IBMA: An SPM toolbox for Neuroimaging Image-Based Meta-Analysis
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While most neuroimaging meta-analyses are based on peak coordinate data, the best practice method is an image-based meta-analysis that combines the effect estimates and the standard errors from each study [7]. Various efforts are underway to facilitate sharing of neuroimaging data to make such intensity-based meta-analysis possible (see, e.g. [4]). When image data is available for each study, a number of approaches (see [6] for a review) have been proposed to perform such meta-analysis including combination of standardised statistics, just effect estimates or both effects estimates and their sampling variance. While the latter is the preferred approach in the statistical community [1], often only standardised estimates are shared, reducing the possible meta-analytic approaches. In view of the increasing availability of image data for neuroimaging analyses, we introduce IBMA, a toolbox for SPM [8] providing a set of tools for image-based meta-analysis. The toolbox is freely available at: https://github.com/NeuroimagingMetaAnalysis/ibma.
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In this talk I will review several real-world applications and tools developed at the University of Waikato over the past 15 years. The early applications focused on agricultural problems such as cow culling, venison bruising and grass grubs. Following this we looked at the use of near infrared spectroscopy coupled with data mining as an alternate laboratory technique for predicting compound concentrations in soil and plant samples. Our latest application is in the area of gas chromatography mass spectrometry (GCMS), a technique used to determine in environmental applications, for example, the petroleum content in soil and water samples.
Lecture 2 Data mining process.pdf
This document outlines the typical phases in a data mining process: 1) discovery, 2) data preparation, 3) data exploration and conditioning, 4) model planning, 5) model building, 6) results interpretation, and 7) model deployment. It also discusses key aspects of each phase such as identifying business problems, obtaining and checking data, handling missing values, selecting modeling techniques, evaluating models, and deploying results. Supervised and unsupervised learning methods are introduced along with examples like prediction, classification, clustering and association rules.
Cross-validation aggregation for forecasting
Cross-validation aggregation combines the benefits of cross-validation and forecast aggregation. It saves the predictions from models estimated on different cross-validation folds and averages these predictions to obtain the final forecast. Empirical results on 111 time series show that cross-validation aggregation outperforms simple model averaging and bagging, with the lowest errors on validation sets. Different cross-validation aggregation methods perform best depending on data characteristics like time series length and forecast horizon.
Data mining Basics and complete description onword
This document discusses data mining and provides examples of its applications. It begins by explaining why data is mined from both commercial and scientific viewpoints in order to discover useful patterns and information. It then discusses some of the challenges of data mining, such as dealing with large datasets, high dimensionality, complex data types, and distributed data sources. The document outlines common data mining tasks like classification, clustering, association rule mining, and regression. It provides real-world examples of how these techniques are used for applications like fraud detection, customer profiling, and scientific discovery.
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
Guiding through a typical Machine Learning Pipeline
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGES
Digital image processing is vast fields which can be using various applications. Which include Detection of criminal face, fingerprint authentication system, in medical field, object recognition etc. Brain tumor detection plays an important role in medical field. Brain tumor detection is detection of tumor affected part in the brain along with its shape size and boundary, so it useful in medical field.
Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologist and are important for planning of treatment strategies, monitoring of disease progression and prediction of patient outcome. For a better understanding of the pathophysiology of diseases, quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures
Similar to IBMA: An SPM toolbox for Neuroimaging Image-Based Meta-Analysis (20)
OHBM 2016: Practical intensity based meta-analysis
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Data mining Basics and complete description onword
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IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
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Guiding through a typical Machine Learning Pipeline
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A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGES
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MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Synopsis presentation VDR gene polymorphism and anemia (2).pptx
I am the PhD scholar of FND department, Assam down town University
Firoozeh Kashani-Sabet - An Esteemed Professor
Dr. Firoozeh Kashani-Sabet is an innovator in Middle Eastern Studies and approaches her work, particularly focused on Iran, with a depth and commitment that has resulted in multiple book publications. She is notable for her work with the University of Pennsylvania, where she serves as the Walter H. Annenberg Professor of History.
