cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24634119
title: Supplementary Material for: Diagnostic immunocytostaining with Peroxisome Proliferator-Activated Receptor-Gamma in urine cytology samples

description: Introduction: Urine cytology is a common method for detection of urothelial carcinoma (UC), however, is not high sensitivity. Improvement of the accuracy of cytodiagnosis using immunocytostaining as an auxiliary method is needed. This study aimed to determine the cyto-diagnostic usefulness of peroxisome proliferator-activated receptor-gamma (PPAR-γ) immunocytostaining in urine cytology for the detection of urothelial carcinomas, particularly low-grade urothelial carcinomas (LGUC). Methods: PPAR-γ immunocytostaining was performed for 37 urothelial carcinoma (UC) cases and 26 benign cases. Among the UC cases, 22 cases were of the papillary proliferation type, not including the mixed type comprising both papillary and flat growth. Fifteen LGUC cases of all papillary proliferation types were included. For comparison, the same samples were also immunocytostained for p53 and Ki-67. Results: Of the UC cases, 25 of 37 were positive for PPAR-γ, while 24 of the 26 benign cases were PPAR-γ-negative. Regardless of histological grading, 13 of the 22 UC cases with papillary proliferation were PPAR-γ-positive. In particular, PPAR-γ immunocytostaining showed higher sensitivity for LGUC cases than that of the other biomarkers. Regarding LGUC specifically, 4 of 10 cases not identified by primary cytology were detected by PPAR-γ immunocytostaining. Conclusion: PPAR-γ immunocytostaining enhances the accuracy of urine cytodiagnosis. Furthermore, PPAR-γ is a more useful immunobiomarker in urine cytology than p53 and Ki-67, the commonly used immunobiomarkers for malignant cell detection.
DOI: 10.6084/m9.figshare.24634119.v1
title: Supplementary Material for: Diagnostic immunocytostaining with Peroxisome Proliferator-Activated Receptor-Gamma in urine cytology samples

description: Introduction: Urine cytology is a common method for detection of urothelial carcinoma (UC), however, is not high sensitivity. Improvement of the accuracy of cytodiagnosis using immunocytostaining as an auxiliary method is needed. This study aimed to determine the cyto-diagnostic usefulness of peroxisome proliferator-activated receptor-gamma (PPAR-γ) immunocytostaining in urine cytology for the detection of urothelial carcinomas, particularly low-grade urothelial carcinomas (LGUC). Methods: PPAR-γ immunocytostaining was performed for 37 urothelial carcinoma (UC) cases and 26 benign cases. Among the UC cases, 22 cases were of the papillary proliferation type, not including the mixed type comprising both papillary and flat growth. Fifteen LGUC cases of all papillary proliferation types were included. For comparison, the same samples were also immunocytostained for p53 and Ki-67. Results: Of the UC cases, 25 of 37 were positive for PPAR-γ, while 24 of the 26 benign cases were PPAR-γ-negative. Regardless of histological grading, 13 of the 22 UC cases with papillary proliferation were PPAR-γ-positive. In particular, PPAR-γ immunocytostaining showed higher sensitivity for LGUC cases than that of the other biomarkers. Regarding LGUC specifically, 4 of 10 cases not identified by primary cytology were detected by PPAR-γ immunocytostaining. Conclusion: PPAR-γ immunocytostaining enhances the accuracy of urine cytodiagnosis. Furthermore, PPAR-γ is a more useful immunobiomarker in urine cytology than p53 and Ki-67, the commonly used immunobiomarkers for malignant cell detection.
DOI: 10.5281/zenodo.10206309
title: IMPORTANCE OF DEVELOPING LEARNERS' AUTONOMY  IN HIGHER EDUCATION INSTITUTIONS IN UZBEKISTAN
DOI: 10.5281/zenodo.10206308
title: IMPORTANCE OF DEVELOPING LEARNERS' AUTONOMY  IN HIGHER EDUCATION INSTITUTIONS IN UZBEKISTAN
DOI: 10.5281/zenodo.10206303
title: Dataset for PCPrior Project
DOI: 10.5281/zenodo.10206302
title: Dataset for PCPrior Project
DOI: 10.5281/zenodo.10206315
title: MEASURES TO PREVENT THE NEGATIVE EFFECTS OF NOISE
DOI: 10.5281/zenodo.10206314
title: MEASURES TO PREVENT THE NEGATIVE EFFECTS OF NOISE
DOI: 10.5281/zenodo.10206322
title: BASIC AND ADDITIONAL METHODS OF EXAMINATION OF DENTAL PATIENTS
DOI: 10.5281/zenodo.10206321
title: BASIC AND ADDITIONAL METHODS OF EXAMINATION OF DENTAL PATIENTS
DOI: 10.5281/zenodo.10206347
title: GIDROSFERAGA ANTROPOGEN TA'SIR VA UNI HIMOYA QILISH
DOI: 10.5281/zenodo.10206346
title: GIDROSFERAGA ANTROPOGEN TA'SIR VA UNI HIMOYA QILISH
DOI: 10.5281/zenodo.10206356
title: YUKLARNI TASHISHGA BO'LGAN XAVFSIZLIK TALABLARI
DOI: 10.5281/zenodo.10206357
title: YUKLARNI TASHISHGA BO'LGAN XAVFSIZLIK TALABLARI
DOI: 10.5281/zenodo.10206392
title: INNOVATIVE ENERGY SAVING AS A FORM OF INCREASING THE COMPETITIVENESS OF ENTERPRISES
DOI: 10.5281/zenodo.10206393
title: INNOVATIVE ENERGY SAVING AS A FORM OF INCREASING THE COMPETITIVENESS OF ENTERPRISES
DOI: 10.6084/m9.figshare.24634116
title: 230690144_0_高职专创融合的调查问卷（学生版）_data.703.xlsx

