Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

PGFinder, an Open-Source Software for Peptidoglycomics: The Structural Analysis of Bacterial Peptidoglycan by LC-MS.

Peptidoglycan is a major and essential component of the bacterial cell envelope that confers cell shape and provides protection against internal osmotic pressure. This complex macromolecule is made of glycan strands cross-linked by short peptides, and its structure is continually modified throughout growth via a process referred to as "remodeling." Peptidoglycan remodeling allows cells to grow, adapt to their environment, and release fragments that can act as signaling molecules during host-pathogen interactions. Preparing peptidoglycan samples for structural analysis first requires purification of the peptidoglycan sacculus, followed by its enzymatic digestion into disaccharide peptides (muropeptides). These muropeptides can then be characterized by liquid chromatography coupled mass spectrometry (LC-MS) and used to infer the structure of intact peptidoglycan sacculi. Due to the presence of unusual crosslinks, noncanonical amino acids, and amino sugars, the analysis of peptidoglycan LC-MS datasets cannot be handled by traditional proteomics software. In this chapter, we describe a protocol to perform the analysis of peptidoglycan LC-MS datasets using the open-source software PGFinder. We provide a step-by-step strategy to deconvolute data from various mass spectrometry instruments, generate muropeptide databases, perform a PGFinder search, and process the data output.

Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls.

Tandem mass spectrometry (MS/MS) facilitates the rapid identification of posttranslational modifications (PTMs), which play a pivotal role in regulating numerous biological processes. This chapter explores recent advancements that expand the types of detectable PTMs and enhance the speed of the PTM searches. We also delve into computational challenges associated with searching for a multitude of PTMs simultaneously. The latter section introduces an automated procedure to identify an extensive range of PTMs using MODplus, a free PTM analysis software tool. We guide the reader through the preparation of the modification search, the determination of optional search parameters, the execution of the search, and the analysis of results, exemplified by a case study using specific MS/MS dataset.

Recent Advances on Jamming and Spoofing Detection in GNSS.

Increased interest in the development and integration of navigation and positioning services into a wide range of receivers makes them susceptible to a variety of security attacks such as Global Navigation Satellite Systems (GNSS) jamming and spoofing attacks. The availability of low-cost devices including software-defined radios (SDRs) provides a wide accessibility of affordable platforms that can be used to perform these attacks. Early detection of jamming and spoofing interferences is essential for mitigation and avoidance of service degradation. For these reasons, the development of efficient detection methods has become an important research topic and a number of effective methods has been reported in the literature. This survey offers the reader a comprehensive and systematic review of methods for detection of GNSS jamming and spoofing interferences. The categorization and classification of selected methods according to specific parameters and features is provided with a focus on recent advances in the field. Although many different detection methods have been reported, significant research efforts toward developing new and more efficient methods remain ongoing. These efforts are driven by the rapid development and increased number of attacks that pose high-security risks. The presented review of GNSS jamming and spoofing detection methods may be used for the selection of the most appropriate solution for specific purposes and constraints and also to provide a reference for future research.

Traffic Feature Selection and Distributed Denial of Service Attack Detection in Software-Defined Networks Based on Machine Learning.

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method's efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy.

Nitrate and ammonium removal in constructed wetlands: Experimental insights and zero-dimensional numerical modeling.

Constructed wetlands (CWs) have emerged as effective wastewater treatment systems, mimicked natural wetland processes but engineered for enhanced pollutant removal efficiency. Ammonium (NH4+) and nitrate (NO3-) are among common pollutants in wastewater, posing significant environmental and health risks. The primary objective of this study is to compares the performance of CWs using gravel and three sizes of natural pumice, along with phragmites australis, in horizontal and horizontal-vertical CWs for nitrate and ammonium removal in the complementary treatment of domestic wastewater. Additionally, the study aims to develop and validate a numerical model using MATLAB software to predict the removal efficiency of these pollutants, thereby contributing to the optimization of CW design and operation. The model operates as a zero-dimensional model based on the law of mass conservation, treating the wetland as a completely mixed reactor, thus avoiding complexities associated with solute movement in porous media. It accurately could predict removal efficiency of chemical, biochemical, and biological indicators while considering active and passive absorption mechanisms by plant uptake. Notably, the determination of coefficients in the model equation does not rely on potentially error-prone laboratory measurements due to sampling issues. Instead, optimization techniques alongside field data robustly estimate these coefficients, ensuring reliability and practicality. Results indicate that higher pollutant concentrations increase reaction rates, particularly enhancing CW efficiency in ammonium removal. Pumice, especially in larger sizes, exhibits superior absorption due to increased porosity and surface area. Overall, the model accurately predicts nitrates concentrations, demonstrating its potential for CW performance optimization and confirming the significance of effective pollutant removal strategies in wastewater treatment.

