cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4205760.v1
title: Supplemental-File-1 from Evolution in eggs and phases: experimental evolution of fecundity and reproductive timing in <i>Caenorhabditis elegans</i>

description: To examine the role of natural selection on fecundity in a variety of Caenorhabditis elegans genetic backgrounds, we used an experimental evolution protocol to evolve 14 distinct genetic strains over 15-20 generations. We were able to generate 790 distinct genealogies, which provided information on both the effects of natural selection and the evolvability of each strain. Among these genotypes are a wild-type (N2) and a collection of mutants with targeted mutations in the daf-c, daf-d and AMPK pathways. Differences are observed in reproductive fitness along with related changes in reproductive timing. The majority of selective effects on fecundity occur during the first few generations of evolution, while the negative selection for reproductive timing occurs on longer time scales. In addition, positive selection on fecundity results in positive and negative strain-dependent selection on reproductive timing. A derivative of population size per generation called reproductive carry-over (RCO) may be informative in terms of developmental selection. While these findings transcend mutations in a specific gene, changes in the RCO measure may nevertheless be products of selection. In conclusion, the broader implications of these findings are discussed, particularly in the context of genotype-fitness maps and the role of uncharacterized mutations in individual variation and evolvability.
DOI: 10.7910/dvn/v1rjpf/jtjvs9
title: cbb20160402_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zcp8ah
title: cbb20160323_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sgvsdq
title: cbb20160203_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/flpvmu
title: cbb20160815_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yagvd1
title: cbb20160213_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xgisi0
title: cbb20160916_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/nsntij
title: cbb20160827_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205289
title: Biotransformation of 5-en-3β-ol steroids by <i>Mucor circinelloides lusitanicus</i>

description: In this work, we report the mode of biotransformation of 5-en-3β-ol steroids using <i>Mucor circinelloides lusitanicus</i> for the first time. Here, we selected seven 5-en-3β-ol steroids as substrates. The main characteristic of the fungus was to introduce a 7α-hydroxyl group into substrates <b>1</b>--<b>5</b>. With substrate <b>2</b>, 3β, 7α, 11α-trihydroxypregna-5-en-20-one (<b>2b</b>) was obtained as the final product in good yield (46.4%). All the metabolites were determined by infrared spectra, high-resolution mass spectrometry, proton nuclear magnetic resonance, and carbon-13 nuclear magnetic resonance.
DOI: 10.7910/dvn/v1rjpf/32jj7y
title: cbb20160525_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205289.v1
title: Biotransformation of 5-en-3β-ol steroids by <i>Mucor circinelloides lusitanicus</i>

description: In this work, we report the mode of biotransformation of 5-en-3β-ol steroids using <i>Mucor circinelloides lusitanicus</i> for the first time. Here, we selected seven 5-en-3β-ol steroids as substrates. The main characteristic of the fungus was to introduce a 7α-hydroxyl group into substrates <b>1</b>--<b>5</b>. With substrate <b>2</b>, 3β, 7α, 11α-trihydroxypregna-5-en-20-one (<b>2b</b>) was obtained as the final product in good yield (46.4%). All the metabolites were determined by infrared spectra, high-resolution mass spectrometry, proton nuclear magnetic resonance, and carbon-13 nuclear magnetic resonance.
DOI: 10.7910/dvn/v1rjpf/4okazz
title: cbb20160813_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/6golzl
title: cbb20160111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/2e8mmg
title: cbb20160722_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9jmgob
title: cbb20160314_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205259
title: Supplementary Material for: Factors Influencing Decision-Making for or against Adjuvant and Neoadjuvant Chemotherapy in Postmenopausal Hormone Receptor-Positive Breast Cancer Patients in the EvAluate-TM Study

