Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

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Enterprise Labeling Software Market Size 2024-2028

The enterprise labeling software market size is forecast to increase by USD 133.9 mn at a CAGR of 6.59% between 2023 and 2028.

The market is witnessing significant growth due to several key trends. The adoption of enterprise labeling solutions is increasing as businesses seek to streamline their labeling processes and improve efficiency. Dynamic labeling, which allows for real-time updates to labels, is gaining popularity as it enables companies to quickly respond to changing regulations or product information. The market is experiencing growth as companies leverage data integration and analytics to streamline labeling processes, ensuring greater accuracy, compliance, and operational efficiency. Moreover, stringent government regulations mandating accurate and compliant labeling are driving the need for enterprise labeling software. These factors are expected to fuel market growth In the coming years. The market landscape is constantly evolving, and staying abreast of these trends is essential for businesses looking to remain competitive.

What will be the Size of the Enterprise Labeling Software Market During the Forecast Period?

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The market encompasses solutions designed for creating, managing, and printing labels in various industries. Compliance with regulations and ensuring labeling accuracy are key drivers for this market. Real-time updates and customizable templates enable businesses to maintain consistency and adapt to changing requirements. Integration capabilities with enterprise systems, data management planning, and the printing process are essential for streamlining workflows and improving efficiency. Innovative technology, such as automation and machine learning, enhances labeling quality and speed, providing a competitive edge.
A user-friendly interface and economic conditions influence market demand. Urbanization and the growing need for packaging solutions, branding, and on-premises-based software further expand the market's reach. Overall, the market continues to grow, offering significant benefits to businesses seeking to optimize their labeling processes.

How is this Enterprise Labeling Software Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premise
  Cloud


End-user

  FMCG
  Retail and e-commerce
  Healthcare
  Warehousing and logistics
  Others


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  Middle East and Africa



  South America

By Deployment Insights

The on-premise segment is estimated to witness significant growth during the forecast period.

The market is driven by the need for compliance, creation, management, printing, and real-time updates of labels in various industries. Large enterprises require unique labeling solutions to meet diverse industry standards and traceability regulations, ensuring product quality and customer satisfaction. On-premise and cloud-based enterprise labeling software offer agility, scalability, and flexibility, optimizing operations and enhancing resilience and adaptability. Compliance management, seamless collaboration, contactless processes, safety measures, and predictive analytics are key features. Driving factors include digitalization, automation, and evolving challenges in logistics and e-commerce. However, varying industry standards, implementation costs, legacy systems, and integration challenges pose restraining factors. Enterprise labeling software solutions offer customizable templates, integration capabilities, and language support, catering to the economic condition, urbanization, and packaging solutions.

Brands prioritize a data-driven approach and regulatory requirements In their labeling strategy. The market is expected to grow, with key players catering to enterprise sizes and time to market.

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The On-premise segment was valued at USD 163.80 mn in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 41% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in APAC is projected to experience significant growth due to the increasing number of end-users in sectors such as food and beverage, personal care products, and pharmaceuticals. This tren

Cloud Labeling Software Market Outlook

According to our latest research, the global cloud labeling software market size reached USD 1.24 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to expand at a CAGR of 11.6% from 2025 to 2033, achieving a forecasted value of USD 3.09 billion by 2033. This impressive growth trajectory is primarily driven by the increasing digital transformation initiatives, stringent regulatory compliance requirements, and the surge in demand for efficient supply chain and inventory management solutions worldwide.

A key growth factor propelling the cloud labeling software market is the escalating need for real-time, automated, and centralized labeling solutions across global supply chains. As businesses increasingly operate in complex, multi-location environments, the importance of cloud-based platforms capable of managing, updating, and standardizing labels remotely has become paramount. Cloud labeling software enables organizations to maintain consistency, ensure compliance, and reduce manual intervention, thereby minimizing errors and operational costs. Furthermore, the integration of these platforms with enterprise resource planning (ERP) and warehouse management systems (WMS) enhances overall efficiency, supporting industries in meeting the dynamic demands of global commerce.

