cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-01.2709642/2305085
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-02.2732641/2305086
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-01.2707324/2305087
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2710333/2305088
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2709891/2305089
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2710372/2305090
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2710908/2305091
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2709904/2305092
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2709879/2305093
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2710960/2305094
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2711409/2305095
title: Data for EMSL Project 51287 from January 2023
DOI: 10.6084/m9.figshare.7075100
title: Incorporating Biomarkers to Improve Statistical Power of Immunotherapeutic Neoadjuvant Clinical Trials in Patients with Triple-Negative Breast Cancer

description: Neoadjuvant (preoperative) approach to breast cancer treatment has become widely accepted. Traditionally, the primary objective of neoadjuvant treatment is to improve subsequent surgical intervention and the effectiveness is often evaluated by its ability to achieve complete pathological response (pCR) and the eradication of the malignant disease in the breast and axillary lymph nodes. More recently, neoadjuvant treatment has also become recognized as an in vivo, preoperative” window of opportunity” to explore the efficacy of novel agents such as immunotherapies where several tumor biomarkers are also routinely collected to quantify antitumor immunity. However, one challenge to combine the traditional pCR and efficacy biomarkers is that these tumor biomarkers are only partially available and cannot be measured in patients who have achieved pCR. In this article, a stepwise hypothesis testing procedure is proposed to combine a continuous tumor biomarker with the conventional binary endpoint in a two-arm randomized phase II superiority trial to improve statistical power. The operating characteristics of the proposed procedure are illustrated with a real-world example and the performance is also evaluated numerically.
DOI: 10.6084/m9.figshare.7075115
title: Inferring Brain Signals Synchronicity From a Sample of EEG Readings

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents nontrivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering, and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.7075100.v1
title: Incorporating Biomarkers to Improve Statistical Power of Immunotherapeutic Neoadjuvant Clinical Trials in Patients with Triple-Negative Breast Cancer

description: Neoadjuvant (preoperative) approach to breast cancer treatment has become widely accepted. Traditionally, the primary objective of neoadjuvant treatment is to improve subsequent surgical intervention and the effectiveness is often evaluated by its ability to achieve complete pathological response (pCR) and the eradication of the malignant disease in the breast and axillary lymph nodes. More recently, neoadjuvant treatment has also become recognized as an in vivo, preoperative” window of opportunity” to explore the efficacy of novel agents such as immunotherapies where several tumor biomarkers are also routinely collected to quantify antitumor immunity. However, one challenge to combine the traditional pCR and efficacy biomarkers is that these tumor biomarkers are only partially available and cannot be measured in patients who have achieved pCR. In this article, a stepwise hypothesis testing procedure is proposed to combine a continuous tumor biomarker with the conventional binary endpoint in a two-arm randomized phase II superiority trial to improve statistical power. The operating characteristics of the proposed procedure are illustrated with a real-world example and the performance is also evaluated numerically.
DOI: 10.25582/data.2023-01.2706487/2305096
title: Data for EMSL Project 51287 from January 2023
DOI: 10.6084/m9.figshare.7075115.v1
title: <b>Inferring Brain Signals Synchronicity from a Sample of EEG Readings</b>

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.
DOI: 10.25582/data.2023-01.2711231/2305097
title: Data for EMSL Project 51287 from January 2023
DOI: 10.6084/m9.figshare.7075115.v2
title: Inferring Brain Signals Synchronicity from a Sample of EEG Readings

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.
DOI: 10.6084/m9.figshare.7075115.v3
title: Inferring Brain Signals Synchronicity From a Sample of EEG Readings

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents nontrivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering, and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.25582/data.2023-01.2710208/2305098
title: Data for EMSL Project 51287 from January 2023
DOI: 10.6084/m9.figshare.7075115.v4
title: Inferring Brain Signals Synchronicity From a Sample of EEG Readings

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents nontrivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering, and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.7075115.v6
title: Inferring Brain Signals Synchronicity From a Sample of EEG Readings

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents nontrivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering, and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.7075115.v5
title: Inferring Brain Signals Synchronicity From a Sample of EEG Readings

description: Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents nontrivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering, and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.25582/data.2023-01.2708820/2305099
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25387/g3.7075178
title: Supplemental Material for Huynh et al., 2018

