Majorana zero modes, which have potential applications in topological quantum computing. Here, we report a two-dimensional (2D) magnetic-superconducting heterostructure consisting of single-layer chromium diiodide (CrI2) on a niobium diselenide (NbSe2) superconductor. Single-layer CrI2 nanosheets, which hold antiferromagnetic (AFM) ground states by our first-principles calculations, were epitaxially grown on the layered NbSe2 substrate. Using scanning tunneling microscopy/spectroscopy, we observed robust in-gap states spatially located at the edge of the nanosheets and defect-induced zero-energy peaks inside the CrI2 nanosheets. Magnetic-flux vortices induced by an external field exhibit broken 3-fold rotational symmetry of the pristine NbSe2 superconductor, implying the efficient modulation of the interfacial superconducting states by the epitaxial CrI2 layer. A phenomenological model suggests the existence of chiral edge states in a 2D AFM-superconducting hybrid system with an even Chern number, providing a qualitatively plausible understanding for our experimental observation.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Gyral peaks and patterns in human brains.

Cortical folding patterns are related to brain function, cognition, and behavior. Since the relationship has not been fully explained on a coarse scale, many efforts have been devoted to the identification of finer grained cortical landmarks, such as sulcal pits and gyral peaks, which were found to remain invariant across subjects and ages and the invariance may be related to gene mediated proto-map. However, gyral peaks were only investigated on macaque monkey brains, but not on human brains where the investigation is challenged due to high inter-individual variabilities. To this end, in this work, we successfully identified 96 gyral peaks both on the left and right hemispheres of human brains, respectively. These peaks are spatially consistent across individuals. Higher or sharper peaks are more consistent across subjects. Both structural and functional graph metrics of peaks are significantly different from other cortical regions, and more importantly, these nodal graph metrics are anti-correlated with the spatial consistency metrics within peaks. In addition, the distribution of peaks and various cortical anatomical, structural/functional connective features show hemispheric symmetry. These findings provide new clues to understanding the cortical landmarks, as well as their relationship with brain functions, cognition, behavior in both healthy and aberrant brains.

Model analysis and data validation of structured prevention and control interruptions of emerging infectious diseases.

The design of optimized non-pharmaceutical interventions (NPIs) is critical to the effective control of emergent outbreaks of infectious diseases such as SARS, A/H1N1 and COVID-19 and to ensure that numbers of hospitalized cases do not exceed the carrying capacity of medical resources. To address this issue, we formulated a classic SIR model to include a close contact tracing strategy and structured prevention and control interruptions (SPCIs). The impact of the timing of SPCIs on the maximum number of non-isolated infected individuals and on the duration of an infectious disease outside quarantined areas (i.e. implementing a dynamic zero-case policy) were analyzed numerically and theoretically. These analyses revealed that to minimize the maximum number of non-isolated infected individuals, the optimal time to initiate SPCIs is when they can control the peak value of a second rebound of the epidemic to be equal to the first peak value. More individuals may be infected at the peak of the second wave with a stronger intervention during SPCIs. The longer the duration of the intervention and the stronger the contact tracing intensity during SPCIs, the more effective they are in shortening the duration of an infectious disease outside quarantined areas. The dynamic evolution of the number of isolated and non-isolated individuals, including two peaks and long tail patterns, have been confirmed by various real data sets of multiple-wave COVID-19 epidemics in China. Our results provide important theoretical support for the adjustment of NPI strategies in relation to a given carrying capacity of medical resources.

Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index χ and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all χ > 0.1 except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all χ ≤ 0.001 except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both χ = 0.3182 for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.

RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data.

BACKGROUND: Massive amounts of data are produced by combining next-generation sequencing with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein-DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner. RESULTS: We here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors. CONCLUSION: We present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen . The documentation is available at: https://reg-gen.readthedocs.io.

Supervised topological data analysis for MALDI mass spectrometry imaging applications.

BACKGROUND: Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) displays significant potential for applications in cancer research, especially in tumor typing and subtyping. Lung cancer is the primary cause of tumor-related deaths, where the most lethal entities are adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Distinguishing between these two common subtypes is crucial for therapy decisions and successful patient management. RESULTS: We propose a new algebraic topological framework, which obtains intrinsic information from MALDI data and transforms it to reflect topological persistence. Our framework offers two main advantages. Firstly, topological persistence aids in distinguishing the signal from noise. Secondly, it compresses the MALDI data, saving storage space and optimizes computational time for subsequent classification tasks. We present an algorithm that efficiently implements our topological framework, relying on a single tuning parameter. Afterwards, logistic regression and random forest classifiers are employed on the extracted persistence features, thereby accomplishing an automated tumor (sub-)typing process. To demonstrate the competitiveness of our proposed framework, we conduct experiments on a real-world MALDI dataset using cross-validation. Furthermore, we showcase the effectiveness of the single denoising parameter by evaluating its performance on synthetic MALDI images with varying levels of noise. CONCLUSION: Our empirical experiments demonstrate that the proposed algebraic topological framework successfully captures and leverages the intrinsic spectral information from MALDI data, leading to competitive results in classifying lung cancer subtypes. Moreover, the framework's ability to be fine-tuned for denoising highlights its versatility and potential for enhancing data analysis in MALDI applications.

Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding.

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.

Genetic Influence on Gyral Peaks.

Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

Sample plug induced peak splitting in capillary electrophoresis studied using dual backscattered interferometry and fluorescence detection.

The appearance of unexpected peaks in capillary electrophoresis (CE) is common and can lengthen the time of method development as assay conditions and experimental parameters are varied to understand and mitigate the effects of the additional peaks. Additional peaks can arise when a single-analyte zone is split into multiple zones. Understanding the underlying mechanism of these phenomena, recognizing conditions that favor its presence, and knowing how to confirm and eliminate the effect are important for efficient method optimization. In this study, we examine how the overlap of analyte zones with the sample plug can lead to peak splitting. This is explored experimentally using dual detection CE, which enables both the sample plug and analyte zones to be independently and simultaneously measured from the same detection volume. Simulations performed via COMSOL Multiphysics confirm the origin of the splitting and help guide experiments to reduce and eliminate the effect. Our findings show that this peak splitting mechanism can arise in separations of both small and large molecules but is, especially, prevalent in separations of slowly migrating macromolecules. This effect is also more prevalent when using a short length-to-detector, as is commonly found in microfluidic applications. A simple diffusion-less model is introduced to develop strategies for reducing peak splitting that avoids modifying the apparatus, such as by lengthening the separation length, which can be difficult. Decreasing the sample plug length and slowing the electroosmotic flow can both reduce this effect, which is confirmed experimentally.

A simplified mid-infrared anti-resonant chalcogenide fiber with fewest resonant peaks.

High-power laser delivery in the mid-infrared via hollow-core fibers is attractive, but it is too difficult to be fabricated using chalcogenide glasses. Here, we designed a mid-infrared hollow-core anti-resonant chalcogenide fiber (HC-ARCF) with a simplified Kagome cladding micro-structure for the first time. Then, the fiber was firstly fabricated through a precision mechanical drilling and pressured fiber drawing method. Ultra-thin walls of 2µm in the fiber lead to the fewest resonance peaks in the 2-5µm among all reported HC-ARCFs. All the fundamental mode, the second-order mode, tube mode and node mode in the fiber were excited and observed at 1550 nm. The power and spectral properties of the core and cladding of HC-ARCF are studied for the first time. The fiber can deliver high-power of 4.84 W without damage with core-coupling, while the threshold of the node in the cladding is only 3.5 W. A broadening of the output spectrum from 1.96 to 2.41µm due to the high nonlinearity at the node was successfully observed under short-pulse laser pumping at 2µm. The potentials of the fiber used for mid-infrared high-power laser delivery via core, or nonlinear laser generation via node, were thus demonstrated.

Spatiotemporal effects of meteorological conditions on global influenza peaks.

BACKGROUND: Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes. OBJECTIVES: We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries. METHODS: Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons. RESULTS: Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus. CONCLUSION: Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.

Proteotypic peptides of hairs for the identification of common European domestic and wild animal species revealed by in-sample protein digestion and mass spectrometry analysis.

The aim of this work is to offer an alternative or complementary analytical tool to the time-consuming and expensive methods commonly used for the recognition of animal species according to their hair. The paper introduces a simple and fast way for species differentiation of animal hairs called in-sample digestion. A total of 10 European animal species, including cat, cow, common degu, dog, fallow deer, goat, horse, sika deer, rabbit, roe deer, and 17 different breeds of dogs were examined using specific tryptic cleavage directly in hair followed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography-electrospray ionization quadrupole time of flight. Principal component analysis was used for the subsequent mass spectrometric data evaluation. This novel approach demonstrates the ability to distinguish among individual animal species, which is supported by finding characteristic m/z values obtained by the mass spectrometry for each animal species. The approach was successfully tested on two "blind" samples. On the other hand, the attempt to distinguish among hairs of different dog breeds has not been successful due to the very similar protein composition and their amino acid sequences.

Power Transformers OLTC Condition Monitoring Based on Feature Extraction from Vibro-Acoustic Signals: Main Peaks and Euclidean Distance.

The detection of On-Load Tap-Changer (OLTC) faults at an early stage plays a significant role in the maintenance of power transformers, which is the most strategic component of the power network substations. Among the OLTC fault detection methods, vibro-acoustic signal analysis is known as a performant approach with the ability to detect many faults of different types. Extracting the characteristic features from the measured vibro-acoustic signal envelopes is a promising approach to precisely diagnose OLTC faults. The present research work is focused on developing a methodology to detect, locate, and track changes in on-line monitored vibro-acoustic signal envelopes based on the main peaks extraction and Euclidean distance analysis. OLTC monitoring systems have been installed on power transformers in services which allowed the recording of a rich dataset of vibro-acoustic signal envelopes in real time. The proposed approach was applied on six different datasets and a detailed analysis is reported. The results demonstrate the capability of the proposed approach in recognizing, following, and localizing the faults that cause changes in the vibro-acoustic signal envelopes over time.

