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## Overview
DYX is a dataset for classification tasks - it contains CLASS annotations for 277 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Maximum LOD scores at DYX loci in the original dataset ‘r’ indicates maximization of lod score under recessive model, ‘d’ under dominant model.
This dataset includes NEXRAD Reflectivity and Velocity Imagery from the VORTEX2 field catalogs. These imagery products are taken from the NEXRAD station located in Abilene/Moran, TX during 2009-04-14 through 2009-06-15, and 2010-04-14 through 2010-06-21 VORTEX 2 periods. Images are only available during these time periods. The files are in .gif format. See the catalog links in the related links section below to access the field catalogs.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Prices for DXY Dollar Index including live quotes, historical charts and news. DXY Dollar Index was last updated by Trading Economics this August 2 of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
DTx is a dataset for object detection tasks - it contains DTxG annotations for 1,403 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
Daily sample data for STOXX Europe 50 Index DTX timestamped in Chicago time
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Explore the historical Whois records related to dyx.wang (Domain). Get insights into ownership history and changes over time.
Tick (trades only) sample data for STOXX Europe 50 Index DTX timestamped in Chicago time
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Cytoplasmic assembly of ciliary dyneins, a process known as preassembly, requires numerous non-dynein proteins, but the identities and functions of these proteins are not fully elucidated. Here, we show that the classical Chlamydomonas motility mutant pf23 is defective in the Chlamydomonas homolog of DYX1C1. The pf23 mutant has a 494 bp deletion in the DYX1C1 gene and expresses a shorter DYX1C1 protein in the cytoplasm. Structural analyses, using cryo-ET, reveal that pf23 axonemes lack most of the inner dynein arms. Spectral counting confirms that DYX1C1 is essential for the assembly of the majority of ciliary inner dynein arms (IDA) as well as a fraction of the outer dynein arms (ODA). A C-terminal truncation of DYX1C1 shows a reduction in a subset of these ciliary IDAs. Sucrose gradients of cytoplasmic extracts show that preassembled ciliary dyneins are reduced compared to wild-type, which suggests an important role in dynein complex stability. The role of PF23/DYX1C1 remains unknown, but we suggest that DYX1C1 could provide a scaffold for macromolecular assembly.
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A family of DyX4M(12-MCMnIII(N)shi-4) compounds were synthesized and magnetically characterized (X = salicylate, acetate, benzoate, trimethylacetate, M = NaI or KI). The bridging ligands were systematically varied while keeping the remainder of the MC-geometry constant. The type of monovalent cation, necessary for charge balance, was also altered. The dc magnetization and susceptibility of all compounds were similar across the series. Regardless of the identity of the countercation, the Dy(Hsal)4M 12-MC-4 compounds were the only compounds to show frequency-dependent ac magnetic susceptibility, a hallmark of single-molecule magnetism. This indicates that the nature of the bridging ligand in the 12-MCMnIII(N)shi-4 compounds strongly affects the out-of-phase magnetic properties. The SMM behavior appears to correlate with the pKa of the bridging ligand.
Intraday 1 minute sample data for STOXX Europe 50 Index DTX timestamped in Chicago time
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In 2023, the Digital Therapeutics (DTx) Market reached a value of USD 4.87 billion, and it is projected to surge to USD 25.98 billion by 2030.
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Get key insights from Market Research Intellect's report_name, valued at current_value in 2024, and forecast to grow to forecast_value by 2033, with a CAGR of cagr_value (2026-2033).
Download Historical STOXX Europe 50 Index Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
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The report on North America Digital Therapeutic (DTx) covers a summarized study of several factors supporting market growth, such as market size, market type, major regions, and end-user applications. The report enables customers to recognize key drivers that influence and govern the market.
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The global Digital Therapeutic (DTx) market size is projected to grow from USD 4.5 billion in 2023 to USD 15.8 billion by 2032, reflecting a robust CAGR of 14.9%. This significant growth can be attributed to several factors, including the rising prevalence of chronic diseases, increased adoption of digital health technologies, and a shift towards personalized medicine.
One of the primary growth factors for the DTx market is the increasing prevalence of chronic diseases such as diabetes, cardiovascular diseases, and mental health disorders. Chronic diseases require continuous monitoring and management, which digital therapeutics can offer through their innovative approaches. The rise in lifestyle-related disorders has further driven the demand for efficient and accessible treatment options offered by DTx solutions. The ability to provide real-time data and feedback to patients and healthcare providers ensures that health conditions are managed more effectively, reducing the burden on traditional healthcare systems.