Methods of grain storage Structures in India.pdf
•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate
Farming systems analysis: what have we learnt?.pptx
Presentation given at the official farewell of Prof Ken Gillet at Wageningen on 13 June 2024
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Explainable Deepfake Image/Video Detection
Presentation of our paper, "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection", by K. Tsigos, E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris. Presented at the ACM Int. Workshop on Multimedia AI against Disinformation (MAD’24) of the ACM Int. Conf. on Multimedia Retrieval (ICMR’24), Thailand, June 2024. https://doi.org/10.1145/3643491.3660292 https://arxiv.org/abs/2404.18649
Software available at https://github.com/IDT-ITI/XAI-Deepfakes
Embracing Deep Variability For Reproducibility and Replicability
Embracing Deep Variability For Reproducibility and ReplicabilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
Embracing Deep Variability For Reproducibility and Replicability
Abstract: Reproducibility (aka determinism in some cases) constitutes a fundamental aspect in various fields of computer science, such as floating-point computations in numerical analysis and simulation, concurrency models in parallelism, reproducible builds for third parties integration and packaging, and containerization for execution environments. These concepts, while pervasive across diverse concerns, often exhibit intricate inter-dependencies, making it challenging to achieve a comprehensive understanding. In this short and vision paper we delve into the application of software engineering techniques, specifically variability management, to systematically identify and explicit points of variability that may give rise to reproducibility issues (eg language, libraries, compiler, virtual machine, OS, environment variables, etc). The primary objectives are: i) gaining insights into the variability layers and their possible interactions, ii) capturing and documenting configurations for the sake of reproducibility, and iii) exploring diverse configurations to replicate, and hence validate and ensure the robustness of results. By adopting these methodologies, we aim to address the complexities associated with reproducibility and replicability in modern software systems and environments, facilitating a more comprehensive and nuanced perspective on these critical aspects.
https://hal.science/hal-04582287
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptx
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
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Mechanics:- Simple and Compound Pendulum
a compound pendulum is a physical system with a more complex structure than a simple pendulum, incorporating its mass distribution and dimensions into its oscillatory motion around a fixed axis. Understanding its dynamics involves principles of rotational mechanics and the interplay between gravitational potential energy and kinetic energy. Compound pendulums are used in various scientific and engineering applications, such as seismology for measuring earthquakes, in clocks to maintain accurate timekeeping, and in mechanical systems to study oscillatory motion dynamics.
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IBMA: An SPM toolbox for Neuroimaging Image-Based Meta-Analysis
- 1. University of Warwick, Warwick Manufacturing Group & Department of Statistics, Coventry, UK. Camille Maumet and Thomas E. Nichols IBMA: An SPM toolbox for Neuroimaging Image-Based Meta-Analysis
- 2. Agenda • Meta-analyses in Neuroimaging – Why? – Coordinate-Based or Image-Based? • Image-Based Meta-Analysis – Gold standard – Other approaches • Validity of IBMA approaches in neuroimaging 2
- 4. Why meta-analyses? • Power increase • Combine information across studies Data acquisition Analysis Experiment Raw data Results Data acquisition Analysis Experiment Raw data Results … Results Meta- analysis 4
- 5. Data analysis in neuroimaging Analysis ResultsExperiment Data acquisition Raw data Paper Publication Imaging data 5 500MB/subject 20GB 2.5GB/subject 100GB [ ~2GB for stats] < 0.5MB0MB
- 6. Coordinate-Based Meta-Analysis Table of local maxima (quantitative) 6 Paper Publication ? Detection images (qualitative) Peaks (quantitative) < 0.5MB
- 7. Coordinate- or Image-Based? 7 Data acquisition Analysis Experiment Raw data Results Data acquisition Analysis Experiment Raw data Results … Publication Publication Paper Paper Coordinate-based meta-analysis Image-based meta-analysis Shared results Data sharing
- 9. Meta-analysis levelStudy levelSubject level Meta-analysis gold standard Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Inference Detections (subject-level) Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Inference Detections (subject-level) Model fitting and estimation Contrast and std. err. maps Inference Detections (study-level) Pre-processed data Subject1 Model fitting and estimation Contrast and std. err. maps Model fitting and estimationPre-processed data Subjectn Contrast and std. err. maps … Model fitting and estimation Contrast and std. err. maps Model fitting and estimation Contrast and std. err. maps Inference Detections (meta-analysis) Inference Detections (study-level) 9
- 10. Image-based Meta-analysis • Gold standard: • But… – Units will depend on: • The scaling of the data (subject-level) • The scaling of the predictor(s) that are involved in the selected contrast (subject- and study-level) • The scaling of the selected contrast (subject- and study- level). – Contrast estimates and standard error maps are rarely shared… 10 Third-level Mixed-Effects GLM
- 11. Image-Based Meta-Analysis • Other (sub-optimal) statistics available: – Based on z-statistic: • Fishers’s; Stouffer’s; “Stouffers’s MFX” – Based on z-statistic + sample size • Weighted Stouffer’s – Based on contrast estimates only: • RFX GLM; – Based on contrast estimates and standard error • Fixed-Effects GLM • Based on restrictive assumptions, robustness to violation need to be further studied 11
- 12. IBMA toolbox • Plugin for • Available on github: https://github.com/NeuroimagingMetaAnalysis/ibma 12
- 13. Validity of IBMA approaches in neuroimaging 13
- 14. Meta-analysis of 21 pain studies • Data – 21 studies investigated pain in healthy subjects 14
- 15. Conclusion • Towards Image-Based meta-analysis. • In practice, it is difficult to use the gold standard Third-level Mixed-Effects General Linear Model. • IBMA toolbox provides alternative approaches. • Further investigation: two-sample analysis… 15
- 16. Acknowledgements Q & A We gratefully acknowledge the use of MRI data from the Tracey pain group, FMRIB, Oxford. This work is supported by the 16