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints.

The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have proven ineffective in accurately identifying non-stopping points caused by trailing or round trips. To address this issue, this paper proposes a novel density peak clustering algorithm based on coherence distance, incorporating temporal and entropy constraints, referred to as the two-step DPCC-TE. The proposed algorithm introduces a coherence index to integrate spatial and temporal features, and imposes temporal and entropy constraints on the clusters to mitigate local density increase caused by slow-moving points and back-and-forth movements. Moreover, to address the issue of interactions between subclusters after one-step clustering, a two-step clustering algorithm is proposed based on the DPCC-TE algorithm. Experimental results demonstrate that the proposed two-step clustering algorithm outperforms the DBSCAN-TE and one-step DPCC-TE methods, and achieves an accuracy of 95.49% in identifying stopping points.

Local Peaks Search Method for Solving Lamb Waves' Dispersion Equation of Laminated Structures and the Application.

To study the acoustic characteristics of sound scattered from laminated structures such as elastic plates and shells, it is usually required to solve the Lamb waves' dispersion equations. Many traditional root-finding methods such as bisection, the Newton-Raphson method, and the Muller method are not able to tackle the problem completely. A simple but powerful method named local peaks search (LPS) is proposed to overcome their drawbacks. Firstly, the non-zero part of the dispersion equation is defined as the dispersion function, and its reciprocal is used to transform the zeros (i.e., roots) into local peaks. Secondly, the chosen complex domain is discretized, and the coarse local domains where the local peaks exist are determined by the direct search method globally. Thirdly, the Muller method is applied to obtain the refined locations of local peaks. Lastly, in order to refine the results, a hierarchical scheme is designed and the iteration of the above procedures is implemented; the error is set to be 10-16 as the stop criteria. The accuracy of the LPS method is validated by comparing it with the bisection method for the problem of elastic plates in the vacuum. The acoustic echo structures are analyzed experimentally. By computation of Lamb waves' phase velocity, the critical angles are derived numerically and compared with the results acquired by an experiment using monostatic sound transducers. In this way, it is validated that the elastic scattered wave components are the highlights shown in the time-angle figure. Furthermore, the work can be applied for non-destructive testing, especially underwater structural health monitoring.

Unique Features of the m6A Methylome and Its Response to Salt Stress in the Roots of Sugar Beet (Beta vulgaris).

Salt is one of the most important environmental factors in crop growth and development. N6-methyladenosine (m6A) is an epigenetic modification that regulates plant-environment interaction at transcriptional and translational levels. Sugar beet is a salt-tolerant sugar-yielding crop, but how m6A modification affects its response to salt stress remains unknown. In this study, m6A-seq was used to explore the role of m6A modification in response to salt stress in sugar beet (Beta vulgaris). Transcriptome-wide m6A methylation profiles and physiological responses to high salinity were investigated in beet roots. After treatment with 300 mM NaCl, the activities of peroxidase and catalase, the root activity, and the contents of Na+, K+, and Ca2+ in the roots were significantly affected by salt stress. Compared with the control plants, 6904 differentially expressed genes (DEGs) and 566 differentially methylated peaks (DMPs) were identified. Association analysis revealed that 243 DEGs contained DMP, and 80% of these DEGs had expression patterns that were negatively correlated with the extent of m6A modification. Further analysis verified that m6A methylation may regulate the expression of some genes by controlling their mRNA stability. Functional analysis revealed that m6A modifications primarily affect the expression of genes involved in energy metabolism, transport, signal transduction, transcription factors, and cell wall organization. This study provides evidence that a post-transcriptional regulatory mechanism mediates gene expression during salt stress by affecting the stability of mRNA in the root.

Electrodeposition of Polyaniline on Tantalum: Redox Behavior, Morphology and Capacitive Properties.

Polyaniline (PANI) is among the most widely studied conducting polymers due to its potential technological applications in various fields. Recently, PANI-based hybrid materials have played an important role in the development of energy storage and conversion systems. The aim of the present work is the investigation of the simultaneous electrochemical growth of PANI and Ta2O5 on the Ta substrate and the characterization of the morphology, redox behavior and pseudocapacitive properties of the resulting micro- or nanostructured composite thin films. A well-adherent conductive Ta2O5-PANI composite film was first formed using cyclic voltammetry on Ta that facilitates the on-top electrodeposition of single PANI via an autocatalytic mechanism. The electrochemical characterization of the Ta|Ta2O5-PANI|PANI electrodes reveals unique redox properties of PANI not shown previously upon using PANI electrodeposition on Ta. Scanning electron microscopy shows that the morphology of the electrodeposited films comprises nano- or microspheres that may develop into nano- or microrods when the polymerization proceeds. Preliminary evaluation of the capacitive properties of the Ta|Ta2O5-PANI|PANI electrode shows adequately high specific capacitance values as high as 1130 F g-1 (at 9.2 mA cm-2), depending on the electrochemical parameters, as well as adequate stability (~80% retention after 100 cycles), indicating their potential application as energy storage devices.