description: Research activities for vocational college students' professional innovation integration.
DOI: 10.6084/m9.figshare.24634116.v1
title: 230690144_0_高职专创融合的调查问卷（学生版）_data.703.xlsx

description: Research activities for vocational college students' professional innovation integration.
DOI: 10.5281/zenodo.10103595
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 1)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10103596
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 1)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10118640
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 2)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10118641
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 2)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.6084/m9.figshare.24634131
title: Supplementary Material for: Individualized homeopathic medicines in the treatment of post-COVID-19 fatigue in adults: A single-blind, randomized, placebo-controlled trial

description: Introduction: The coronavirus disease (COVID-19) is leading to unknown and unusual health conditions that are challenging to manage. Post-COVID-19 fatigue is one of those challenges, becoming increasingly common as the pandemic evolves, as it impairs the quality of life of an individual. This trial attempts to identify the preliminary evidence of the efficacy of individualized homeopathic medicines (IHMs) against placebos in the treatment of post-COVID-19 fatigue in adults. Methods: A 3-month, single-blind, randomized, placebo-controlled, parallel-arm trial was conducted at the outpatient department of The Calcutta Homoeopathic Medical College and Hospital, India. Sixty participants were randomized in a 1:1 ratio to receive either IHMs (n = 30) or identical-looking placebos (n = 30). The primary and secondary outcome measures were the Fatigue Assessment Scale (FAS) and Outcome in Relation to Impact on Daily Living (ORIDL), respectively, measured every month for up to 3 months. Comparative analysis was carried out on the intention-to-treat sample to detect group differences. Results: Group differences in both the primary (FAS total: F1, 58 = 14.356, P &lt; 0.001) and secondary outcomes (ORIDL: F1, 58 = 210.986, P &lt; 0.001) after 3 months favored IHMs against placebos. Lycopodium clavatum (11.7%), Sulphur (11.7%), Arsenicum album (10%), and Thuja occidentalis (10%) were the most frequently indicated medicines. No harm, unintended effects, homeopathic aggravations, or any serious adverse events were reported from either of the groups. Conclusion: IHMs produced significantly better effects than placebos in the treatment of post-COVID-19 fatigue in adults. Definitive, robust trials may be undertaken to confirm the findings. Trial registration: CTRI/2022/03/041316
DOI: 10.6084/m9.figshare.24634131.v1
title: Supplementary Material for: Individualized homeopathic medicines in the treatment of post-COVID-19 fatigue in adults: A single-blind, randomized, placebo-controlled trial