A novel strategy for improving bowel preparation based on social software-enhanced education: A prospective, multicenter, randomized controlled study.

BACKGROUND AND AIM: The compliance and timeliness of oral laxatives have always been the key factors restricting bowel preparation (BP). We have constructed a novel enhanced-educational content and process based on social software (SS) for BP to optimize these issues. METHODS: A multicenter, prospective, randomized controlled study was conducted at 13 hospitals in China from December 2019 to December 2020. A total of 1774 enrollees received standard instructions for BP and were randomly assigned (1:1) to the SS group (SSG) that received a smartphone-based enhanced-education strategy starting 4 h before colonoscopy or the control group (CG). RESULTS: A total of 3034 consecutive outpatient colonoscopy patients were assessed for eligibility, and 1774 were enrolled and randomly assigned. Ultimately, data from 1747 (SSG vs CG: 875 vs 872) enrollees were collected. The BP adequacy rate was 92.22% (95% CI: 90.46-93.98) in the SSG vs 88.05% (95% CI: 85.91-90.18) in the CG (P = 0.005), and the total Boston Bowel Preparation Scale scores (6.89 ± 1.15 vs 6.67 ± 1.15, P < 0.001) of those in the SSG were significantly higher than those in the CG. The average number of polyps detected in the SSG was considerably higher than that in the CG (0.84 ± 2.00 vs 0.53 ± 1.19, P = 0.037), and the average diameter of the polyps was significantly lower than that of the control group (4.0 ± 2.5 vs 4.9 ± 3.7, P < 0.001). CONCLUSIONS: This SS-enhanced education strategy can improve the BP adequacy rate and increase the average number of polyps detected, especially those of small diameter.

Derivation of water quality criteria for paraquat, bisphenol A and carbamazepine using quantitative structure-activity relationship and species sensitivity distribution (QSAR-SSD).

The risk assessment of an expanding array of emerging contaminants in aquatic ecosystems and the establishment of water quality criteria rely on species sensitivity distribution (SSD), necessitating ample multi-trophic toxicity data. Computational methods, such as quantitative structure-activity relationship (QSAR), enable the prediction of specific toxicity data, thus mitigating the need for costly experimental testing and exposure risk assessment. In this study, robust QSAR models for four aquatic species (Rana pipiens, Crassostrea virginica, Asellus aquaticus, and Lepomis macrochirus) were developed using leave-one-out (LOO) screening variables and the partial least squares algorithm to predict toxicity data for paraquat, bisphenol A, and carbamazepine. These predicted data can be integrated with experimental data to construct SSD models and derive hazardous concentration for 5 % of species (HC5) for the criterion maximum concentration. The chronic water quality criterion for paraquat, bisphenol A, and carbamazepine were determined at 6.7, 11.1, and 3.5 µg/L, respectively. The QSAR-SSD approach presents a viable and cost-effective method for deriving water quality criteria for other emerging contaminants.

Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis.

Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible. Supplementary Information: The online version contains supplementary material available at 10.1007/s10682-024-10291-7.

Challenges of Protein-Protein Docking of the Membrane Proteins.

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

5G-enabled UAVs for energy-efficient opportunistic networking.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Association of Klotho with Neuropsychiatric Disorder: A Meta-Analysis.

