Poster - STEP UP YOUR MORPHOLOGICAL CELL PROFILING GAME WITH AI
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Check out our poster on a swift and fully-automated cell painting image analysis and feature extraction from raw data. You can download it totally for free and share it with your colleagues. Feel free to also have a look at our other posters available for download directly from our website as well: https://www.kmlvision.com/category/posters/ References: [1] S. N. Chandrasekaran et al., bioRxiv 2022.01.05.475090, 2023. [2] M.-A. Bray et al., Nature Protocols, 11(9):1757–1774, 2016. [3] Cell Painting Gallery, Available: https://registry.opendata.aws/cellpainting-gallery/, Accessed: May 2023. [4] KML Vision GmbH, IKOSA (software), Graz, Austria, software available at https://app.ikosa.ai, Accessed: May 2023. [5] AE. Carpenter et al., Genome Biology, 7:1-11, 2006. [6] Core Life Analytics, Available: https://corelifeanalytics.com, Accessed: May 2023. Links: Visit our website: www.kmlvision.com About the company: https://www.kmlvision.com/ KML Vision GmbH, Nikolaiplatz 4, 2nd floor, 8020 Graz, Austria Contact us: slideshare@kmlvision.com
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![STEP UP YOUR MORPHOLOGICAL CELL PROFILING GAME WITH AI
Background and Motivation
A fast approach for fully-automated Cell Painting image analysis and feature extraction from raw image data
Philipp Kainz1
, Bendeguz H. Zovathi1
, Phillip Clarke1
, Maria Roa Oyaga2
, Victor Wong2
, David Egan2
, Thomas Ebner1
1
KML Vision GmbH, 8020 Graz, Austria
www.kmlvision.com
2
Core Life Analytics BV, 5211 DA 's-Hertogenbosch, The Netherlands
www.corelifeanalytics.com
Reproducible morphological profiling, particularly for drug discovery, has become an essential tool for compound evaluation [1]. We describe a
novel approach for cell profiling using a fully-automated computer vision approach with IKOSA AI. Our main motivation is to make Cell Painting
[2] easily accessible for biologists without computer science knowledge. The primary objective of this work is to create a parameter-free, robust,
deep learning-based computer vision algorithm to achieve precise object segmentations for fully-automated high-content imaging.
Materials and Methods
Results
Conclusions and Outlook
Validation dataset Cells (n=62,560) Nuclei (n=58,290)
Precision 0.97 0.98
Recall 0.89 0.94
Average Precision 0.86 0.92
IoU 0.86 0.91
[1] S. N. Chandrasekaran et al., bioRxiv 2022.01.05.475090, 2023.
[2] M.-A. Bray et al., Nature Protocols, 11(9):1757–1774, 2016.
[3] Cell Painting Gallery, Available: https://registry.opendata.aws/cellpainting-gallery/, Accessed: May 2023.
[4] KML Vision GmbH, IKOSA (software), Graz, Austria, software available at https://app.ikosa.ai, Accessed: May 2023.
[5] AE. Carpenter et al., Genome Biology, 7:1-11, 2006.
[6] Core Life Analytics, Available: https://corelifeanalytics.com, Accessed: May 2023.
ground truth image (nucleus)
Our approach (nucleus)
ground truth image (cell)
Our approach (cell)
BR00117054-C21-5
Cell segmentation
BR00116991-N15-5
Highlights Nucleus segmentation
DMSO
(negative
control)
Homoharringtonine
Web-based
software platform
High availability. Access
anywhere, anytime.
Fully-automated
feature extraction
No programming
knowledge required
StratoMineR
integration
Optimized feature
selection
Robust computer
vision pipeline
Easily transfer to
other datasets
We expect a significant impact on
resource efficiency in drug discovery
and personalized medicine.
Cell Painting will be available soon
on the IKOSA platform for
high-throughput scenarios.