description: Introduction: The coronavirus disease (COVID-19) is leading to unknown and unusual health conditions that are challenging to manage. Post-COVID-19 fatigue is one of those challenges, becoming increasingly common as the pandemic evolves, as it impairs the quality of life of an individual. This trial attempts to identify the preliminary evidence of the efficacy of individualized homeopathic medicines (IHMs) against placebos in the treatment of post-COVID-19 fatigue in adults. Methods: A 3-month, single-blind, randomized, placebo-controlled, parallel-arm trial was conducted at the outpatient department of The Calcutta Homoeopathic Medical College and Hospital, India. Sixty participants were randomized in a 1:1 ratio to receive either IHMs (n = 30) or identical-looking placebos (n = 30). The primary and secondary outcome measures were the Fatigue Assessment Scale (FAS) and Outcome in Relation to Impact on Daily Living (ORIDL), respectively, measured every month for up to 3 months. Comparative analysis was carried out on the intention-to-treat sample to detect group differences. Results: Group differences in both the primary (FAS total: F1, 58 = 14.356, P &lt; 0.001) and secondary outcomes (ORIDL: F1, 58 = 210.986, P &lt; 0.001) after 3 months favored IHMs against placebos. Lycopodium clavatum (11.7%), Sulphur (11.7%), Arsenicum album (10%), and Thuja occidentalis (10%) were the most frequently indicated medicines. No harm, unintended effects, homeopathic aggravations, or any serious adverse events were reported from either of the groups. Conclusion: IHMs produced significantly better effects than placebos in the treatment of post-COVID-19 fatigue in adults. Definitive, robust trials may be undertaken to confirm the findings. Trial registration: CTRI/2022/03/041316
DOI: 10.5281/zenodo.10206329
title: 16S rRNA Gene Sequencing Analysis of the Gut Microbiota of ten Mulberry Silkworms Fed with Mulberry Leaves (SY), ten Artificial Diet (SL), and tem Reverted from Artificial Diet to Mulberry Leaves (ZS)
DOI: 10.5281/zenodo.10206330
title: 16S rRNA Gene Sequencing Analysis of the Gut Microbiota of ten Mulberry Silkworms Fed with Mulberry Leaves (SY), ten Artificial Diet (SL), and tem Reverted from Artificial Diet to Mulberry Leaves (ZS)
DOI: 10.6084/m9.figshare.24634134
title: Supplementary Material for: Individualized homeopathic medicines in the treatment of post-COVID-19 fatigue in adults: A single-blind, randomized, placebo-controlled trial

description: Introduction: The coronavirus disease (COVID-19) is leading to unknown and unusual health conditions that are challenging to manage. Post-COVID-19 fatigue is one of those challenges, becoming increasingly common as the pandemic evolves, as it impairs the quality of life of an individual. This trial attempts to identify the preliminary evidence of the efficacy of individualized homeopathic medicines (IHMs) against placebos in the treatment of post-COVID-19 fatigue in adults. Methods: A 3-month, single-blind, randomized, placebo-controlled, parallel-arm trial was conducted at the outpatient department of The Calcutta Homoeopathic Medical College and Hospital, India. Sixty participants were randomized in a 1:1 ratio to receive either IHMs (n = 30) or identical-looking placebos (n = 30). The primary and secondary outcome measures were the Fatigue Assessment Scale (FAS) and Outcome in Relation to Impact on Daily Living (ORIDL), respectively, measured every month for up to 3 months. Comparative analysis was carried out on the intention-to-treat sample to detect group differences. Results: Group differences in both the primary (FAS total: F1, 58 = 14.356, P &lt; 0.001) and secondary outcomes (ORIDL: F1, 58 = 210.986, P &lt; 0.001) after 3 months favored IHMs against placebos. Lycopodium clavatum (11.7%), Sulphur (11.7%), Arsenicum album (10%), and Thuja occidentalis (10%) were the most frequently indicated medicines. No harm, unintended effects, homeopathic aggravations, or any serious adverse events were reported from either of the groups. Conclusion: IHMs produced significantly better effects than placebos in the treatment of post-COVID-19 fatigue in adults. Definitive, robust trials may be undertaken to confirm the findings. Trial registration: CTRI/2022/03/041316
DOI: 10.6084/m9.figshare.24634134.v1
title: Supplementary Material for: Individualized homeopathic medicines in the treatment of post-COVID-19 fatigue in adults: A single-blind, randomized, placebo-controlled trial