Neuropsychiatric disorders are mainly concerned with the behavioural, emotional and cognition symptoms that may be due to disturbed cerebral functions or extracerebral disease. Klotho protein is an antiaging protein that is mostly associated with cognitive changes in these disorders and thus this meta-analysis is conducted in order to find Klotho proteins association with these disorders. We searched related topics in pubmed, by using the key word i.e. Klotho and related disorder from neuropsychiatry e.g. Klotho levels and schizophrenia, Klotho levels and parkinsonism etc. Total 82 studies were found till 9th February 2021 after extensive search and 10 studies were selected for further analysis. The meta-analysis of studies was performed using the Random effect model. The forest plot represented each study in the meta-analysis, so as to make the comparison of SMD value across studies. The meta-analysis outcome demonstrated that overall schizophrenia had higher klotho levels as compared with bipolar disorder, psychosocial stress, parkinsonism, multiple sclerosis, depression, Alzheimer's disease, and healthy controls, followed by MS. The meta-analysis also found that bipolar disorder and Alzheimer's disease were associated with low klotho levels as compared to schizophrenia. The results indicate a significant association of the klotho levels and schizophrenia. Further studies are needed to characterize the potential biological roles of klotho levels in psychiatric disorders.

Analysis of Carrier Transport at Zn1-xSnxOy/Absorber Interface in Sb2(S,Se)3 Solar Cells.

This work explores the effect of a Zn1-xSnxOy (ZTO) layer as a potential replacement for CdS in Sb2(S,Se)3 devices. Through the use of Afors-het software v2.5, it was determined that the ZTO/Sb2(S,Se)3 interface exhibits a lower conduction band offset (CBO) value of 0.34 eV compared to the CdS/Sb2(S,Se)3 interface. Lower photo-generated carrier recombination can be obtained at the interface of the ZTO/Sb2(S,Se)3 heterojunction. In addition, the valence band offset (VBO) value at the ZTO/Sb2(S,Se)3 interface increases to 1.55 eV. The ZTO layer increases the efficiency of the device from 7.56% to 11.45%. To further investigate the beneficial effect of the ZTO layer on the efficiency of the device, this goal has been achieved by five methods: changing the S content of the absorber, changing the thickness of the absorber, changing the carrier concentration of ZTO, using various Sn/(Zn+Sn) ratios in ZTO, and altering the thickness of the ZTO layer. When the S content in Sb2(S,Se)3 is around 60% and the carrier concentration is about 1018 cm-3, the efficiency is optimal. The optimal thickness of the Sb2(S,Se)3 absorber layer is 260 nm. A ZTO/Sb2(S,Se)3 interface with a Sn/(Zn+Sn) ratio of 0.18 exhibits a better CBO value. It is also found that a ZTO thickness of 20 nm is needed for the best efficiency.

Comparison of TEVA vs. PRAAT in the Acoustic Characterization of the Tracheoesophageal Voice in Laryngectomized Patients.

Background: Previous studies have assessed the capability of PRAAT for acoustic voice analysis in total laryngectomized (TL) patients, although this software was designed for acoustic analysis of laryngeal voice. Recently, we have witnessed the development of specialized acoustic analysis software, Tracheoesophageal Voice Analysis (TEVA). This study aims to compare the analysis with both programs in TL patients. Methods: Observational analytical study of 34 TL patients where a quantitative acoustic analysis was performed for stable phonation with vowels [a] and [i] as well as spectrographic characterization using the TEVA and PRAAT software. Results: The Voice Handicap Index (VHI-10) showed a mean score of 11.29 ± 11.16 points, categorized as a moderate handicap. TEVA analysis found lower values in the fundamental frequency vs. PRAAT (p < 0.05). A significant increase in shimmer values was observed with TEVA (>20%). No significant differences were found between spectrographic analysis with TEVA and PRAAT. Conclusions: Tracheoesophageal speech is an alaryngeal voice, characterized by a higher degree of irregularity and noise compared to laryngeal speech. Consequently, it necessitates a more tailored approach using objective assessment tools adapted to these distinct features, like TEVA, that are designed specifically for TL patients. This study provides statistical evidence supporting its reliability and suitability for the evaluation and tracking of tracheoesophageal speakers.

Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance.

PURPOSE: This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts. MATERIALS AND METHODS: This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors' significance and contributions. RESULTS: The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors. CONCLUSIONS: This study sheds light on the factors influencing dental professionals' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.

phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria.