True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%)
True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%)
True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%)
True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%)
References
Sign-up for your IKOSA AI free trial today! View the poster online
higher is better
higher is better
higher is better
Intersection over Union, higher is better
Ground truth label generation from
Cell Painting Gallery JUMP-CP pilot
dataset [3] using U2OS and A549 cell
lines; 5 fluorescent (Mito, AGP, RNA,
ER, DNA) and 3 brightfield channels
Training a deep learning instance
segmentation model using the IKOSA
AI software [4] on a well-defined,
diverse subset
Morphological feature extraction
based on CellProfiler [5] cell painting
parameter groups
Feature prioritization using
StratoMineR software [6] by Core
Life Analytics (SLAS EU 2023 poster
number: 1084-A)
Validation & quality control:
Compare IKOSA AI with the
CellProfiler output](https://image.slidesharecdn.com/slaseuposterdesigndigital-230622091412-c0ebe356/85/Poster-STEP-UP-YOUR-MORPHOLOGICAL-CELL-PROFILING-GAME-WITH-AI-1-320.jpg)
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The document discusses using a graph database approach to integrate diverse types of patient omics and clinical data in order to enable a systems biology perspective in medicine. It describes building a graph database called GENOME to integrate multi-omics and clinical data from brain tumor patients to identify novel biomarkers and survival correlates. Random walk simulations on the graph are able to find statistically significant pathways involving different data types that may be biologically meaningful. This approach allows an unbiased and comprehensive analysis not possible with traditional methods.
Cyberinfrastructure Day 2010: Applications in Biocomputing
UNM Cyberinfrastructure Day 2010 presentation: Applications in Biocomputing, biomedical and cheminformatics research computing cyberinfrastructure issues.
MALARIAL PARASITES DETECTION IN THE BLOOD CELL USING CONVOLUTIONAL NEURAL NET...
This document describes a study that uses a convolutional neural network (CNN) to detect malaria parasites in blood cell images. The researchers collected over 27,000 labeled images of parasitized and uninfected blood cells from an online source. They preprocessed the images using median filtering to remove noise. Then they trained and validated a CNN model on 80% of the dataset, achieving an accuracy of 95.34% at detecting the presence or absence of malaria parasites in individual blood cell images. The goal of the study was to develop an automated method for malaria detection as an alternative to existing diagnostic techniques that require skilled microscopists or have limitations in detecting low parasite levels.
Impact of Big Data & Artificial Intelligence in Drug Discovery & Development ...
Oral Presentation given at European Drug Discovery Innovation & Outsourcing Programme on 12th September 2023 in Barcelona. Overview around impact for AstraZeneca R&D from examples in the past 5+ years, including machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, and examples applying AI for right dose - identifying risk factors for CV patients and automated Population PK model prediction.
IRJET- A Survey on Medical Image Interpretation for Predicting Pneumonia
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Various Image Analysis Use Cases solved with IKOSA
A presentation about various use cases solved by our AI-based IKOSA software for microscopy image analysis.
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- Cell Painting Assay
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A presentation about angiogenesis use cases solved by our AI-based IKOSA software for microscopy image analysis.
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- Network Formation Assay
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The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
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This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
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Towards automated phenotypic cell profiling with high-content imaging
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ABSTRACT
Phenotypic profiling of cells with high-content imaging is emerging as an important methodology with high predictive power. The true power of these methods comes when integrated into automated, robotized systems that can be run continuously and not restricted to batch analysis. One of the main challenges then becomes how to manage and continuously analyze the large amounts of data produced. In this talk I will present our efforts to establish an automated lab for cell profiling of drugs using multiplexed fluorescence imaging (Cell Painting). I will describe our computational and lab infrastructure as well as the systems, tools an methods we are developing to sustain continuous profiling of cells and continuous AI modeling. A key objective in the group is on improving screening and toxicity assessment, but also to explore predictions of mechanisms and pathways. The long-term goal is to build a closed-loop system where results from analyses are used by an AI system to design the next round of experiments and iteratively improve the confidence in predictions. Research website: https://pharmb.io
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AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGES
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
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The document discusses using a MobileNetV2 deep learning model for accurate and efficient acute lymphoblastic leukemia (ALL) detection from microscopic images. It achieved 98.88% accuracy on training data and 98.58% on test data. The model was trained and evaluated on a dataset of 3256 images from 89 patients, including 25 healthy individuals, classified into early pre-B, pre-B and pro-B ALL stages. MobileNetV2 was chosen for its efficiency and lightweight design suitable for resource-constrained applications and real-time use.
