cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29573454.v1
title: Real-world outcomes of selective internal radiation therapy with <sup>90</sup>Y resin microspheres in HCC/mCRC: RESIN registry Asia

description: Asia-Pacific guidelines recommend selective internal radiation therapy (SIRT) as an alternative to transarterial chemoembolization for patients with intermediate or advanced hepatocellular carcinoma (HCC) and, in more limited circumstances, for metastatic colorectal cancer (mCRC). This multicenter, single-arm, prospective, observational registry evaluated safety and efficacy of SIRT with <sup>90</sup>Y resin microspheres for HCC or mCRC in Asia. Adults (<i>n</i> = 202) with HCC or mCRC eligible for, and scheduled to receive, SIRT with <sup>90</sup>Y resin microspheres at participating centers were recruited. Dosimetry methods used were body surface area or partition model at the physician’s discretion. The primary endpoints were overall response rate (ORR), frequency of treatment-emergent adverse events (TEAEs) and serious adverse events (SAEs), and liver function baseline changes. ORR at 3 or 6 months was 56% (HCC) and 33% (mCRC). 491 TEAEs were reported in 125 patients and 71 SAEs were reported in 29 patients. Performance status was maintained or improved in most patients 6 months after SIRT. SIRT may offer clinicians a targeted, single-use approach for HCC or mCRC palliative care or for tumor down-sizing or down-staging in Asia. Controlled trials of SIRT using personalized dosimetry could further define a role for SIRT in these populations. Selective internal radiation therapy (SIRT) is a treatment for patients with liver cancer – including primary tumors (those that start in the liver) and metastatic tumors (when the cancer has spread to the liver from elsewhere in the body). This study involved 202 adult patients from Singapore, Taiwan and Thailand who were able to receive SIRT. The main goals were to measure the benefits of SIRT and to check for side effects. These effects were studied in everyday medical practice. The results showed that 56% of patients with primary liver cancer and 33% of patients with metastatic tumors had a response to SIRT within 6 months of treatment. Patients with primary liver cancer survived for a median of 23.4 months. Patients with metastatic tumors survived for a median of 13.6 months. These results are similar or better to earlier studies of SIRT. About 1 in 10 patients went on to have surgery to remove their tumors or had liver transplantation. In most patients, the ability to function normally and care for themselves stayed the same or improved after SIRT. As expected with this type of treatment, side effects were common, and 29 patients had serious side effects. Side effects were similar to those seen previously with SIRT. This study suggests that SIRT is a useful addition to liver cancer treatments in Asia. This observational registry of selective internal radiation therapy (SIRT) with <sup>90</sup>Y resin microspheres in Asia showed an overall response rate of 56% and 33% in patients with HCC and mCRC, respectively. SIRT may offer another targeted therapy for HCC and mCRC management in Asia.
DOI: 10.6084/m9.figshare.29382367
title: Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients

description: This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) derived from ultrasound images to improve predictive accuracy for lymph node metastasis (LNM) in pancreatic cancer (PC) patients. A retrospective analysis of 249 histopathologically confirmed PC cases, including 78 with LNM, was conducted, with an 8:2 division into training and testing cohorts. Eight transfer learning models and a baseline logistic regression model incorporating handcrafted radiomic and clinicopathological features were developed to evaluate predictive performance. Diagnostic effectiveness was assessed for junior and senior ultrasound physicians, both with and without DLRN assistance. InceptionV3 showed the highest performance among DL models (AUC = 0.844), while the DLRN model, integrating deep learning and radiomic features, demonstrated superior accuracy (AUC = 0.909), robust calibration, and significant clinical utility per decision curve analysis. DLRN assistance notably enhanced diagnostic performance, with AUC improvements of 0.238 (<i>p</i> = 0.006) for junior and 0.152 (<i>p</i> = 0.085) for senior physicians. The ultrasound-based DLRN model exhibits strong predictive capability for LNM in PC, offering a valuable decision-support tool that bolsters diagnostic accuracy, especially among less experienced clinicians, thereby supporting more tailored therapeutic strategies for PC patients. Pancreatic cancer is a serious illness, and one of the most important factors in determining a patient’s outlook is whether the cancer has spread to nearby lymph nodes. Lymph nodes are small, bean-shaped structures that are part of the immune system and can act as pathways for cancer to spread throughout the body. However, it is often difficult to accurately detect this spread before surgery.In this study, we aimed to improve how doctors predict lymph node involvement by using detailed information from ultrasound images. We analyzed data from 249 patients who had been diagnosed with pancreatic cancer. By integrating measurable features extracted from medical images with relevant clinical data, we developed a predictive model known as a radiomics nomogram – an individualized risk assessment tool that estimates the likelihood of disease spread with improved accuracy.We found that this tool provided highly accurate predictions. It also helped doctors make better diagnostic decisions, especially those with less experience. When using the tool, both junior and senior ultrasound doctors were more likely to correctly identify patients with lymph node involvement.This approach has the potential to support doctors in making more informed treatment plans for pancreatic cancer patients and may help guide decisions before surgery. A PDF version of this infographic is available as supplemental material.
DOI: 10.6084/m9.figshare.29382367.v1
title: Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients

