Interpretation Of Data Research Articles

Music Information Retrieval using Deep Learning Techniques

Music Information Retrieval (MIR) is gaining attention due to the surge in digital music and the need for efficient search and recommendation systems. Traditional MIR methods rely on hand-crafted features and rule-based systems, limiting their adaptability. Deep Learning (DL) shows promise in automatically extracting complex patterns from raw data. This paper offers an extensive overview of MIR tasks like classification, genre recognition, similarity search, and recommendation, along with DL models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including LSTM and Transformer architectures, tailored for MIR. Challenges such as data scarcity, computational complexity, and interpretability persist, with proposed solutions like data augmentation, transfer learning, and attention mechanisms. Experimental results on benchmark datasets demonstrate DL's superiority in accuracy, scalability, and robustness over traditional methods. Practical examples highlight DL's potential to revolutionize music search, recommendation, and analysis. Emphasizing the importance of large annotated datasets for training high-quality DL models, strategies for data collection, labeling, and preprocessing are outlined. DL offers promising prospects for advancing MIR by addressing its inherent challenges and establishing new performance benchmarks. Further DL development is expected to drive innovation and enhance digital music consumption experiences through MIR systems. Keywords: Deep Learning, Convolution Neural Network (CNNs), Recurrent Neural Network (RNNs), Digital Music Consumption, Genre recognition.

Students’ Perspective on Undergraduate Research Experiences in Indonesian Dental Schools

Undergraduate (UG) research is considered as an essential part of dental education. Numerous dental schools have included required course-based undergraduate research in their curricula. However, the implementation of UG research courses in the curriculum may vary between dental schools. In the present study, we aimed to evaluate student perspectives on UG research in the curriculum of Indonesian dental schools. A total of 203 participants from 10 dental schools returned the questionnaire. The participants were clinical students of the dentistry profession program who completed their undergraduate dentistry program from 2017 to 2022. The majority of study participants favored UG research in the curriculum of the undergraduate dentistry study program. Less than 20% participants perceived UG research experiments were not important in dental education. Factors that influenced these perceptions included the availability of adequate time to complete the course and sufficient support from research supervisors. Recommendations for improvement included providing an adequate time to complete UG research and adequate supervision to guide students to understand the conceptual background information of the research topics, designs, and scientific communication of data interpretation. Regular monitoring of students’ performance and progress would ensure completion of UG research courses in a timely manner. In conclusion, although UG research as a compulsory course in the Indonesian dental curriculum was well received by the students, overcoming the challenges is essential for the improvement of the research environment for undergraduate dental students.

HiPSC-CM Electrophysiology: Impact of Temporal Changes and Study Parameters on Experimental Reproducibility.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are frequently used for preclinical cardiotoxicity testing and remain an important tool for confirming model-based predictions of drug effects in accordance with Comprehensive in Vitro Proarrhythmia Assay (CiPA). Despite the considerable benefits hiPSC-CMs provide, concerns surrounding experimental reproducibility have emerged. We investigated the effects of temporal changes and experimental parameters on hiPSC-CM electrophysiology. iCell cardiomyocytes2 were cultured and biosignals were acquired using a microelectrode array (MEA) system (1-14 days). Continuous recordings revealed a 22.6% increase in the beating rate and 7.7% decrease in the field potential duration (FPD) during a 20-minute equilibration period. Location specific differences across a multiwell plate were also observed, with iCell cardiomyocytes2 in the outer rows beating 8.8 beats per minute (BPM) faster than the inner rows. Cardiac endpoints were also impacted by cell culture duration; from 2-14 days the beating rate decreased (-12.7 BPM), FPD lengthened (+257 ms), and spike amplitude increased (+3.3 mV). Cell culture duration (4-10 days) also impacted cardiomyocyte drug responsiveness (E-4031, nifedipine, isoproterenol). qRT-PCR results suggest that daily variations in cardiac metrics may be linked to the continued maturation of hiPSC-CMs in culture (2-30 days). Daily experiments were also repeated using a second cell line (Cor.4U). Collectively, our study highlights multiple sources of variability to consider and address when performing hiPSC-CM MEA studies. To improve reproducibility and data interpretation, MEA-based studies should establish a standardized protocol and report key experimental conditions (e.g., cell line, culture time, equilibration time, electrical stimulation settings, raw data values).

Defining high bleeding risk in patients undergoing transcatheter aortic valve implantation: a VARC-HBR consensus document.