Another significant driver is the growing emphasis on regulatory compliance and traceability, particularly in sectors such as healthcare, food & beverage, and pharmaceuticals. The proliferation of international trade and evolving labeling standards necessitate agile software solutions that can swiftly adapt to new regulations. Cloud labeling software provides the flexibility to update label templates and data in real-time, ensuring all products meet the latest compliance requirements. This adaptability not only mitigates the risk of costly recalls or penalties but also bolsters consumer trust in product authenticity and safety. As governments and industry bodies continue to tighten labeling mandates, demand for advanced cloud-based labeling platforms is set to intensify.

Furthermore, the adoption of cloud labeling software is fueled by the rapid digitalization of small and medium enterprises (SMEs) seeking scalable, cost-effective solutions. Unlike traditional on-premises systems, cloud-based platforms offer lower upfront costs, seamless updates, and the ability to support remote workforces. These features are particularly attractive to SMEs with limited IT resources, allowing them to compete more effectively with larger enterprises. The proliferation of Internet of Things (IoT) devices and connected packaging further amplifies the need for dynamic labeling solutions that can accommodate real-time data and personalized labeling at scale, thus creating new avenues for market expansion.

From a regional perspective, North America currently dominates the cloud labeling software market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced IT infrastructure, high adoption rates of cloud technologies, and stringent regulatory landscape. However, Asia Pacific is poised for the fastest growth over the forecast period, driven by rapid industrialization, expanding e-commerce, and increasing investments in digital transformation across emerging economies such as China and India. Europe also represents a significant market, propelled by strong manufacturing and logistics sectors, alongside a growing focus on sustainability and traceability in supply chains.

Component Analysis

The cloud labeling software market is segmented by component into software and services, with each playing a pivotal role in the ecosystem. The software segment encompasses core labeling platforms, including label design, template management, integration capabilities, and real-time data synchronization. These solutions are designed to automate and streamline the entire labeling process, ensuring consistency and complianc

Medical Device Labeling Software Market Outlook

According to our latest research, the global medical device labeling software market size reached USD 1.21 billion in 2024, demonstrating robust momentum across all major geographies. The market is projected to expand at a CAGR of 7.8% from 2025 to 2033, with the total market value anticipated to reach USD 2.39 billion by 2033. The primary growth driver is the escalating demand for regulatory compliance and traceability in medical device manufacturing, as well as the increasing globalization of the healthcare supply chain. The market’s expansion is further propelled by the growing complexity of labeling requirements and the integration of advanced digital solutions for efficient label management.

The medical device labeling software market is experiencing significant growth due to the ever-tightening regulatory frameworks established by authorities such as the US FDA, European Medicines Agency, and other global regulatory bodies. These agencies mandate stringent labeling requirements to ensure patient safety, effective product tracking, and swift recall management. Consequently, medical device manufacturers are increasingly adopting sophisticated labeling software solutions to automate labeling processes, maintain compliance, and minimize the risk of costly errors. The proliferation of unique device identification (UDI) mandates, serialization, and the need for real-time updates across diverse product lines have further intensified the demand for comprehensive labeling software platforms that can seamlessly handle these intricate requirements.

Another critical growth factor is the rapid digital transformation within the healthcare and life sciences sectors. The adoption of cloud computing, artificial intelligence, and machine learning in labeling software is revolutionizing the way organizations manage their labeling operations. Cloud-based solutions offer scalability, remote accessibility, and centralized control, enabling organizations to maintain consistent and compliant labeling practices across multiple facilities and regions. Integration with enterprise resource planning (ERP) and product lifecycle management (PLM) systems is streamlining workflows, reducing manual intervention, and enhancing operational efficiency. As a result, both large enterprises and small to medium-sized businesses are increasingly investing in advanced labeling software to gain a competitive edge, ensure accuracy, and accelerate time-to-market for new medical devices.

The market’s growth is also fueled by the rising demand for personalized medicine and customized medical devices, which necessitate dynamic and adaptive labeling capabilities. With the expansion of global healthcare markets and the increasing prevalence of chronic diseases, the volume and diversity of medical devices continue to rise. This trend amplifies the need for software that can manage multilingual labeling, variable data printing, and region-specific regulatory content. Furthermore, the emphasis on patient-centric care and transparency is driving manufacturers to provide clear, accessible, and accurate labeling information, further boosting the adoption of sophisticated labeling software solutions.