description: This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) derived from ultrasound images to improve predictive accuracy for lymph node metastasis (LNM) in pancreatic cancer (PC) patients. A retrospective analysis of 249 histopathologically confirmed PC cases, including 78 with LNM, was conducted, with an 8:2 division into training and testing cohorts. Eight transfer learning models and a baseline logistic regression model incorporating handcrafted radiomic and clinicopathological features were developed to evaluate predictive performance. Diagnostic effectiveness was assessed for junior and senior ultrasound physicians, both with and without DLRN assistance. InceptionV3 showed the highest performance among DL models (AUC = 0.844), while the DLRN model, integrating deep learning and radiomic features, demonstrated superior accuracy (AUC = 0.909), robust calibration, and significant clinical utility per decision curve analysis. DLRN assistance notably enhanced diagnostic performance, with AUC improvements of 0.238 (<i>p</i> = 0.006) for junior and 0.152 (<i>p</i> = 0.085) for senior physicians. The ultrasound-based DLRN model exhibits strong predictive capability for LNM in PC, offering a valuable decision-support tool that bolsters diagnostic accuracy, especially among less experienced clinicians, thereby supporting more tailored therapeutic strategies for PC patients. Pancreatic cancer is a serious illness, and one of the most important factors in determining a patient’s outlook is whether the cancer has spread to nearby lymph nodes. Lymph nodes are small, bean-shaped structures that are part of the immune system and can act as pathways for cancer to spread throughout the body. However, it is often difficult to accurately detect this spread before surgery.In this study, we aimed to improve how doctors predict lymph node involvement by using detailed information from ultrasound images. We analyzed data from 249 patients who had been diagnosed with pancreatic cancer. By integrating measurable features extracted from medical images with relevant clinical data, we developed a predictive model known as a radiomics nomogram – an individualized risk assessment tool that estimates the likelihood of disease spread with improved accuracy.We found that this tool provided highly accurate predictions. It also helped doctors make better diagnostic decisions, especially those with less experience. When using the tool, both junior and senior ultrasound doctors were more likely to correctly identify patients with lymph node involvement.This approach has the potential to support doctors in making more informed treatment plans for pancreatic cancer patients and may help guide decisions before surgery. A PDF version of this infographic is available as supplemental material.
DOI: 10.6084/m9.figshare.29380424
title: The economic and humanistic burden of tenosynovial giant cell tumor: a targeted literature review