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The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
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Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Towards Automated AI-guided Drug Discovery Labs
This document discusses using AI, automation, and intelligent experimental design to accelerate drug discovery. Key points include:
- AI and automation will be increasingly important in biological labs for experimental design, hypothesis generation and testing.
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- The goal is to close the loop between data generation, analysis, and experimental design using AI to intelligently design new experiments.
- This would accelerate drug discovery by enabling prediction of safety concerns, explanation of drug mechanisms, and screening for new drugs.
Cytell Cell Imaging System
The Cytell Cell Imaging System is a compact, automated cell imaging system that combines digital microscopy, image cytometry and cell counting capabilities. It provides high quality cellular and subcellular image data and analysis through the use of preconfigured biological applications. These applications simplify routine assays and tasks while generating detailed experimental reports. The system allows non-experts to perform assays but also provides flexibility for experienced researchers. It captures multi-channel fluorescence and brightfield images to analyze cells in a variety of sample formats.
Bioinformatica 29-09-2011-t1-bioinformatics
The document discusses the field of bioinformatics, which involves applying computational techniques and building tools to solve biological problems, such as analyzing genetic sequences and modeling molecular structures. It outlines several applications of bioinformatics, including in medicine for disease research and drug design, as well as in agriculture and animal health. The emergence of bioinformatics is attributed to the convergence of rapid growth in fields like biotechnology and information technology.
Introduction to Machine Learning and Texture Analysis for Lesion Characteriza...
Review the basic principles of machine learning.
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Understand how to combine texture analysis and machine learning for lesion classification tasks.
Learn the how to visualize and analyze results.
Understand how to avoid common mistakes like overfitting and incorrect model selection.
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BRAINREGION.pptx
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Enabling Patient-Driven Medicine Using Graph Database
The document discusses using a graph database approach to integrate diverse types of patient omics and clinical data in order to enable a systems biology perspective in medicine. It describes building a graph database called GENOME to integrate multi-omics and clinical data from brain tumor patients to identify novel biomarkers and survival correlates. Random walk simulations on the graph are able to find statistically significant pathways involving different data types that may be biologically meaningful. This approach allows an unbiased and comprehensive analysis not possible with traditional methods.
Cyberinfrastructure Day 2010: Applications in Biocomputing
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MALARIAL PARASITES DETECTION IN THE BLOOD CELL USING CONVOLUTIONAL NEURAL NET...
This document describes a study that uses a convolutional neural network (CNN) to detect malaria parasites in blood cell images. The researchers collected over 27,000 labeled images of parasitized and uninfected blood cells from an online source. They preprocessed the images using median filtering to remove noise. Then they trained and validated a CNN model on 80% of the dataset, achieving an accuracy of 95.34% at detecting the presence or absence of malaria parasites in individual blood cell images. The goal of the study was to develop an automated method for malaria detection as an alternative to existing diagnostic techniques that require skilled microscopists or have limitations in detecting low parasite levels.
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Oral Presentation given at European Drug Discovery Innovation & Outsourcing Programme on 12th September 2023 in Barcelona. Overview around impact for AstraZeneca R&D from examples in the past 5+ years, including machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, and examples applying AI for right dose - identifying risk factors for CV patients and automated Population PK model prediction.
IRJET- A Survey on Medical Image Interpretation for Predicting Pneumonia
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
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BioAssay Express: Creating and exploiting assay metadata
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Towards automated phenotypic cell profiling with high-content imaging
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Introduction to Machine Learning and Texture Analysis for Lesion Characteriza...
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6 Applications:
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References:
Bichlmayer, E. M., Mahl, L., Hesse, L., Pion, E., Haller, V., Moehwald, A., … & Haerteis, S. (2022). A 3D In Vivo Model for Studying Human Renal Cystic Tissue and Mouse Kidney Slices. Cells, 11(15), 2269.