<span style="font-size:12pt"><span style="font-family:Aptos,sans-serif">The identification and management of patients at high bleeding risk (HBR) undergoing transcatheter aortic valve implantation (TAVI) are of major importance, but the lack of standardised definitions is challenging for trial design, data interpretation, and clinical decision-making. The Valve Academic Research Consortium for High Bleeding Risk (VARC-HBR) is a collaboration among leading research organisations, regulatory authorities, and physician-scientists from Europe, the USA, and Asia, with a major focus on TAVI-related bleeding. VARC-HBR is an initiative of the CERC (Cardiovascular European Research Center), aiming to develop a consensus definition of TAVI patients at HBR, based on a systematic review of the available evidence, to provide consistency for future clinical trials, clinical decision-making, and regulatory review. This document represents the first pragmatic approach to a consistent definition of HBR evaluating the safety and effectiveness of procedures, devices and drug regimens for patients undergoing TAVI.</span></span>.

New insights into how temperature affects the electrical conductivity of clay-free porous rocks

Summary Geothermal energy is increasingly important for the global environment and for the sustainable development of our society. Electrical surveys are widely employed for the exploration of geothermal energy, because the electrical geophysical properties provide useful information about the fluids at depth. However, although quantitative interpretation of electrical survey data relies on the knowledge about the effects of temperature on the electrical properties of fluid-bearing rocks, it remains poorly understood about how temperature affects the electrical conductivity of clay-free porous rocks. We bridge this knowledge gap by measuring the electrical conductivity and porosity of five brine saturated clean Berea sandstones with temperature ranging between 25° C and 140° C, and analysing all the factors that impact the rock conductivity. We showed that the effects of surface conductivity on the temperature dependent electrical conductivity can be negligible, whereas the temperature induced variation in the porosity and pore structure quantitatively characterized in terms of cementation exponent can be more significant. We also found that temperature affects the electrical conductivity of brine saturated Berea sandstones by impacting the brine conductivity, and the pore structure and porosity of the samples, with their importance in a descending order. The results have provided new insights into how temperature affects the electrical conductivity of clay-free porous rocks, and will help to improve the quantitative interpretation of electrical survey data for the exploration of geothermal energy.

Microsatellites for butterfly conservation: historical challenges, current relevance, and a guide to implementation

We are nearing the 20-year anniversary of a groundbreaking paper which details how microsatellite marker development in Lepidoptera is “extremely difficult for no apparent reason.” How far have we come in these past 20 years? Microsatellites are still the marker of choice in many population genetics studies for their ease of use, high degrees of polymorphism, species-specificity, and low cost. The rise of next-generation sequencing technologies (e.g. 454, Illumina, PacBio, etc.) has greatly advanced our abilities to generate many microsatellite markers per species. In this paper, we summarize the improvements in marker development using next-generation technology. Using case studies, we review the use and implementation of microsatellite markers in different conservation programs. Lastly, we provide a guide to data interpretation of microsatellite data generated for butterflies, with the goal of supporting student researchers and conservation practitioners in evaluating the meaning in their data.

A simulation study: comparing independent component analysis and signal-space projection – source-informed reconstruction for rejecting muscle artifacts evoked by transcranial magnetic stimulation

IntroductionThe combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection–source-informed reconstruction approach (SSP–SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP–SIR.MethodsWe simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS–EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP–SIR, and subsequent comparisons were made with the ground truth data.ResultsICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP–SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP–SIR being better when difference between topographies was larger. Overall, SSP–SIR performed better than ICA across the tested conditions. Based on these simulations, SSP–SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials.DiscussionSelecting between ICA and SSP–SIR should be guided by the characteristics of the artifacts. SSP–SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP–SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.

Research ethics in a multilingual world: A guide to reflecting on language decisions in all disciplines

Doing research in a globalized context – regardless of the discipline – requires language decisions at different stages of the research process. Many of these language decisions have ethical implications. Existing literature and ethical guidance tend to focus on ethical concerns that arise in communication with participants who use a language different from the main research language. As this article shows, language decisions with potential ethical implications can occur in many additional ways. Two questions guided this work: how do language decisions and research ethics intersect at different stages of the research process, and what potential harm is related to language decisions and how can such concerns be mitigated? Relevant literature – combined with practical research experiences – show that language decisions can potentially result in harm at the conception stage of research projects, when working with participants, during data analysis, interpretation, and dissemination, as well as when managing a research project. Thus, the article highlights that the role of language-related ethical dilemmas is not only organizational (e.g., ensuring participants can understand what the research is about), but also social (e.g., developing trust and relationships in research teams) as well as ideological (e.g., awareness of power structures and diverse worldviews). In order to support researchers in globalized contexts, this article offers a reflective framework that complements regulatory guidance issued by ethical bodies and facilitates a deeper awareness of ethical implications related to language decisions in a multilingual world.