description: To understand the epidemiology, treatment patterns, economic burden, and humanistic burden of tenosynovial giant cell tumor (TGCT) in the contemporary treatment landscape. Embase, MEDLINE, and select conferences were searched to identify studies published between 1 January 2013, and 22/23 August 2023 (database dependent) that included ≥ 20 TGCT patients (≥ 40 for humanistic burden studies). For economic outcomes and treatment patterns, data from 1 January 2008, and later were included. A total of 35 studies were included. The incidence of TGCT was &lt; 50 cases/million person-years. Surgery was the most common treatment, for which recurrence rates were up to 67%. While quality of life improved after surgery in the absence of recurrence, repeat surgeries for persistent symptoms or recurrent tumors negatively impacted quality of life and inflicted a high economic burden. Systemic therapies were effective and overall improved patient symptoms and quality of life. Treatment was multidisciplinary and involved orthopedic surgeons and medical oncologists as well as supplemental care such as physical therapy. TGCT has a high burden, which can be exacerbated by repeat surgeries. Systemic therapies may improve disease and quality of life outcomes in some patients with tumors that are locally advanced or in complicated anatomical locations. Tenosynovial giant cell tumor (TGCT) is a rare disease that affects the joints and surrounding tissues. We reviewed published studies to understand how common TGCT is, current treatments, healthcare-related services and costs, and its impact on patients. Studies confirmed that TGCT is rare and that it impacts a relatively young population. Patients with TGCT commonly experience pain and stiffness and have less mobility and worse overall quality of life compared with the general population. Most patients with symptoms are treated with surgery. However, TGCT can come back after surgery in up to 2 out of 3 patients, potentially requiring them to undergo additional surgeries, which may be expensive and worsen symptoms. Alternatively, medications can be used for treatment and improve symptoms and quality of life for many patients. However, medications are not currently widely available and some are associated with negative side effects. More studies on existing medications and new medications that have fewer side effects are needed. In addition to surgery and medications, many patients receive physical therapy. Treatment decisions should consider the risks and benefits of all options on an individual basis.
DOI: 10.6084/m9.figshare.29380424.v1
title: The economic and humanistic burden of tenosynovial giant cell tumor: a targeted literature review

description: To understand the epidemiology, treatment patterns, economic burden, and humanistic burden of tenosynovial giant cell tumor (TGCT) in the contemporary treatment landscape. Embase, MEDLINE, and select conferences were searched to identify studies published between 1 January 2013, and 22/23 August 2023 (database dependent) that included ≥ 20 TGCT patients (≥ 40 for humanistic burden studies). For economic outcomes and treatment patterns, data from 1 January 2008, and later were included. A total of 35 studies were included. The incidence of TGCT was &lt; 50 cases/million person-years. Surgery was the most common treatment, for which recurrence rates were up to 67%. While quality of life improved after surgery in the absence of recurrence, repeat surgeries for persistent symptoms or recurrent tumors negatively impacted quality of life and inflicted a high economic burden. Systemic therapies were effective and overall improved patient symptoms and quality of life. Treatment was multidisciplinary and involved orthopedic surgeons and medical oncologists as well as supplemental care such as physical therapy. TGCT has a high burden, which can be exacerbated by repeat surgeries. Systemic therapies may improve disease and quality of life outcomes in some patients with tumors that are locally advanced or in complicated anatomical locations. Tenosynovial giant cell tumor (TGCT) is a rare disease that affects the joints and surrounding tissues. We reviewed published studies to understand how common TGCT is, current treatments, healthcare-related services and costs, and its impact on patients. Studies confirmed that TGCT is rare and that it impacts a relatively young population. Patients with TGCT commonly experience pain and stiffness and have less mobility and worse overall quality of life compared with the general population. Most patients with symptoms are treated with surgery. However, TGCT can come back after surgery in up to 2 out of 3 patients, potentially requiring them to undergo additional surgeries, which may be expensive and worsen symptoms. Alternatively, medications can be used for treatment and improve symptoms and quality of life for many patients. However, medications are not currently widely available and some are associated with negative side effects. More studies on existing medications and new medications that have fewer side effects are needed. In addition to surgery and medications, many patients receive physical therapy. Treatment decisions should consider the risks and benefits of all options on an individual basis.
DOI: 10.6084/m9.figshare.29828924
title: Optimal Transport based Cross-Domain Integration for Heterogeneous Data