description: <i>Background:</i> Decision-making for or against neoadjuvant or adjuvant chemotherapy in postmenopausal patients with hormone receptor-positive breast cancer does not follow any clear guidelines, and some patients may unnecessarily undergo chemotherapy and be exposed to the associated toxicity. The aim of this study was to identify the patient population for whom this issue may bear relevance. <i>Methods:</i>Patients being treated with letrozole in the prospective multicenter noninterventional EvAluate-TM study were recruited. The percentage of patients receiving chemotherapy and factors associated with chemotherapy administration were identified. <i>Results:</i> In all, 3,924 (37.4%) patients received chemotherapy before treatment with letrozole. Of these, 293 (20%) underwent neoadjuvant therapy. Younger age was predictive for both adjuvant and neoadjuvant therapy. Overall, decisions in favor of administering chemotherapy are more likely to be made in patients with a higher body mass index (BMI), and neoadjuvant chemotherapy is administered at a higher rate in women with a lower BMI. Concomitant medication influenced the overall decision-making regarding chemotherapy, irrespective of whether it was given on a neoadjuvant or adjuvant basis. <i>Conclusion:</i> There is an ongoing debate as to whether all of the many patients who receive chemotherapy actually benefit from it. Neoadjuvant chemotherapy is frequently administered in this patient population, and this should encourage further research to resolve current clinical and research issues.
DOI: 10.7910/dvn/v1rjpf/f8ggwf
title: cbb20161105_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/z581n9
title: cbb20160927_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205259.v1
title: Supplementary Material for: Factors Influencing Decision-Making for or against Adjuvant and Neoadjuvant Chemotherapy in Postmenopausal Hormone Receptor-Positive Breast Cancer Patients in the EvAluate-TM Study

description: <i>Background:</i> Decision-making for or against neoadjuvant or adjuvant chemotherapy in postmenopausal patients with hormone receptor-positive breast cancer does not follow any clear guidelines, and some patients may unnecessarily undergo chemotherapy and be exposed to the associated toxicity. The aim of this study was to identify the patient population for whom this issue may bear relevance. <i>Methods:</i>Patients being treated with letrozole in the prospective multicenter noninterventional EvAluate-TM study were recruited. The percentage of patients receiving chemotherapy and factors associated with chemotherapy administration were identified. <i>Results:</i> In all, 3,924 (37.4%) patients received chemotherapy before treatment with letrozole. Of these, 293 (20%) underwent neoadjuvant therapy. Younger age was predictive for both adjuvant and neoadjuvant therapy. Overall, decisions in favor of administering chemotherapy are more likely to be made in patients with a higher body mass index (BMI), and neoadjuvant chemotherapy is administered at a higher rate in women with a lower BMI. Concomitant medication influenced the overall decision-making regarding chemotherapy, irrespective of whether it was given on a neoadjuvant or adjuvant basis. <i>Conclusion:</i> There is an ongoing debate as to whether all of the many patients who receive chemotherapy actually benefit from it. Neoadjuvant chemotherapy is frequently administered in this patient population, and this should encourage further research to resolve current clinical and research issues.
DOI: 10.6084/m9.figshare.4205244
title: Jurabrontes curtedulensis virtual tracks generated with DigTrace

description: <b><i>Jurabrontes curtedulensis</i> virtual tracks generated with DigTrace</b><br>Virtual tracks, (point-clouds, .csv) generated with DigTrace Academic (www.digtrace.co.uk) comparing:A: the digitized tracks of the holotype trackway SCR1500-T1 track (7 specimens)B: the holotype and paratypes (4 specimens)<br>The software compares a track population based in the topographical differences among the individual specimens and provided virtual tracks based on statistical comparisons (listed in the file name).1. mean2. median3. max4. min5. standard deviation (std)6. peak-to-peak (ptp)<br>This is a supporting material for http://dx.doi.org/10.1080/08912963.2017.1324438<br><b>Citation for this article</b>: Marty D., Belvedere M., Razzolini N.L., Lockley M.G., Paratte G., Cattin M., Lovis C. and Meyer C.A. 2017. The tracks of giant theropods (<i>Jurabrontes curtedulensis</i> ichnogen. & ichnosp. nov.) from the Late Jurassic of NW Switzerland: palaeoecological and palaeogeographical implications. Historical Biology. doi: 10.1080/08912963.2017.1324438<br><br>
DOI: 10.7910/dvn/v1rjpf/5nhkvw
title: cbb20160220_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zhnkkf
title: cbb20160613_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wgmj5v
title: cbb20160802_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/chcnsf
title: cbb20160109_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gfbbao
title: cbb20160405_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/1bcvro
title: cbb20160228_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/k93acq
title: cbb20160603_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/0lq0j4
title: cbb20160229_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/y8nr0o
title: cbb20161118_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fkngwm
title: cbb20160728_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/e2l7eo
title: cbb20161205_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/u9yw41
title: cbb20161027_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yxukoo
title: cbb20160925_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/thgh00
title: cbb20160812_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wlfpjf
title: cbb20161217_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vc4rs8
title: cbb20160315_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/shityy
title: cbb20160606_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yen2rv
title: cbb20161026_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3a4lyg
title: cbb20161025_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205079
title: Data from: Mei et al. (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize Front. Plant Sci. | doi: 10.3389/fpls.2017.00694