From a regional perspective, North America currently dominates the medical device labeling software market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The North American market benefits from a highly regulated environment, a large concentration of medical device manufacturers, and early adoption of digital labeling technologies. Europe’s growth is underpinned by the implementation of the Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR), while Asia Pacific is witnessing rapid expansion due to increasing healthcare investments, the proliferation of local manufacturing, and growing export activities. Latin America and the Middle East & Africa are emerging as promising markets, driven by improving healthcare infrastructure and rising awareness of global compliance standards.

Manually disambiguated ground-truth for the Gnome GTK project supporting the replication of the results presented in the article "gambit – An Open Source Name Disambiguation Tool for Version Control Systems".

Please request access via zenodo.

Scientific services in the area of OMICS research is becoming increasingly popular. In gen- eral omics research can produce a massive amount of data that can pose a challenge for computing infrastructure. While in the genomics area, many applications can run on Linux nodes the situation in proteomics is different. In proteomics, many applications are optimized to run on Windows computer only. As a sci- entific service provider, a core facility needs reliable, reproducible and easy to use integrated solutions. Liquid chromatography mass spectrometry intensity based label-free quantifica- tion using data-dependent acquisition is a popular approach in proteomics to perform relative quantification of proteins in complex samples. MaxQuant is a widely used software for this type of analysis which has a complex graphical user interface and provides information-rich outputs. We run it in which also includes Scaffold for search result validation and visualization and an R based quality control report generation. Data analysis workflows consists of several components: a workflow engine, compute hosts, and archives. In particular, applications can run on compute hosts, while the data is kept in an archive server. Therefore, the input and output need to be staged to the compute host and the results need to be staged back to the archive. This complexity can be overwhelming for a most common user. These different components have all been integrated into a robust and user-friendly application to process standardized label-free quantification experiments. We integrated MaxQuant as an in-house Software as a Service application so it can be used by any workflow engine in a platform-independent manner. In this manuscript, we provide a technical description of how MaxQuant as software service has been integrated into our heterogeneous compute environment for reproducible and automatic large scale high throughput data processing of label-free quantification experiments. In this Pride dataset we provide four raw files along with the full MaxQuant results, the Scaffold file, the QC-pdf report to have a concrete idea of the potential of our workflow. These data are generated in the FGCZ-course in Nov. 2016 (for further information see: http://www.fgcz.ch/education/genomics-courses01.html).

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Annotation Workforce Management Software Market Outlook

As per our latest research, the global Annotation Workforce Management Software market size reached USD 1.48 billion in 2024, demonstrating robust momentum driven by the accelerated adoption of artificial intelligence and machine learning technologies across various industries. The market is set to expand at a CAGR of 20.7% from 2025 to 2033, projecting a value of approximately USD 9.19 billion by 2033. This surge is primarily fueled by the growing need for high-quality annotated datasets to support the development and deployment of advanced AI models, alongside the increasing complexity and scale of data labeling requirements globally.

The primary growth factor for the Annotation Workforce Management Software market is the exponential rise in demand for annotated data, which forms the backbone of supervised learning in AI and machine learning applications. Organizations across sectors such as healthcare, automotive, and retail are investing heavily in AI-driven solutions, and the quality of these solutions is intrinsically linked to the accuracy and efficiency of data annotation. As AI models become more sophisticated, the volume and complexity of data requiring annotation have also increased, prompting enterprises to seek robust workforce management solutions that can streamline task allocation, monitor annotator productivity, and ensure high-quality outcomes. This has led to the proliferation of specialized software platforms designed to manage large, distributed annotation teams, facilitate collaboration, and maintain stringent quality control standards.

Another significant driver is the rapid digital transformation initiatives being undertaken by businesses worldwide. The shift towards automation, predictive analytics, and personalized services has amplified the need for scalable annotation workforce management tools. These platforms not only enable organizations to manage remote and hybrid annotation teams efficiently but also help in optimizing operational costs by leveraging automation, analytics, and integration with existing enterprise systems. The increasing prevalence of cloud-based deployment further enhances accessibility, allowing organizations to scale their annotation workforce dynamically in response to fluctuating project demands. As a result, annotation workforce management software is becoming a strategic investment for enterprises aiming to derive maximum value from their AI initiatives.