An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress.

Molecular Hydrogen Acts as a Hydrogen Bond Proton Acceptor: From Protonated Betaine Tagging to the Weakest Hydrogen Bond.

In an attempt to gain further insights into the intermolecular interactions implied by Rizzo's group's cautionary tale related to molecular tagging in infrared multiple photon dissociation (IRMPD) spectroscopy with molecular messengers [Masson, A. . J. Chem. Phys. 2015, 143, 104313], in the present study, we provide an in-depth analysis of the noncovalent interaction between the molecular hydrogen and protonated betaine molecule in the gas phase. We aim to shed some new light on the fundamental issues concerning the wide diapason of hydrogen-bonding-type intermolecular interactions, with a wide variety of proton acceptors. We demonstrate that in the course of tagging the protonated betaine with molecular hydrogen from the OH group side, it is the σ bond of molecular hydrogen that plays the role of hydrogen-bonding proton acceptor. The tagging thus induces a small yet significant red shift of the protonated betaine O-H stretching mode. We investigate the performance of a wide range of density functional theory (DFT) functionals for the calculation of anharmonic vibrational frequency shifts of the studied system, which are essential for the correct interpretation of the experimental IRMPD data. For an accurate prediction of the OH stretching frequency shifts, specifically designed functionals such as Handy's group HCTH/407 should be applied. The empirical dispersion correction enhances the systematic overestimation of the anharmonic frequency shift, characteristic of the most widely used DFT functionals. Combining the full-wave function approach with the charge field perturbation and natural bond orbital (NBO) deletion analyses, we demonstrate that the frequency shift in the OH-tagged structure is governed by the σHH → σ*OH intermolecular charge transfer. This interaction stabilizes the OH-tagged dimer as well, in contrast to the dipole-quadrupole electrostatic interaction energy term. Topological analysis of the electron density reveals the presence of an intermolecular bond critical point with a positive value of the density Laplacian very close to the lower limit for hydrogen bonds. NCI analyses demonstrate that the OH···H2 interaction is weaker than the intramolecular CH···O one within the protonated betaine molecule, with the through of reduced density gradient appearing at less negative sign(λ2)·ρ values. Analyzing the O-H stretching vibrational potential with the second-generation absolutely localized molecular orbitals energy decomposition analysis (ALMO-EDA 2) revealed that in the case of betaineH(+) tagged from the OH group side, the permanent electrostatics (ΔEelec), polarization (ΔEpol), and charge-transfer (ΔEct) contributions to the total intermolecular interaction energy contribute favorably to the weak hydrogen bond formation and to the red shift of the fundamental O-H stretching frequency, the ΔEct contribution being the most significant in the last context. The Pauli repulsion term, on the other hand, favors an O-H stretching frequency blue shift as a consequence of the vibrational confinement effects.

Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

BackgroundKnowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.MethodsOne hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.ResultsKnowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.ConclusionThe study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

FAULT-TRANSFORMER: AN AUTOMATIC FAULT DETECTION ALGORITHM ON SEISMIC IMAGES USING A TRANSFORMER ENHANCED NEURAL NETWORK

Seismic fault detection is a key step in seismic interpretation and reservoir characterization that often requires a large amount of human labor and interpretation time. Therefore, automatic seismic fault detection is critical for improving the efficiency of seismic data processing and interpretation. Existing AI methods are mostly based on convolutional neural networks (CNNs) with a U-shaped encoder-decoder structure, known as the U-net. However, the convolution is limited in modeling long-range correlative features. Instead, Transformers, utilizing self-attention mechanisms, avoid the local nature of the convolution, which has the potential to extract long-distance correlations. Transformers are proven to perform well in natural language processing, image classification, and segmentation tasks in precision and recall. Here, we develop a new deep neural network with transformers and a U-net-like structure: Fault-Transformer, to perform the fault detection task. The new network outperforms traditional U-net in the application with synthetic data sets.