Objectives: The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of research across multiple research groups and datasets. Materials and Methods: We developed the phoenix R package and Python module to provide researchers with intuitive and simple functions to apply the Phoenix criteria to EHR data. Results: The phoenix R package and Python module enable researchers to apply the Phoenix criteria to EHR datasets and derive the relevant indicators, total scores, and sub-scores. Discussion: The transition to the Phoenix criteria marks a major change in the conceptual definition of pediatric sepsis. Applicable across differentially resourced settings, the Phoenix criteria should help improve clinical care and research. Conclusion: The phoenix R package and Python model are freely available on CRAN, PyPi, and GitHub. These tools enable the consistent and accurate application of the Phoenix criteria to EHR datasets.

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.

BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE: Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS: The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS: According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION: From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

Holographic mixed reality for planning transcatheter aortic valve replacement.

BACKGROUND: Using three-dimensional (3D) modalities for optimal pre-procedure planning in transcatheter aortic valve replacement (TAVR) is critical for procedural success. However, current methods rely on visualizing images on a two-dimensional screen, using shading and colors to create the illusion of 3D, potentially impeding the accurate comprehension of the actual anatomy structures. In contrast, a new Mixed Reality (MxR) based software enables accurate 3D visualization, imaging manipulation, and quantification of measurements. AIMS: The study aims to evaluate the feasibility, reproducibility, and accuracy of dimensions of the aortic valve complex as measured with a new holographic MxR software (ARTICOR®, Artiness srl, Milano, Italy) compared to a widely used software for pre-operative sizing and planning (3mensio Medical Imaging BV, Bilthoven, The Netherlands) . METHODS: This retrospective, observational, double-center study enrolled 100 patients with severe aortic stenosis who underwent cardiac computed tomography (CCT) before TAVR. The CCT datasets of volumetric aortic valve images were analyzed using 3Mensio and newly introduced MxR-based software. RESULTS: 98% of the CCT datasets were successfully converted into holographic models. A higher level of agreement between the two software systems was observed for linear metrics (short, long, and average diameter). In comparison, agreement was lower for area, perimeter, and annulus-to-coronary ostia distance measurements. Notably, the annulus area, annular perimeter, LVOT area, and LVOT perimeter were significantly and consistently smaller with the MxR-based software compared to the 3Mensio. Excellent interobserver reliability was demonstrated for most measurements, especially for direct linear measurements. CONCLUSIONS: Linear Measurements of the aortic valve complex using MxR-based software are reproducible compared to the standard CCT dataset analyzed with 3Mensio. MxR-based software could represent an accurate tool for the pre-procedural planning of TAVR.

BASSA: New software tool reveals hidden details in visualisation of low-frequency animal sounds.

The study of animal sounds in biology and ecology relies heavily upon time-frequency (TF) visualisation, most commonly using the short-time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%-28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT's bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low-frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop 'BASSA', an open-source, GUI software application that offers a no-code, user-friendly tool for analysing short-duration recordings of low-frequency animal sounds for the Windows platform. The SLT visualises low-frequency animal sounds with improved accuracy, in a user-friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics-driven studies of animal communication, vocal production methods, phonation and species identification.

APIS: an updated parentage assignment software managing triploids induced from diploid parents.

In aquaculture, sterile triploids are commonly used for production as sterility gives them potential gains in growth, yields and quality. However, they cannot be reproduced, and DNA parentage assignment to their diploid or tetraploid parents is required to estimate breeding values for triploid phenotypes. No publicly available software has the ability to assign triploids to their parents. Here, we updated the R package APIS to support triploids induced from diploid parents. First, we created new exclusion and likelihood tables that account for the double allelic contribution of the dam and the recombination that can occur during female meiosis. As the effective recombination rate of each marker with the centromere is usually unknown, we set it at 0.5 and found that this value maximises the assignment rate even for markers with high or low recombination rates. The number of markers needed for a high true assignment rate did not strongly depend on the proportion of missing parental genotypes. The assignment power was however affected by the quality of the markers (minor allele frequency, call rate). Altogether, 96 to 192 SNPs were required to have a high parentage assignment rate in a real rainbow trout dataset of 1232 triploid progenies from 288 parents. The likelihood approach was more efficient than exclusion when the power of the marker set was limiting. When more markers were used, exclusion was more advantageous, with sensitivity reaching unity, very low False Discovery Rate (<0.01) and excellent specificity (0.96-0.99). Thus, APIS provides an efficient solution to assign triploids to their diploid parents.