Faihs, L., Firouz, B., Slezak, P., Slezak, C., Weißensteiner, M., Ebner, T., … & Dungel, P. (2022). A Novel Artificial Intelligence-Based Approach for Quantitative Assessment of Angiogenesis in the Ex Ovo CAM Model. Cancers, 14(17), 4273.
Kuri, P. M., Pion, E., Mahl, L., Kainz, P., Schwarz, S., Brochhausen, C., … & Haerteis, S. (2022). Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model—Establishment and Addition to Laser Speckle Contrast Imaging (LSCI). Cells, 11(15), 2321.
Links:
Visit our website:
www.kmlvision.com
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
Solving the reproducibility problem in biomedical research
The lack of reproducible results in biomedical research has vast negative effects on public health, scientific output, and progress. We have summarised everything you need to know on how to solve the reproducibility problem in biomedical research.
If you want to know more, we dive deeper into this exciting subject in our blogpost https://www.kmlvision.com/solving-the-reproducibility-problem-in-biomedical-research/
References:
Anderson, J. M., Wright, B., Rauh, S., Tritz, D., Horn, J., Parker, I., Bergeron, D., Cook, S., & Vassar, M. (2021). Evaluation of indicators supporting reproducibility and transparency within cardiology literature. Heart (British Cardiac Society), 107(2), 120–126. https://doi.org/10.1136/heartjnl-2020-316519
Association for Computing Machinery (2016). Artifact Review and Badging. Available online at: https://www.acm.org/publications/policies/artifact-review-badging
Baker M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454. https://doi.org/10.1038/533452a
Begley, C. G., & Ioannidis, J. P. (2015). Reproducibility in science: improving the standard for basic and preclinical research. Circulation research, 116(1), 116–126. https://doi.org/10.1161/CIRCRESAHA.114.303819
Bryan, C. J., Yeager, D. S., & O’Brien, J. M. (2019). Replicator degrees of freedom allow publication of misleading failures to replicate. Proceedings of the National Academy of Sciences of the United States of America, 116(51), 25535–25545. https://doi.org/10.1073/pnas.1910951116
Plesser H. E. (2018). Reproducibility vs. Replicability: A Brief History of a Confused Terminology. Frontiers in neuroinformatics, 11, 76. https://doi.org/10.3389/fninf.2017.00076
Serghiou, S., Contopoulos-Ioannidis, D. G., Boyack, K. W., Riedel, N., Wallach, J. D., & Ioannidis, J. P. A. (2021). Assessment of transparency indicators across the biomedical literature: How open is open?. PLoS biology, 19(3), e3001107. https://doi.org/10.1371/journal.pbio.3001107
Links:
Visit our website:
www.kmlvision.com
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
Simplifying N:C Ratio Analysis in Cytology: An Easy Approach Using Image Anal...
Utilizing the advanced deep learning technology available in the IKOSA AI software, we analyzed the N:C ratios in Pap smear samples. We were able to perform a N:C ratio analysis with high accuracy and reproducibility.
If you want to know more our use case, check out our blogpost: https://www.kmlvision.com/simplifying-nc-ratio-analysis-in-cytology-an-easy-approach-using-image-analysis-software/
References:
Sebastian, J. A., Moore, M. J., Berndl, E. S. L., & Kolios, M. C. (2021). An image-based flow cytometric approach to the assessment of the nucleus-to-cytoplasm ratio. PloS one, 16(6), e0253439. https://doi.org/10.1371/journal.pone.0253439
Sung, W. W., Lin, Y. M., Wu, P. R., Yen, H. H., Lai, H. W., Su, T. C., Huang, R. H., Wen, C. K., Chen, C. Y., Chen, C. J., & Yeh, K. T. (2014). High nuclear/cytoplasmic ratio of Cdk1 expression predicts poor prognosis in colorectal cancer patients. BMC cancer, 14, 951. https://doi.org/10.1186/1471-2407-14-951
Links:
Visit our website:
www.kmlvision.com
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
An Example of the Drug Development Process - Infographic
Drug development can be divided into different phases. This infographic provides an overview of the process, including an estimated timeline for each step.