Furthermore, the market is benefiting from the growing emphasis on data privacy and regulatory compliance, particularly in sectors handling sensitive information such as healthcare and finance. Annotation workforce management software often incorporates features that facilitate compliance with data protection regulations, including role-based access controls, audit trails, and secure data handling protocols. This not only mitigates the risk of data breaches but also instills confidence among clients and regulatory bodies. As organizations continue to navigate an evolving regulatory landscape, the demand for annotation workforce management solutions that can ensure both efficiency and compliance is expected to rise significantly, further propelling market growth.

Regionally, North America maintains its dominance in the Annotation Workforce Management Software market, accounting for a substantial share of global revenue in 2024. The region’s leadership is attributed to the early adoption of AI technologies, a mature IT infrastructure, and a strong presence of leading technology companies. Europe follows closely, driven by increased investments in AI research and stringent data privacy regulations that necessitate robust workforce management solutions. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, a burgeoning AI startup ecosystem, and government initiatives promoting AI adoption. Latin America and the Middle East & Africa, while currently representing smaller shares, are poised for steady growth as local enterprises ramp up their AI capabilities and seek efficient annotation workforce management solutions.

Label Management Software Market Outlook

According to our latest research, the global label management software market size reached USD 2.18 billion in 2024, reflecting the sector’s robust expansion driven by increasing automation and regulatory compliance needs across industries. The market is projected to grow at a CAGR of 10.2% during the forecast period, with expectations to reach USD 5.23 billion by 2033. The primary growth factor is the accelerated adoption of digital solutions for label design, printing, and lifecycle management, particularly in industries such as pharmaceuticals, food and beverage, and logistics, where traceability and compliance are critical.

The expansion of the label management software market is significantly fueled by the rising complexity of global supply chains and the need for real-time, accurate labeling solutions. As manufacturers and retailers face stringent international regulations regarding product information, traceability, and serialization, there is an increasing reliance on advanced software platforms that can centralize and standardize label management. These platforms not only help organizations maintain compliance across multiple jurisdictions but also reduce operational costs by minimizing labeling errors, improving workflow automation, and enabling remote management capabilities. Furthermore, the integration of label management software with enterprise resource planning (ERP) and warehouse management systems (WMS) is streamlining end-to-end supply chain operations, making it a vital tool for organizations aiming for operational excellence.

Another significant growth driver for the label management software market is the surge in demand for personalized and variable data labeling, particularly in the food and beverage, retail, and healthcare sectors. Consumer preferences are shifting towards products with transparent labeling, including nutritional information, allergen declarations, and sustainability credentials. As a result, companies are investing in software solutions that facilitate rapid label changes, localization, and customization to cater to diverse markets and regulatory environments. The proliferation of e-commerce and omnichannel retailing has further amplified the need for flexible labeling processes, as businesses strive to enhance customer experience and meet evolving regulatory requirements. This trend is expected to intensify over the next decade, further propelling market growth.

The ongoing digital transformation across industries has also contributed to the rapid adoption of cloud-based label management software, which offers scalability, remote accessibility, and enhanced security features. Cloud deployment models are particularly attractive to small and medium enterprises (SMEs) due to their lower upfront costs and reduced IT infrastructure requirements. Additionally, the advancement of technologies such as the Internet of Things (IoT) and artificial intelligence (AI) is enabling real-time monitoring and predictive analytics in label management, helping organizations optimize their labeling processes and proactively address compliance risks. These technological innovations are expected to create new growth avenues for market players in the coming years.

Regionally, North America continues to dominate the label management software market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption rate of automation technologies, stringent regulatory frameworks, and the presence of leading software vendors have contributed to the region’s leadership. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid industrialization, increasing investments in digital infrastructure, and the rising demand for efficient supply chain management solutions. Latin America and the Middle East & Africa are also emerging as promising markets, fueled by expanding manufacturing sectors and the growing need for regulatory compliance.