A STUDY ON MANAGING MATERIAL HANDLING, STORAGE AND PACKAGING IN FREIGHT FORWARDING AT FLYJAC LOGISTICS PVT LT

This study looks into the difficulties and practices that the freight forwarding industry is currently facing in these areas. A combination of quantitative data collection through surveys and qualitative insights gained from interviews with industry experts were combined in a mixed-method research approach. Thematic analysis and statistical methods were used for data interpretation and analysis. The results indicate that there exist noteworthy prospects for enhancing material handling procedures, optimizing storage, and refining packaging tactics. Among the recommendations are the use of cutting-edge technologies for inventory control, the application of lean concepts to optimize processes, and the creation of environmentally friendly packaging options. Freight forwarding businesses can increase customer satisfaction, cut expenses, and increase efficiency by putting these recommendations into practice. KEY WORDS: Inventory management software, Conveyor systems, Inventory control, Space utilization and Packaging materials.

A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have numerous shortcomings, such as addressing complex constraints and conducting multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This novel method stands out by addressing complex constraints and facilitating multi-perspective effectiveness evaluations. It has several advantages: (1) it combines supervised and unsupervised learning methods, allowing for nuanced data interpretation and enhanced attribute selection; (2) by incorporating multi-granularity structures, the algorithm can analyze data at various levels of granularity. This allows for a more detailed understanding of data characteristics at each level, which can be crucial for complex datasets; and (3) by using neighborhood relations instead of indiscernibility relations, the method effectively handles uncertain and fuzzy data, making it suitable for real-world datasets that often contain imprecise or incomplete information. It not only selects the optimal granularity level or attribute set based on specific requirements, but also demonstrates its versatility and robustness through extensive experiments on 15 UCI datasets. Comparative analyses against six established attribute reduction algorithms confirms the superior reliability and consistency of our proposed method. This research not only enhances the understanding of attribute reduction mechanisms, but also sets a new benchmark for future explorations in the field.

Sex and the Kidney Drug-Metabolizing Enzymes and Transporters: Are Preclinical Drug Disposition Data Translatable to Humans?

Cross-species differences in drug transport and metabolism are linked to poor translation of preclinical pharmacokinetic and toxicology data to humans, often resulting in the failure of new chemical entities (NCEs) during clinical drug development. Specifically, inaccurate prediction of renal clearance and renal accumulation of NCEs due to differential abundance of enzymes and transporters in kidneys can lead to differences in pharmacokinetics and toxicity between experimental animals and humans. We carried out liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based protein quantification of 78 membrane drug-metabolizing enzymes and transporters (DMETs) in the kidney membrane fractions of humans, rats, and mice for characterization of cross-species and sex-dependent differences. In general, majority of DMET proteins were higher in rodents than in humans. Significant cross-species differences were observed in 30 out of 33membrane DMET proteins quantified in all three species. Although no significant sex-dependent differences were observed in humans, the abundance of 28 and 46 membrane proteins showed significant sex dependence in rats and mice, respectively. These cross-species and sex-dependent quantitative abundance data are valuable for gaining a mechanistic understanding of drug renal disposition and accumulation. Further, these data can also be integrated into systems pharmacology tools, such as physiologically based pharmacokinetic models, to enhance the interpretation of preclinical pharmacokinetic and toxicological data.

Design of Concrete Mock-Ups with Complex Defect Scenarios Using Numerical Simulations

The major objective when applying non-destructive testing (NDT) in civil engineering is the detection of defects such as honeycombs. Defects can impair the service life of structures and could lead in the worst case to fatal failures. All structures such as bridges, tunnels, locks etc. are unique objects with variation in geometry, dimensions, materials, environmental condition, load scenarios etc. Thus, the interpretation of NDT data is challenging and the validation of the NDT method, the training of inspectors and the reliability analysis of NDT rely on mock-ups with known defect situations mimicking the type of defect as realistic as possible. The considered defect sizes inside the mock-ups should cover the transition zone between detectable and non-detectable to properly evaluate the capability of the inspection system. However, knowledge about the limits of an inspection system is mostly non-existent. To overcome this limitation, this study applies realistic numerical simulations for ultrasonic testing in reinforced concrete performed with the elastodynamic finite integration technique (EFIT) to identify this transition zone. First, the study shows the development and the validation of a numerical representation of concrete considering the implementation of a qualitatively realistic noise level. Additionally, the accurate representation of the sources during ultrasonic testing is confirmed by an investigation of the radiation characteristic for dry point contact ultrasonic transducers. The simulation outcome enabled the design of a mock-up, which explores the detection limits for honeycombs during ultrasonic testing under different boundary conditions. A case study demonstrates the applicability of the design scheme based on numerical simulation. In the real specimen designed with the assistance of numerical simulations, 68%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$68\\mathrm{\\%}$$\\end{document} of the implemented defects are detectable, proving the effectiveness of the design methodology.