If you want to know more, we dive deep into this exciting subject in our blogpost https://www.kmlvision.com/the-key-to-robust-translational-results-in-preclinical-data-analysis/
References:
Aban, I. B., & George, B. (2015). Statistical considerations for preclinical studies. Experimental neurology, 270, 82–87. https://doi.org/10.1016/j.expneurol.2015.02.024
Choueiry, G. (2023). Quantifying Health. https://quantifyinghealth.com/r-squared-study/
EMA (2023). European Medicines Agency. https://www.ema.europa.eu/en
FDA/Office of the Commissioner. (2023). U.S. Food and Drug Administration. https://www.fda.gov/
Meijering E. (2020). A bird’s-eye view of deep learning in bioimage analysis. Computational and structural biotechnology journal, 18, 2312–2325. https://doi.org/10.1016/j.csbj.2020.08.003
Polson, A. G., & Fuji, R. N. (2012). The successes and limitations of preclinical studies in predicting the pharmacodynamics and safety of cell-surface-targeted biological agents in patients. British Journal of Pharmacology, 166(5), 1600–1602. https://doi.org/10.1111/j.1476-5381.2012.01916.x
Shegokar, R. (2020). Preclinical Testing – Understanding the basics first. In: Ranjita, Shegokar (Editor): Drug Delivery Aspects. Volume 4: Expectations and Realities of Multifunctional Drug Delivery Systems. Science Direct.
Steinmetz, K. L., & Spack, E. G. (2009). The basics of preclinical drug development for neurodegenerative disease indications. BMC Neurology, 9 Suppl 1(Suppl 1), S2. https://doi.org/10.1186/1471-2377-9-S1-S2
Links:
Visit our website:
www.kmlvision.com
CAM Assay: https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3459416089/CAM+Assay+App+Versions
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
6 Steps in Preclinical Research
We summarized 6 Steps in Preclinical Research in this downloadable infographic. Feel free to share it with your colleagues.
If you want to know more, we dive deep into this exciting subject in our blogpost https://www.kmlvision.com/the-key-to-robust-translational-results-in-preclinical-data-analysis/
References:
Aban, I. B., & George, B. (2015). Statistical considerations for preclinical studies. Experimental neurology, 270, 82–87. https://doi.org/10.1016/j.expneurol.2015.02.024
Choueiry, G. (2023). Quantifying Health. https://quantifyinghealth.com/r-squared-study/
EMA (2023). European Medicines Agency. https://www.ema.europa.eu/en
FDA/Office of the Commissioner. (2023). U.S. Food and Drug Administration. https://www.fda.gov/
Meijering E. (2020). A bird’s-eye view of deep learning in bioimage analysis. Computational and structural biotechnology journal, 18, 2312–2325. https://doi.org/10.1016/j.csbj.2020.08.003
Polson, A. G., & Fuji, R. N. (2012). The successes and limitations of preclinical studies in predicting the pharmacodynamics and safety of cell-surface-targeted biological agents in patients. British Journal of Pharmacology, 166(5), 1600–1602. https://doi.org/10.1111/j.1476-5381.2012.01916.x
Shegokar, R. (2020). Preclinical Testing – Understanding the basics first. In: Ranjita, Shegokar (Editor): Drug Delivery Aspects. Volume 4: Expectations and Realities of Multifunctional Drug Delivery Systems. Science Direct.
Steinmetz, K. L., & Spack, E. G. (2009). The basics of preclinical drug development for neurodegenerative disease indications. BMC Neurology, 9 Suppl 1(Suppl 1), S2. https://doi.org/10.1186/1471-2377-9-S1-S2
Links:
Visit our website:
www.kmlvision.com
CAM Assay: https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3459416089/CAM+Assay+App+Versions
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
Preclinical Data Analysis - The Key to Robust Translational Results
We summarized the most important facts about Preclinical Data Analysis and how to obtain robust translational results.