description: <b>GTF file of maize B73 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_B73_entropyscore2_IRfilter_fpkm1frac0.05.gtf<b>GTF file of maize Mo17 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Mo17_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><b>GTF file of sorghum alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Sorghum_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><br><b>maize B73 alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_B73_March18th_2015.indiv_splice_labels_and_coords.dat<b>maize Mo17 alternative splicing events table:</b> Final_PASA_OUTPUT_pasa_wmei_Mo17_March18_2015.indiv_splice_labels_and_coords.dat<b>sorghum alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_sorghum_March19_2015.indiv_splice_labels_and_coords.dat<br><br>If you use the data, please cite:Wenbin Mei, Sanzhen Liu, James C. Schnable, Cheng-Ting Yeh, Nathan M. Springer, Patrick S. Schnable and William B. Barbazuk. (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize. Front. Plant Sci. | doi: 10.3389/fpls.2017.00694
DOI: 10.7910/dvn/v1rjpf/r2zsun
title: cbb20160524_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mffhw7
title: cbb20160225_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205079.v1
title: Data from: A comprehensive analysis of alternative splicing in paleopolyploid maize

description: <b>GTF file of maize B73 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_B73_entropyscore2_IRfilter_fpkm1frac0.05.gtf<b>GTF file of maize Mo17 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Mo17_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><b>GTF file of sorghum alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Sorghum_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><br><b>maize B73 alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_B73_March18th_2015.indiv_splice_labels_and_coords.dat<b>maize Mo17 alternative splicing events table:</b> Final_PASA_OUTPUT_pasa_wmei_Mo17_March18_2015.indiv_splice_labels_and_coords.dat<b>sorghum alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_sorghum_March19_2015.indiv_splice_labels_and_coords.dat<br><br>If you use the data, please cite:Mei et al. <b>A comprehensive analysis of alternative splicing in paleopolyploid maize</b>. (In review)
DOI: 10.7910/dvn/v1rjpf/lj18qu
title: cbb20161214_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4205079.v3
title: Data from: Mei et al. (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize Front. Plant Sci. | doi: 10.3389/fpls.2017.00694

description: <b>GTF file of maize B73 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_B73_entropyscore2_IRfilter_fpkm1frac0.05.gtf<b>GTF file of maize Mo17 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Mo17_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><b>GTF file of sorghum alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Sorghum_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><br><b>maize B73 alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_B73_March18th_2015.indiv_splice_labels_and_coords.dat<b>maize Mo17 alternative splicing events table:</b> Final_PASA_OUTPUT_pasa_wmei_Mo17_March18_2015.indiv_splice_labels_and_coords.dat<b>sorghum alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_sorghum_March19_2015.indiv_splice_labels_and_coords.dat<br><br>If you use the data, please cite:Wenbin Mei, Sanzhen Liu, James C. Schnable, Cheng-Ting Yeh, Nathan M. Springer, Patrick S. Schnable and William B. Barbazuk. (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize. Front. Plant Sci. | doi: 10.3389/fpls.2017.00694
DOI: 10.6084/m9.figshare.4205079.v2
title: Data from: Mei et al. (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize Front. Plant Sci. | doi: 10.3389/fpls.2017.00694

description: <b>GTF file of maize B73 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_B73_entropyscore2_IRfilter_fpkm1frac0.05.gtf<b>GTF file of maize Mo17 alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Mo17_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><b>GTF file of sorghum alternative splicing isoforms:</b> Final_FilterSingleTranscriptLoci_Sorghum_entropyscore2_IRfilter_fpkm1frac0.05.gtf<br><br><b>maize B73 alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_B73_March18th_2015.indiv_splice_labels_and_coords.dat<b>maize Mo17 alternative splicing events table:</b> Final_PASA_OUTPUT_pasa_wmei_Mo17_March18_2015.indiv_splice_labels_and_coords.dat<b>sorghum alternative splicing events table: </b>Final_PASA_OUTPUT_pasa_wmei_sorghum_March19_2015.indiv_splice_labels_and_coords.dat<br><br>If you use the data, please cite:Wenbin Mei, Sanzhen Liu, James C. Schnable, Cheng-Ting Yeh, Nathan M. Springer, Patrick S. Schnable and William B. Barbazuk. (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize. Front. Plant Sci. | doi: 10.3389/fpls.2017.00694
DOI: 10.6084/m9.figshare.4204965
title: Responsible and effective use of metrics/indicators