Electron Spectroscopy and Microscopy: A Window into the Surface Electronic Properties of Polycrystalline Metal Halide Perovskites.

In the past years, an increasing number of experimental techniques have emerged to address the need to unveil the chemical, structural, and electronic properties of perovskite thin films with high vertical and lateral spatial resolutions. One of these is angle-resolved photoemission electron spectroscopy which can provide direct access to the electronic band structure of perovskites, with the aim of overcoming elusive and controversial information due to the complex data interpretation of purely optical spectroscopic techniques. This perspective looks at the information that can be gleaned from the direct measurement of the electronic band structure of single crystal perovskites and the challenges that remain to beovercame to extend this technique to heterogeneous polycrystalline metal halide perovskites.

Analysis of academic research data with the use of ATLAS.ti. Experiences of use in the area of Tourism and Hospitality Administration

Qualitative data analysis in academic research is a challenge. In this context, the use of tools such as ATLAS.ti has emerged as a potential solution to improve the understanding and management of data in the analysis of in-depth interviews. The main objective of the research was to analyze the perspectives of Tourism and Hospitality Management students on the use of ATLAS.ti in the analysis of interviews in qualitative research. The methodology employs a qualitative approach and a descriptive-interpretative design. Data were collected through in-depth interviews and focus groups directed to 40 students of the X cycle who conducted this approach in their research to opt for the bachelor’s degree in Tourism and Hospitality Administration during the years 2022 and 2023. The findings reveal that the use of ATLAS.ti in qualitative data analysis is highly beneficial, facilitating the coding, organization, and identification of emerging patterns in in-depth interviews. The relevance of its effective use in qualitative analysis is highlighted, improving data management, and understanding of participants' perspectives. It is concluded that it is a valuable and effective tool in this context, although the need for researchers to acquire a deep understanding of the tool and receive adequate training is emphasized. It is suggested that they focus on continuous training in its use and constant practice of its advanced functionalities, especially in areas such as coding and code creation, to achieve a deeper interpretation of qualitative data.

Faecal egg count reduction tests and nemabiome analysis reveal high frequency of multi-resistant parasites on sheep farms in north-east Germany involving multiple strongyle parasite species

Anthelmintic resistance in sheep parasitic gastrointestinal nematodes is widespread and a severe health and economic issue but prevalence of resistance and involved parasite species are unknown in Germany. Here, the faecal egg count reduction test (FECRT) was performed on eight farms using fenbendazole, ivermectin and moxidectin and on four farms using only moxidectin. A questionnaire was used to obtain data on management practices to potentially identify risk factors for presence of resistance. All requirements of the recently revised WAAVP guideline for diagnosing anthelmintic resistance using the FECRT were applied. Nematode species composition in pre- and post-treatment samples was analysed with the nemabiome approach. Using the eggCounts statistic package, resistance against fenbendazole, ivermectin and moxidectin was found on 7/8, 8/8 and 8/12 farms, respectively. No formal risk factor analysis was conducted since resistance was present on most farms. Comparison with the bayescount R package results revealed substantial agreement between methods (Cohen's κ = 0.774). In contrast, interpretation of data comparing revised and original WAAVP guidelines resulted in moderate agreement (Cohen's κ = 0.444). The FECR for moxidectin was significantly higher than for ivermectin and fenbendazole. Nemabiome data identified 4 to 12 species in pre-treatment samples and treatments caused a small but significant decrease in species diversity (inverse Simpson index). Non-metric multidimensional scaling and k-means clustering were used to identify common patterns in pre- and post-treatment samples. However, post-treatment samples were scattered among the pre-treatment samples. Resistant parasite species differed between farms. In conclusion, the revised FECRT guideline allows robust detection of anthelmintic resistance. Resistance was widespread and involved multiple parasite species. Resistance against both drug classes on the same farm was common. Further studies including additional drugs (levamisole, monepantel, closantel) should combine sensitive FECRTs with nemabiome data to comprehensively characterise the anthelmintic susceptibility status of sheep nematodes in Germany.