If you want to know more, we dive deep into this exciting subject in our blogpost https://www.kmlvision.com/the-key-to-robust-translational-results-in-preclinical-data-analysis/
References:
Aban, I. B., & George, B. (2015). Statistical considerations for preclinical studies. Experimental neurology, 270, 82–87. https://doi.org/10.1016/j.expneurol.2015.02.024
Choueiry, G. (2023). Quantifying Health. https://quantifyinghealth.com/r-squared-study/
EMA (2023). European Medicines Agency. https://www.ema.europa.eu/en
FDA/Office of the Commissioner. (2023). U.S. Food and Drug Administration. https://www.fda.gov/
Meijering E. (2020). A bird’s-eye view of deep learning in bioimage analysis. Computational and structural biotechnology journal, 18, 2312–2325. https://doi.org/10.1016/j.csbj.2020.08.003
Polson, A. G., & Fuji, R. N. (2012). The successes and limitations of preclinical studies in predicting the pharmacodynamics and safety of cell-surface-targeted biological agents in patients. British Journal of Pharmacology, 166(5), 1600–1602. https://doi.org/10.1111/j.1476-5381.2012.01916.x
Shegokar, R. (2020). Preclinical Testing – Understanding the basics first. In: Ranjita, Shegokar (Editor): Drug Delivery Aspects. Volume 4: Expectations and Realities of Multifunctional Drug Delivery Systems. Science Direct.
Steinmetz, K. L., & Spack, E. G. (2009). The basics of preclinical drug development for neurodegenerative disease indications. BMC Neurology, 9 Suppl 1(Suppl 1), S2. https://doi.org/10.1186/1471-2377-9-S1-S2
Links:
Visit our website:
www.kmlvision.com
CAM Assay: https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3459416089/CAM+Assay+App+Versions
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
Main reasons to use AI-enhanced pathology software .pdf
Whole-slide image analysis has become an integral part of modern life science research practice.
Advanced pathology software solutions currently transform healthcare practice(s).
To provide you with the 5 main reasons why using AI-enhanced pathology software benefits not only your workflow to ensure efficiency in your lab but also makes your business stand out.
If you want to know more about it, we dive deeper into this exciting subject in our blogpost: https://www.kmlvision.com/pathology-software-for-optimal-lab-performance/
Links:
Visit our website:
www.kmlvision.com
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
Spatial Biology - The New Frontier of Microbiology
We summarized the most important facts about state-of-the art spatial biology techniques and their uses in different areas of life science.
If you want to know more, we dive deep into this exciting subject in our blogpost https://www.kmlvision.com/spatial-biology-introducing-spatial-metrics-to-bioimage-analysis/
References:
Carter, P., Presta, L. E. N., Gorman, C. M., Ridgway, J. B., Henner, D., Wong, W. L., … & Shepard, H. M. (1992). Humanization of an anti-p185HER2 antibody for human cancer therapy. Proceedings of the National Academy of Sciences, 89(10), 4285-4289.
Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B., & Livak, K. J. (2021). Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nature reviews. Clinical oncology, 18(4), 244–256. https://doi.org/10.1038/s41571-020-00449-x
Lu, S., Stein, J. E., Rimm, D. L., Wang, D. W., Bell, J. M., Johnson, D. B., Sosman, J. A., Schalper, K. A., Anders, R. A., Wang, H., Hoyt, C., Pardoll, D. M., Danilova, L., & Taube, J. M. (2019). Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA oncology, 5(8), 1195–1204. https://doi.org/10.1001/jamaoncol.2019.1549
Rad, H. S., Rad, H. S., Shiravand, Y., Radfar, P., Arpon, D., Warkiani, M. E., O’Byrne, K., & Kulasinghe, A. (2021). The Pandora’s box of novel technologies that may revolutionize lung cancer. Lung cancer (Amsterdam, Netherlands), 159, 34–41. https://doi.org/10.1016/j.lungcan.2021.06.022
Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and biology insights, 14, 1177932219899051. https://doi.org/10.1177/1177932219899051
Links:
Visit our website:
www.kmlvision.com
Sparkfinder: https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3449618514/Sparkfinder+App+Versions
About the company:
https://www.kmlvision.com/
KML Vision GmbH,
Nikolaiplatz 4, 2nd floor,
8020 Graz, Austria
Contact us:
slideshare@kmlvision.com
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一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(CU毕业证)卡尔顿大学毕业证如何办理
原件一模一样【微信：95270640】【卡尔顿大学毕业证CU学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔顿大学毕业证CU学位证本科学位证成绩单【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
原版办理【微信号:BYZS866】【巴斯大学毕业证(Bath毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路、）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
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Poster - STEP UP YOUR MORPHOLOGICAL CELL PROFILING GAME WITH AI
- 1. STEP UP YOUR MORPHOLOGICAL CELL PROFILING GAME WITH AI Background and Motivation A fast approach for fully-automated Cell Painting image analysis and feature extraction from raw image data Philipp Kainz1 , Bendeguz H. Zovathi1 , Phillip Clarke1 , Maria Roa Oyaga2 , Victor Wong2 , David Egan2 , Thomas Ebner1 1 KML Vision GmbH, 8020 Graz, Austria www.kmlvision.com 2 Core Life Analytics BV, 5211 DA 's-Hertogenbosch, The Netherlands www.corelifeanalytics.com Reproducible morphological profiling, particularly for drug discovery, has become an essential tool for compound evaluation [1]. We describe a novel approach for cell profiling using a fully-automated computer vision approach with IKOSA AI. Our main motivation is to make Cell Painting [2] easily accessible for biologists without computer science knowledge. The primary objective of this work is to create a parameter-free, robust, deep learning-based computer vision algorithm to achieve precise object segmentations for fully-automated high-content imaging. Materials and Methods Results Conclusions and Outlook Validation dataset Cells (n=62,560) Nuclei (n=58,290) Precision 0.97 0.98 Recall 0.89 0.94 Average Precision 0.86 0.92 IoU 0.86 0.91 [1] S. N. Chandrasekaran et al., bioRxiv 2022.01.05.475090, 2023. [2] M.-A. Bray et al., Nature Protocols, 11(9):1757–1774, 2016. [3] Cell Painting Gallery, Available: https://registry.opendata.aws/cellpainting-gallery/, Accessed: May 2023. [4] KML Vision GmbH, IKOSA (software), Graz, Austria, software available at https://app.ikosa.ai, Accessed: May 2023. [5] AE. Carpenter et al., Genome Biology, 7:1-11, 2006. [6] Core Life Analytics, Available: https://corelifeanalytics.com, Accessed: May 2023. ground truth image (nucleus) Our approach (nucleus) ground truth image (cell) Our approach (cell) BR00117054-C21-5 Cell segmentation BR00116991-N15-5 Highlights Nucleus segmentation DMSO (negative control) Homoharringtonine Web-based software platform High availability. Access anywhere, anytime. Fully-automated feature extraction No programming knowledge required StratoMineR integration Optimized feature selection Robust computer vision pipeline Easily transfer to other datasets We expect a significant impact on resource efficiency in drug discovery and personalized medicine. Cell Painting will be available soon on the IKOSA platform for high-throughput scenarios. True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%) True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%) True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%) True-Positive (overlap≥50%) False-Positive (overlap<50%) False-Negative (overlap<50%) References Sign-up for your IKOSA AI free trial today! View the poster online higher is better higher is better higher is better Intersection over Union, higher is better Ground truth label generation from Cell Painting Gallery JUMP-CP pilot dataset [3] using U2OS and A549 cell lines; 5 fluorescent (Mito, AGP, RNA, ER, DNA) and 3 brightfield channels Training a deep learning instance segmentation model using the IKOSA AI software [4] on a well-defined, diverse subset Morphological feature extraction based on CellProfiler [5] cell painting parameter groups Feature prioritization using StratoMineR software [6] by Core Life Analytics (SLAS EU 2023 poster number: 1084-A) Validation & quality control: Compare IKOSA AI with the CellProfiler output