description: Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subjects, even when rich, time-dependent measurements are available for each individual. These limited sample sizes further diminish the power to detect common dynamic patterns across subjects. In this paper, we propose a novel heterogeneous data integration framework based on optimal transport to extract shared patterns in the conditional mean dynamics of target responses. The key advantage of the proposed method is its ability to enhance discriminative power by reducing heterogeneity unrelated to the signal. This is achieved through the alignment of extracted domain-shared temporal information across multiple datasets from different domains. Our approach is effective regardless of the number of datasets and does not require auxiliary matching information for alignment. Specifically, the method aligns longitudinal data from heterogeneous datasets within a common latent space, capturing shared dynamic patterns while leveraging temporal dependencies within subjects. Theoretically, we establish generalization error bounds for the proposed data integration approach in supervised learning tasks, highlighting a novel trade-off between data alignment and pattern learning. Additionally, we derive convergence rates for the barycentric projection under Gromov-Wasserstein and fused Gromov-Wasserstein distances. Numerical studies on both simulated data and neuroscience applications demonstrate that the proposed data integration framework substantially improves prediction accuracy by effectively aggregating information across diverse data sources and subjects.
DOI: 10.6084/m9.figshare.29828924.v1
title: Optimal Transport based Cross-Domain Integration for Heterogeneous Data

description: Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subjects, even when rich, time-dependent measurements are available for each individual. These limited sample sizes further diminish the power to detect common dynamic patterns across subjects. In this paper, we propose a novel heterogeneous data integration framework based on optimal transport to extract shared patterns in the conditional mean dynamics of target responses. The key advantage of the proposed method is its ability to enhance discriminative power by reducing heterogeneity unrelated to the signal. This is achieved through the alignment of extracted domain-shared temporal information across multiple datasets from different domains. Our approach is effective regardless of the number of datasets and does not require auxiliary matching information for alignment. Specifically, the method aligns longitudinal data from heterogeneous datasets within a common latent space, capturing shared dynamic patterns while leveraging temporal dependencies within subjects. Theoretically, we establish generalization error bounds for the proposed data integration approach in supervised learning tasks, highlighting a novel trade-off between data alignment and pattern learning. Additionally, we derive convergence rates for the barycentric projection under Gromov-Wasserstein and fused Gromov-Wasserstein distances. Numerical studies on both simulated data and neuroscience applications demonstrate that the proposed data integration framework substantially improves prediction accuracy by effectively aggregating information across diverse data sources and subjects.
DOI: 10.6084/m9.figshare.29828729
title: Table S1-S7.xlsx

description: additional document
DOI: 10.6084/m9.figshare.29828729.v1
title: Table S1-S7.xlsx

description: additional document
DOI: 10.15468/dl.ubgzey
title: Occurrence Download

description: A dataset containing 107383 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cathorops mapale)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 107383 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015179-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/9o6nk3
title: 2012-EGG-SK-0601-Wawrek.ogg

description: Enregistrement audio en version originale slovaque
DOI: 10.57745/kyxpye
title: Wawrek 1-1.avi

description: Enregistrement vidéo,
Entretien en slovaque
DOI: 10.57745/xtytap
title: Entretien avec Emil Wawrek. Histoire de vie et expériences de déportation en URSS

description: Cet entretien a eu lieu le 10 janvier 2012 à Bušovce, Slovaquie, au domicile de l'interviewé. Il a été mené par Lubomira Valcheva, avec la participation de Alain Blum qui s'occupait de l'enregistrement. L'entretien a été réalisé en slovaque. Il a duré 32 minutes.
DOI: 10.15468/dl.c38wt4
title: Occurrence Download

description: A dataset containing 50616 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myrmotherula axillaris)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 50616 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015167-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q8u4qd
title: Occurrence Download

description: A dataset containing 346 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gyalidea asteriscus"
 ]
}

 The dataset includes 346 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015194-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e115407v1
title: National Neighborhood Data Archive (NaNDA): Religious Organizations by Census Tract, United States, 2006-2015

description: This project is superseded by:<br><br>Melendez, Robert, Finlay, Jessica, Clarke, Philippa, Noppert, Grace, and Gypin, Lindsay. National Neighborhood Data Archive (NaNDA): Civic, Social, and Religious Organizations by Census Tract and ZCTA, United States, 1990-2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-07-24. https://doi.org/10.3886/E207966V1<br>
description: Religious organizations in all
census tracts in the United States, excluding US island territories. Smallest Geographic Unit: census tract