Component Analysis

The label management software market is segmented by comp

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to fully understand the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without prior knowledge of the type of modification. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased approach of FLEXIQuant-LF to assess the modification extent in quantitative proteomics data provides a novel platform to better understand the function and regulation of PTMs in new experiments and reanalyzed data. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

Cloud RIP for Variable Data Labels Market Outlook

According to our latest research, the global Cloud RIP for Variable Data Labels market size reached USD 1.21 billion in 2024, reflecting robust demand across diverse industry verticals. The market is expected to grow at a remarkable CAGR of 12.6% from 2025 to 2033, projecting a value of USD 3.56 billion by 2033. This growth is primarily driven by the increasing need for highly personalized, data-driven labeling solutions and the rapid adoption of cloud-based technologies by enterprises seeking operational efficiency and scalability.

One of the most significant growth factors for the Cloud RIP for Variable Data Labels market is the surging demand for customization and personalization in product labeling, especially in sectors such as packaging, food and beverage, and pharmaceuticals. Brands are increasingly leveraging variable data labels to enhance customer engagement, improve traceability, and comply with stringent regulatory requirements. The ability of Cloud RIP (Raster Image Processor) solutions to efficiently process complex variable data and deliver high-quality, on-demand labels is a key driver, allowing businesses to respond swiftly to market trends and consumer preferences. Moreover, the rise in e-commerce and direct-to-consumer sales models has amplified the need for agile labeling processes that can handle frequent changes in product information, batch codes, and promotional content.

Another major contributor to market expansion is the shift towards cloud-based deployment models, which offer unparalleled scalability, flexibility, and cost-effectiveness compared to traditional on-premises solutions. Cloud RIP solutions enable enterprises to centralize label management, streamline workflows, and facilitate remote collaboration among stakeholders. This is particularly valuable for multinational companies and organizations with distributed operations, as it reduces IT overhead, enhances data security, and accelerates time-to-market. Furthermore, advancements in cloud security, integration capabilities with ERP and supply chain systems, and support for emerging printing technologies are further fueling adoption across both large enterprises and SMEs.

Technological innovation remains at the core of growth for the Cloud RIP for Variable Data Labels market. The integration of artificial intelligence, machine learning algorithms, and real-time analytics within RIP platforms is enabling advanced features such as predictive maintenance, print optimization, and automated error detection. These capabilities not only improve print quality and operational efficiency but also empower businesses to derive actionable insights from labeling data. Additionally, the proliferation of IoT-enabled printing devices and the move towards Industry 4.0 are creating new opportunities for cloud-based RIP solutions to deliver end-to-end automation and connectivity across the labeling value chain.

Regionally, North America continues to dominate the Cloud RIP for Variable Data Labels market, driven by early technology adoption, a strong presence of key market players, and high demand from the packaging and retail sectors. However, the Asia Pacific region is emerging as the fastest-growing market, supported by rapid industrialization, expanding manufacturing bases, and increasing investments in digital transformation. Europe also holds a significant share, propelled by stringent labeling regulations and a mature printing industry. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, primarily fueled by the modernization of logistics and supply chain infrastructures.

Component Analysis

The Component segment of the Cloud RIP for Variable Data Labels market is categorized into software, hardware, and services, each playing a pivotal role in enabling seamless variable data label production. Software solutions are the backbone of this segment, offering advanced RIP functionalities such as high-speed image p

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and ..., , The program is tested with the following setting:

python==3.9.5 tf-nightly-gpu==2.7.0.dev20210730 keras-nightly==2.7.0.dev2021073000 tensorflow-model-optimization==0.6.0 numpy==1.19.5 scipy==1.6.2 matplotlib==3.3.4 pandas==1.3.0 pyyaml==5.4.1

Newer versions may also work. Â

When data and software files are downloaded, please unzip to a shared folder so that the computer code will work properly.

root directory: contain main routine scripts to train neural networks (NNs), and README.md (this file).

 ./s_toy_routine.py: Python script to train NNs on the toy experiment.  ./s_basic_routine.py: Python script to train NNs on the ECAL experiment.  ./README.md: This file.

./conf/ directory: configuration files for main routine scripts.

 laser_in2048_[cluster]_[frequency]_2ch_internal.yaml: Configuration files for the toy experiment. Use optional [cluster] to select data, use opitional low-pass filter with [frequency] to preprocess data.  `ecal_[network]_in800_8ch_intern..., # Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Introduction

This repository holds the computer code and raw data to reproduce the results in the paper: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.