Another significant driver is the advancements in technology, particularly in the realms of artificial intelligence (AI) and machine learning (ML). These technologies have enabled the creation of more sophisticated and user-friendly digital therapeutic solutions. AI and ML algorithms can analyze vast amounts of data to provide personalized treatment plans, predict health outcomes, and even detect early signs of complications. The integration of wearables and mobile health apps has also contributed to the enhanced efficacy of DTx, making it easier for patients to adhere to treatment regimens and for providers to monitor patient progress remotely.
The growing support from regulatory bodies and favorable government policies have also played a crucial role in the expansion of the DTx market. Regulatory agencies like the U.S. Food and Drug Administration (FDA) have begun to recognize and approve digital therapeutic solutions, which has bolstered confidence among healthcare providers and patients. Government initiatives aimed at promoting digital health and telemedicine, especially in the wake of the COVID-19 pandemic, have further accelerated the adoption of DTx. The increasing investment from both public and private sectors into digital health startups and research is another testament to the market's promising future.
Digital Psychotherapeutics are emerging as a transformative force within the broader digital therapeutic landscape, particularly in the realm of mental health. These innovative solutions leverage technology to deliver therapeutic interventions for mental health conditions, providing patients with access to evidence-based treatments through digital platforms. The integration of cognitive behavioral therapy (CBT) and other therapeutic modalities into digital formats allows for greater accessibility and convenience, enabling individuals to engage with therapy at their own pace and in their own environment. This approach not only reduces the stigma associated with seeking mental health treatment but also addresses barriers such as geographical limitations and scheduling conflicts. As the demand for mental health services continues to rise, Digital Psychotherapeutics offer a scalable and effective solution to meet the growing needs of patients and healthcare providers alike.
Regionally, North America holds a significant share in the DTx market, driven by a high prevalence of chronic diseases, advanced healthcare infrastructure, and strong support from regulatory bodies. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to its large population base, increasing digital literacy, and rising healthcare expenditure. Europe also presents substantial growth opportunities, with countries like Germany, the UK, and France being early adopters of digital health technologies.
The Digital Therapeutic (DTx) market is segmented by product type into software-based DTx and device-based DTx. Software-based DTx solutions are primarily delivered through mobile applications and web-based platforms. These solutions are designed to manage, prevent, or treat medical conditions by providing tailored therapeutic interventions. The software-based segment is anticipated to witness significant growth due to its ease of use, accessibility, and cost-effectiveness. Patients can access these therapies on their smartphones or compute
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
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The Digital Therapeutics (DTx) market is experiencing robust growth, driven by increasing prevalence of chronic diseases, rising healthcare costs, and the growing adoption of telehealth solutions. The market's expansion is fueled by technological advancements in mobile health, artificial intelligence, and wearable sensors, enabling personalized and effective interventions for various conditions. While precise market sizing data wasn't provided, considering the presence of numerous established and emerging players like Proteus Digital Health, Omada Health, and Livongo Health, coupled with a global push towards digital healthcare solutions, a conservative estimate places the 2025 market size at approximately $5 billion USD. Assuming a CAGR (Compound Annual Growth Rate) of 20% (a reasonable figure given market dynamics), the market is projected to reach approximately $15 billion USD by 2033. This growth trajectory is further supported by the ongoing development of innovative DTx solutions targeting mental health, diabetes management, and cardiac rehabilitation, among other areas. Several key factors are shaping the DTx market's future. The increasing integration of DTx with traditional healthcare systems is streamlining patient care and improving treatment outcomes. Regulatory clarity and reimbursement policies are also vital in promoting wider adoption. However, challenges remain, including concerns about data privacy and security, the need for robust clinical evidence demonstrating efficacy, and ensuring equitable access across diverse populations. The market segmentation is diverse, encompassing various therapeutic areas and delivery models. Competition is intensifying, with both established companies and startups vying for market share. Companies are increasingly focusing on developing comprehensive platforms offering integrated solutions, encompassing remote monitoring, patient engagement, and data analytics to enhance overall healthcare efficacy and patient experience.
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The Digital Therapeutic (DTx) market is rapidly emerging as a transformative sector within the healthcare industry, characterized by software-driven interventions designed to prevent, manage, or treat medical conditions. Unlike traditional treatment modalities, DTx solutions leverage technology to deliver evidence-b
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
DYX is a dataset for classification tasks - it contains CLASS annotations for 277 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).