description: Targeting gene function with spatial or temporal specificity is a key goal in molecular genetics. CRISPR-Cas9 has greatly facilitated this strategy, but some standard approaches are problematic. For instance, simple tissue-specific or global overexpression of Cas9 can cause significant lethality or developmental delays even in the absence of gRNAs. In particular, we found that Gal4-mediated expression of UAS-Cas9 in the <i>Drosophila</i>prothoracic gland (PG) was not a suitable strategy to disrupt gene expression, since Cas9 alone caused widespread lethality. The PG is widely used for studying endocrine gland function during animal development, but tools validating PG-specific RNAi phenotypes are lacking. Here, we present a collection of modular gateway-compatible CRISPR-Cas9 tools that allow precise modulation of target gene activity with temporal and spatial specificity. We also demonstrate that Cas9 fused to the progesterone ligand-binding domain can be used to activate gene expression via RU486. Using these approaches, we were able to avoid the lethality associated with simple GAL4-mediated overexpression of Cas9 in the PG. Given that the PG is a polytene tissue, we conclude that these tools work effectively in endoreplicating cells where Cas9 has to target multiple copies of the same locus. Our toolkit can be easily adapted for other tissues and can be used both for gain- and loss-of-function studies.
DOI: 10.25582/data.2023-01.2709617/2305100
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25387/g3.7075178.v1
title: Supplemental Material for Huynh et al., 2018

description: Targeting gene function with spatial or temporal specificity is a key goal in molecular genetics. CRISPR-Cas9 has greatly facilitated this strategy, but some standard approaches are problematic. For instance, simple tissue-specific or global overexpression of Cas9 can cause significant lethality or developmental delays even in the absence of gRNAs. In particular, we found that Gal4-mediated expression of UAS-Cas9 in the <i>Drosophila</i>prothoracic gland (PG) was not a suitable strategy to disrupt gene expression, since Cas9 alone caused widespread lethality. The PG is widely used for studying endocrine gland function during animal development, but tools validating PG-specific RNAi phenotypes are lacking. Here, we present a collection of modular gateway-compatible CRISPR-Cas9 tools that allow precise modulation of target gene activity with temporal and spatial specificity. We also demonstrate that Cas9 fused to the progesterone ligand-binding domain can be used to activate gene expression via RU486. Using these approaches, we were able to avoid the lethality associated with simple GAL4-mediated overexpression of Cas9 in the PG. Given that the PG is a polytene tissue, we conclude that these tools work effectively in endoreplicating cells where Cas9 has to target multiple copies of the same locus. Our toolkit can be easily adapted for other tissues and can be used both for gain- and loss-of-function studies.
DOI: 10.6084/m9.figshare.9696650
title: Social predictors of oral hygiene status in school children from suburban Nigeria

description: Abstract Oral hygiene, which is measured by the status of plaque-free tooth surfaces, is essential for the promotion of oral health. This study aimed to determine the social predictors of good oral hygiene for children in a suburban population in Nigeria. This was a secondary analysis of data. The study participants were 8- to 16-year-old children who were residents in Ile-Ife, which is a suburban population in Nigeria. Information on the age, gender, socioeconomic status, family structure, number of siblings and birth rank of each study participant was retrieved from the an interviewer-administered questionnaire. Oral hygiene status was determined through a simplified-oral hygiene index (OHI-S) and categorized as good, fair and poor. The association between oral hygiene status and sociodemographic variables was determined. The predictors of good oral hygiene were determined using a binomial regression analysis. Data on 2,107 individuals were retrieved, of which 44.8% had good oral hygiene and 17.1% had poor oral hygiene. The odds of having good oral hygiene were reduced for children who were 13 to 16 years old (p = 0.02) or male (P=0.002) and children with low socioeconomic status (p ≤ 0.001). The odds of having good oral hygiene increased for children who were last-born compared with those who were first-born (p = 0.02). Age, gender, socioeconomic status and birth-rank were significant social predictive factors of oral hygiene status among the study population. Based on these findings, targeted interventions can be conducted to improve the oral hygiene status of children and adolescents with these social profiles.
DOI: 10.25582/data.2023-01.2709044/2305101
title: Data for EMSL Project 51287 from January 2023
DOI: 10.6084/m9.figshare.9696650.v1
title: Social predictors of oral hygiene status in school children from suburban Nigeria

description: Abstract Oral hygiene, which is measured by the status of plaque-free tooth surfaces, is essential for the promotion of oral health. This study aimed to determine the social predictors of good oral hygiene for children in a suburban population in Nigeria. This was a secondary analysis of data. The study participants were 8- to 16-year-old children who were residents in Ile-Ife, which is a suburban population in Nigeria. Information on the age, gender, socioeconomic status, family structure, number of siblings and birth rank of each study participant was retrieved from the an interviewer-administered questionnaire. Oral hygiene status was determined through a simplified-oral hygiene index (OHI-S) and categorized as good, fair and poor. The association between oral hygiene status and sociodemographic variables was determined. The predictors of good oral hygiene were determined using a binomial regression analysis. Data on 2,107 individuals were retrieved, of which 44.8% had good oral hygiene and 17.1% had poor oral hygiene. The odds of having good oral hygiene were reduced for children who were 13 to 16 years old (p = 0.02) or male (P=0.002) and children with low socioeconomic status (p ≤ 0.001). The odds of having good oral hygiene increased for children who were last-born compared with those who were first-born (p = 0.02). Age, gender, socioeconomic status and birth-rank were significant social predictive factors of oral hygiene status among the study population. Based on these findings, targeted interventions can be conducted to improve the oral hygiene status of children and adolescents with these social profiles.
DOI: 10.25582/data.2023-01.2709624/2305102
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2708866/2305103
title: Data for EMSL Project 51287 from January 2023
DOI: 10.25582/data.2023-01.2709786/2305104
title: Data for EMSL Project 51287 from January 2023
DOI: 10.6084/m9.figshare.21755370
title: Magma contamination in Himalayan leucogranite by metapelite host rocks: insights from chemical and boron isotopic compositions of tourmaline