description: handout and slides for Impact Report Writing library workshop with a focus on use of metrics
DOI: 10.7910/dvn/v1rjpf/9gadx0
title: cbb20160410_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4204965.v1
title: Responsible and effective use of metrics/indicators

description: handout and slides for Impact Report Writing library workshop with a focus on use of metrics
DOI: 10.7910/dvn/v1rjpf/su8cw1
title: cbb20160831_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4204959
title: Microarray Wang et al.zip

description: The pro-fibrotic role of dipeptidyl peptidase 4 in carbon tetrachloride induced experimental liver injury<br>Microarray data on BL6 and DPP4 gko mice
DOI: 10.6084/m9.figshare.4204956
title: Responses to survey of mathematical publishing

description: Cleaned and categorised responses to survey of mathematical publishing
DOI: 10.6084/m9.figshare.4204959.v1
title: Microarray Wang et al.zip

description: The pro-fibrotic role of dipeptidyl peptidase 4 in carbon tetrachloride induced experimental liver injury<br>Microarray data on BL6 and DPP4 gko mice
DOI: 10.6084/m9.figshare.4204956.v1
title: Responses to survey of mathematical publishing

description: Cleaned and categorised responses to survey of mathematical publishing
DOI: 10.7910/dvn/v1rjpf/7aukfh
title: cbb20160327_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4204935
title: Dataset for: Estimating Personalized Diagnostic Rules Depending on Individualized Characteristics

description: There is an increasing demand for personalization of disease screening based on assessment of patient risk and other characteristics. For example, in breast cancer screening, advanced imaging technologies have made it possible to move away from "one-size-fits-all" screening guidelines to targeted risk-based screening for those who are in need. Since diagnostic performance of various imaging modalities may vary across subjects, applying the most accurate modality to the patients who would benefit the most requires personalized strategy. To address these needs, we propose novel machine learning methods to estimate personalized diagnostic rules for medical screening or diagnosis by maximizing a weighted combination of sensitivity and specificity across subgroups of subjects. We first develop methods that can be applied to estimate personalized diagnostic rules where competing modalities or screening strategies are observed on the same subject (paired design). Next, we present methods for studies where not all subjects receive both modalities (unpaired design). We study theoretical properties including consistency and risk bound of the personalized diagnostic rules, and conduct simulation studies to demonstrate effectiveness of the proposed methods. Lastly, we analyze data collected from a brain imaging study of Parkinson's disease using positron emission tomography (PET) or diffusion tensor imaging (DTI) modalities with paired and unpaired designs. Our results show that in some cases a personalized modality assignment is estimated to improve empirical AUC compared to a "one-size-fits-all" assignment strategy.
DOI: 10.6084/m9.figshare.4204932
title: Dataset for: Meta-analysis of aggregate data on medical events

description: Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared to time-to-event methods. To deal with differences in follow-up - at the cost of assuming specific distributions for event and drop-out times - we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study.
DOI: 10.6084/m9.figshare.4204935.v1
title: Dataset for: Estimating Personalized Diagnostic Rules Depending on Individualized Characteristics

description: There is an increasing demand for personalization of disease screening based on assessment of patient risk and other characteristics. For example, in breast cancer screening, advanced imaging technologies have made it possible to move away from "one-size-fits-all" screening guidelines to targeted risk-based screening for those who are in need. Since diagnostic performance of various imaging modalities may vary across subjects, applying the most accurate modality to the patients who would benefit the most requires personalized strategy. To address these needs, we propose novel machine learning methods to estimate personalized diagnostic rules for medical screening or diagnosis by maximizing a weighted combination of sensitivity and specificity across subgroups of subjects. We first develop methods that can be applied to estimate personalized diagnostic rules where competing modalities or screening strategies are observed on the same subject (paired design). Next, we present methods for studies where not all subjects receive both modalities (unpaired design). We study theoretical properties including consistency and risk bound of the personalized diagnostic rules, and conduct simulation studies to demonstrate effectiveness of the proposed methods. Lastly, we analyze data collected from a brain imaging study of Parkinson's disease using positron emission tomography (PET) or diffusion tensor imaging (DTI) modalities with paired and unpaired designs. Our results show that in some cases a personalized modality assignment is estimated to improve empirical AUC compared to a "one-size-fits-all" assignment strategy.
DOI: 10.7910/dvn/v1rjpf/qnqob5
title: cbb20160201_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pmg37h
title: cbb20160122_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/l6nqrr
title: cbb20160526_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4204824
title: Recording Data

description: Eye movement recordings
DOI: 10.7910/dvn/v1rjpf/bakmad
title: cbb20160120_03.txt.gz

description: :unav