description: Introduction: The coronavirus disease (COVID-19) is leading to unknown and unusual health conditions that are challenging to manage. Post-COVID-19 fatigue is one of those challenges, becoming increasingly common as the pandemic evolves, as it impairs the quality of life of an individual. This trial attempts to identify the preliminary evidence of the efficacy of individualized homeopathic medicines (IHMs) against placebos in the treatment of post-COVID-19 fatigue in adults. Methods: A 3-month, single-blind, randomized, placebo-controlled, parallel-arm trial was conducted at the outpatient department of The Calcutta Homoeopathic Medical College and Hospital, India. Sixty participants were randomized in a 1:1 ratio to receive either IHMs (n = 30) or identical-looking placebos (n = 30). The primary and secondary outcome measures were the Fatigue Assessment Scale (FAS) and Outcome in Relation to Impact on Daily Living (ORIDL), respectively, measured every month for up to 3 months. Comparative analysis was carried out on the intention-to-treat sample to detect group differences. Results: Group differences in both the primary (FAS total: F1, 58 = 14.356, P &lt; 0.001) and secondary outcomes (ORIDL: F1, 58 = 210.986, P &lt; 0.001) after 3 months favored IHMs against placebos. Lycopodium clavatum (11.7%), Sulphur (11.7%), Arsenicum album (10%), and Thuja occidentalis (10%) were the most frequently indicated medicines. No harm, unintended effects, homeopathic aggravations, or any serious adverse events were reported from either of the groups. Conclusion: IHMs produced significantly better effects than placebos in the treatment of post-COVID-19 fatigue in adults. Definitive, robust trials may be undertaken to confirm the findings. Trial registration: CTRI/2022/03/041316
DOI: 10.5281/zenodo.10119045
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 3)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10119044
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 3)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.34740/kaggle/dsv/7051801
title: Gladiator Combat Records and Profiles Dataset
DOI: 10.5281/zenodo.10206468
title: METHODS OF OBTAINING BOTH CEMENT AND ELEMENTARY PHOSPHORUS FROM PHOSPHORITES
DOI: 10.5281/zenodo.10206469
title: METHODS OF OBTAINING BOTH CEMENT AND ELEMENTARY PHOSPHORUS FROM PHOSPHORITES
DOI: 10.25345/c5s46hh45
title: MassIVE MSV000093493 - Chapter IV Thesis CHIKV Antiviral in vitro
title: RP LC MS QTOF of vero cells infected with a Colombian isolated strain of CHIKV and treated with a novel antiviral
DOI: 10.25919/32a1-pr20
title: Parkes observations for project P1101 semester 2023OCTS_02

description: We propose to continue our fortnightly monitoring of a very active repeater, FRB~190520, with the Parkes UWL system. Our current observations show that FRB~190520 is not only the most active FRB that can be observed with Parkes, but also shows extreme rotation measure (RM) variations and unique behavior at 2 to 3 GHz frequency. This makes UWL the ideal instrument to carry out in-depth studies of this repeating FRB. With this program, we expect to detect a large sample of bursts from FRB~190520, and in collaboration with the FAST telescope, we will study the local environment, emission properties, fluence distributions, and polarisation properties over an extremely wide bandwidth with high sensitivity.