In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event da...

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC–MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant’s Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC.

Large-scale proteome analysis requires rapid and high-throughput analytical methods. We recently reported a new paradigm in proteome analysis where direct infusion and ion mobility are used instead of liquid chromatography (LC) to achieve rapid and high-throughput proteome analysis. Here, we introduce an improved direct infusion shotgun proteome analysis protocol including label-free quantification (DISPA-LFQ) using CsoDIAq software. With CsoDIAq analysis of DISPA data, we can now identify up to ∼2000 proteins from the HeLa and 293T proteomes, and with DISPA-LFQ, we can quantify ∼1000 proteins from no more than 1 μg of sample within minutes. The identified proteins are involved in numerous valuable pathways including central carbon metabolism, nucleic acid replication and transport, protein synthesis, and endocytosis. Together with a high-throughput sample preparation method in a 96-well plate, we further demonstrate the utility of this technology for performing high-throughput drug analysis in human 293T cells. The total time for data collection from a whole 96-well plate is approximately 8 h. We conclude that the DISPA-LFQ strategy presents a valuable tool for fast identification and quantification of proteins in complex mixtures, which will power a high-throughput proteomic era of drug screening, biomarker discovery, and clinical analysis.

Abstract The software IPRDataCol was developed in 2017 at the Instituto Agronômico do Paraná (IAPAR) by a research group (Nucleo1) studying information technology applied to agricultural research. Due to the software architecture, whether the label-generating nor the data capture module use any native database, i.e, the program can be used directly from portable storage devices with flash memory. It is designed as a support tool to meet the needs of different research projects, regarding optimization in the identification of experimental plots in the field or laboratory/greenhouse, with subsequent logging of yield data measured by electronic scales, and storage in digital files. The software can be downloaded free of charge from the internet (http://nucleo1.org), without registration. The use of this program will contribute to minimize errors in data acquisition since the typing phase is not necessary for yield data measured by electronic scales.

High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments.

Private Label Management Software Market Outlook

According to our latest research, the global Private Label Management Software market size reached USD 1.27 billion in 2024, with robust growth driven by increasing digital transformation across retail and manufacturing sectors. The market is expected to expand at a CAGR of 12.5% from 2025 to 2033, reaching a forecasted value of USD 3.65 billion by 2033. This growth is primarily propelled by the rising demand for efficient product lifecycle management, inventory optimization, and compliance automation among retailers, manufacturers, and e-commerce companies worldwide. As per the latest research, the adoption of advanced software solutions for private label management is accelerating, underpinned by the need for enhanced supply chain visibility and faster time-to-market for private label products.

One of the key growth factors for the Private Label Management Software market is the increasing complexity of private label supply chains. As private label brands become more prominent, especially among large retailers and e-commerce giants, the need for robust management tools to oversee product development, supplier relationships, and regulatory compliance has intensified. Private label management software streamlines these processes by providing integrated platforms for product data management, supplier onboarding, and real-time inventory tracking. This leads to improved operational efficiency, reduced costs, and faster response times to market changes. Furthermore, the software enables better collaboration between internal teams and external partners, ensuring that products meet quality standards and regulatory requirements from inception to shelf.

Another significant driver is the rapid digitalization of the retail and manufacturing industries. The proliferation of e-commerce platforms and omnichannel retail strategies has created a highly competitive environment, where speed and agility are crucial. Private label management software empowers businesses to quickly launch new products, adapt to shifting consumer preferences, and manage multiple SKUs across various sales channels. The integration of advanced analytics and artificial intelligence within these platforms also provides actionable insights into product performance, customer behavior, and supply chain bottlenecks. This data-driven approach allows organizations to make informed decisions, optimize their product portfolios, and maximize profitability, further fueling the adoption of private label management solutions.

Additionally, regulatory compliance and risk management are becoming increasingly important in the global private label landscape. With evolving food safety standards, sustainability requirements, and labeling regulations, companies must ensure that their private label products adhere to all applicable laws. Private label management software offers automated compliance checks, document management, and audit trails, minimizing the risk of non-compliance and associated penalties. This capability is especially valuable for multinational organizations operating in multiple jurisdictions. As a result, the demand for comprehensive compliance management features within private label software solutions continues to rise, contributing to the overall market growth.