description: Contamination is a common scenario of intracrustal magmatic processes that may significantly change the compositions of involved anatectic melts. Here, we present major element and B isotope analyses on tourmalines from the Guowu leucogranite and their host rocks, which allow us to unravel the potential contaminations of the Himalayan leucogranites by metapelite host rocks. Three types of tourmaline have been identified in our samples. Tur-M in the host schist has the highest Mg/(Mg+Fe) ratio (0.52–0.62) and low Al content, suggesting its metamorphic character. Tourmalines in the leucogranite (Tur-L) and in the contact zone between leucogranite and schist (Tur-C) have an identical core-rim texture, and their cores are characterized by the lowest Mg/(Mg+Fe) ratio (0.13–0.18) and high Al content, which are consistent with the composition of magmatic tourmaline in the Himalayan leucogranites. The rims of Tur-L show higher Mg/(Mg + Fe) ratio (0.32–0.45) and moderate Ca, Ti, F contents, reflecting the contribution of the host schist via contamination. The composition of Tur-C rims is similar to that of Tur-M, suggesting a more significant contribution from contaminated schist than that for Tur-L. The different types of tourmaline share consistent B isotope compositions with δ<sup>11</sup>B ranging from −14 to −12‰. The compositional characteristics of the contaminated tourmaline and the mineral assemblage in the contaminated zone suggest that the involved components derived from the host schist, probably produced by the decomposition of tourmaline, biotite, plagioclase, primarily include Ca, K, Mg, Ti, B and F. In addition, contamination by tourmaline-bearing metapelite may be an alternative interpretation responsible for the peraluminous character of the Himalayan leucogranites. The occurrence of contaminated tourmaline in leucogranites suggests that contamination of host rocks is a possible way promoting the formation of tourmaline in the Himalayan leucogranites, and tourmaline is useful for deciphering the related contamination processes.
DOI: 10.6084/m9.figshare.21755370.v1
title: Magma contamination in Himalayan leucogranite by metapelite host rocks: insights from chemical and boron isotopic compositions of tourmaline

description: Contamination is a common scenario of intracrustal magmatic processes that may significantly change the compositions of involved anatectic melts. Here, we present major element and B isotope analyses on tourmalines from the Guowu leucogranite and their host rocks, which allow us to unravel the potential contaminations of the Himalayan leucogranites by metapelite host rocks. Three types of tourmaline have been identified in our samples. Tur-M in the host schist has the highest Mg/(Mg+Fe) ratio (0.52–0.62) and low Al content, suggesting its metamorphic character. Tourmalines in the leucogranite (Tur-L) and in the contact zone between leucogranite and schist (Tur-C) have an identical core-rim texture, and their cores are characterized by the lowest Mg/(Mg+Fe) ratio (0.13–0.18) and high Al content, which are consistent with the composition of magmatic tourmaline in the Himalayan leucogranites. The rims of Tur-L show higher Mg/(Mg + Fe) ratio (0.32–0.45) and moderate Ca, Ti, F contents, reflecting the contribution of the host schist via contamination. The composition of Tur-C rims is similar to that of Tur-M, suggesting a more significant contribution from contaminated schist than that for Tur-L. The different types of tourmaline share consistent B isotope compositions with δ<sup>11</sup>B ranging from −14 to −12‰. The compositional characteristics of the contaminated tourmaline and the mineral assemblage in the contaminated zone suggest that the involved components derived from the host schist, probably produced by the decomposition of tourmaline, biotite, plagioclase, primarily include Ca, K, Mg, Ti, B and F. In addition, contamination by tourmaline-bearing metapelite may be an alternative interpretation responsible for the peraluminous character of the Himalayan leucogranites. The occurrence of contaminated tourmaline in leucogranites suggests that contamination of host rocks is a possible way promoting the formation of tourmaline in the Himalayan leucogranites, and tourmaline is useful for deciphering the related contamination processes.