From a regional perspective, North America currently dominates the Private Label Management Software market, accounting for the largest share in 2024. This leadership is attributed to the presence of major retail and e-commerce players, high technology adoption rates, and a mature regulatory environment. Europe follows closely, driven by the strong private label culture among supermarkets and increasing regulatory scrutiny. The Asia Pacific region is witnessing the fastest growth, fueled by the rapid expansion of retail infrastructure, rising consumer demand for private label products, and government initiatives promoting digitalization. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing retail sectors and increasing awareness of the benefits of private label management solutions.

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Warning Labels and Stickers Market 2024-2028

The warning labels and stickers market size is estimated to grow at a CAGR of 5.27% between 2023 and 2028. The market size is forecast to increase by USD 4.25 billion. The growth of the market depends on several factors such as the increasing regulations and safety standards across end-users, the expanding international trade and product distribution, and the advancements in label printing technologies and materials. Our report examines historical data from 2018-2022, besides analyzing the current market scenario.

This report extensively covers market segmentation by end-user (pharmaceuticals, tobacco, food and beverages, chemicals, and others), type (chemical labels, hazardous labels, electrical labels, and custom labels), and geography (APAC, Europe, North America, Middle East and Africa, and South America). It also includes an in-depth analysis of drivers, trends, and challenges.

What will be the Size of the Warning Labels And Stickers Market During the Forecast Period?

Warning Labels and Stickers Market Forecast 2024-2028

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Warning Labels and Stickers Market Dynamic

Our researchers studied the data for years, with 2023 as the base year and 2024 as the estimated year, and presented the key drivers, trends, and challenges for the market. Although there has been a disruption in the growth of the market during the COVID-19 pandemic, a holistic analysis of drivers, trends, and challenges will help companies refine marketing strategies to gain a competitive advantage.

Driver- Increasing regulations and safety standards across end-users

The implementation of laws and safety standards aims to enhance consumer safety, furnish essential information, and guarantee that potential hazards are suitably conveyed. Government agencies from all over the world play a crucial role in establishing and upholding these regulations, which in turn fuels demand for stickers and warning labels to satisfy compliance needs.

Furthermore, compliance is a key factor driving the market because of these regulatory obligations, which also generate a steady and increasing demand for digital label printing solutions. To comply with these rules and uphold the safety and trust of their customers, businesses must invest in high-quality, compliant labeling. Increasing regulations and safety standards across end-users will drive the growth of the market during the forecast period. Digital label printing solutions offer flexibility, efficiency, and the ability to produce labels with high precision and customization. These technologies enable businesses to meet regulatory requirements while maintaining product differentiation and brand integrity. As industries prioritize compliance and consumer safety, the adoption of digital label printing solutions is expected to rise, supporting the market's expansion as businesses seek reliable and versatile labeling solutions.

Trends- Incorporating Near Field Communication (NFC), Radio-Frequency Identification (RFID), or Quick Response (QR) codes into labels

Global government agencies are placing increasing emphasis on using these technologies to improve product traceability and safety. For example, the food labeling requirements of the European Union (EU) mandate the use of QR codes to transmit comprehensive product information, encompassing allergens and nutritional details.

Moreover, this trend facilitates regulatory compliance and tracking across the supply chain in addition to providing consumers with better access to crucial product data. Thus, incorporating NFC, RFID, or QR codes into labels is a trend that can drive the growth of the market in focus during the forecast period. Enterprise labeling software plays a crucial role in enabling businesses to implement these advanced labeling technologies effectively. This software facilitates the design, printing, and management of labels that incorporate NFC, RFID, or QR codes, ensuring accurate information dissemination and compliance with regulatory requirements. By integrating enterprise labeling software, organizations can streamline label creation processes, enhance supply chain visibility, and improve operational efficiency. As industries increasingly adopt digital solutions for labeling and data management, the demand for enterprise labeling software is expected to grow, supporting the market's expansion as businesses seek robust solutions for modern labeling challenges.

Challenge- Risk of counterfeit warning labels and stickers

The prevalence of counterfeit warning labels and stickers on the international market compromises the security of products and damages consumer confidence in brands. Counterfeit labels frequently exclude vital safety information and are not authentic, endangering customers and harming the standing of respectable companies.

Moreover, this challenge is especially

Arc‑Flash Boundary Labeling Programs Market Outlook

According to our latest research, the global Arc‑Flash Boundary Labeling Programs market size reached USD 1.17 billion in 2024, with a robust compound annual growth rate (CAGR) of 7.8% projected through the forecast period. By 2033, the market is expected to attain a value of USD 2.37 billion, driven by increasing regulatory compliance demands, heightened workplace safety awareness, and technological advancements in labeling solutions. The market’s growth is further propelled by expanding industrialization and a rising emphasis on risk mitigation across diverse sectors.

Growing awareness regarding electrical safety standards is a significant driver for the Arc‑Flash Boundary Labeling Programs market. Organizations across industries are increasingly recognizing the critical importance of adhering to safety protocols to prevent arc-flash incidents, which can result in severe injuries, fatalities, and substantial financial losses. Regulatory bodies such as OSHA and NFPA have mandated strict compliance with arc-flash labeling requirements, compelling enterprises to implement robust labeling programs. This regulatory push, combined with the heightened focus on worker safety, is fostering widespread adoption of advanced arc-flash boundary labeling solutions. Additionally, as industries expand and upgrade their electrical infrastructure, the need for clear, durable, and compliant labeling becomes even more pronounced, further accelerating market growth.

Technological innovation is transforming the landscape of arc-flash boundary labeling programs. The integration of software-driven solutions with hardware components allows for automated, accurate, and easily updatable labeling systems, reducing human error and ensuring consistent compliance. The emergence of cloud-based platforms enables centralized management of labeling data, facilitating real-time updates, remote access, and seamless integration with other safety management systems. These advancements not only enhance the efficiency and reliability of labeling programs but also support scalability across multi-site operations. As a result, organizations are increasingly investing in modern, technology-enabled labeling solutions to streamline safety processes, improve operational efficiency, and minimize risk exposure.

The expansion of industries such as manufacturing, utilities, and data centers is another key factor fueling the growth of the Arc‑Flash Boundary Labeling Programs market. As these sectors continue to scale their operations and adopt more complex electrical systems, the risk of arc-flash incidents escalates, necessitating comprehensive safety measures. The proliferation of high-density electrical equipment in data centers and the ongoing modernization of utility infrastructure are driving demand for specialized labeling solutions tailored to unique operational environments. Moreover, the growing trend towards digital transformation and automation in industrial settings is creating new opportunities for innovative labeling technologies, further propelling market expansion.

Regionally, North America remains the dominant market for Arc‑Flash Boundary Labeling Programs, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong regulatory framework, high levels of industrialization, and proactive adoption of advanced safety solutions in North America underpin its leadership position. Europe’s market is propelled by stringent safety norms and a mature industrial base, while rapid industrial growth and increasing safety awareness are driving demand in Asia Pacific. Emerging markets in Latin America and the Middle East & Africa are also witnessing steady growth, supported by infrastructure development and rising investments in workplace safety initiatives.

Component Analysis

The Arc‑Flash Boundary Labeling Programs market is segmented by component into software, services, and hardware, each playing a pivotal

Chemical cross-linking combined with mass spectrometry provides a powerful method for identifying protein−protein interactions and probing the structure of protein complexes. A number of strategies have been reported that take advantage of the high sensitivity and high resolution of modern mass spectrometers. Approaches typically include synthesis of novel cross-linking compounds, and/or isotopic labeling of the cross-linking reagent and/or protein, and label-free methods. We report Xlink-Identifier, a comprehensive data analysis platform that has been developed to support label-free analyses. It can identify interpeptide, intrapeptide, and deadend cross-links as well as underivatized peptides. The software streamlines data preprocessing, peptide scoring, and visualization and provides an overall data analysis strategy for studying protein−protein interactions and protein structure using mass spectrometry. The software has been evaluated using a custom synthesized cross-linking reagent that features an enrichment tag. Xlink-Identifier offers the potential to perform large-scale identifications of protein−protein interactions using tandem mass spectrometry.