cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21258/1651035
title: Inventario forestale nazionale svizzero - Tabella n. 892831
DOI: 10.21258/1639723
title: Inventario forestale nazionale svizzero - Tabella n. 891831
DOI: 10.21258/1652151
title: Inventario forestale nazionale svizzero - Tabella n. 898343
DOI: 10.21258/1665487
title: Inventario forestale nazionale svizzero - Tabella n. 899241
DOI: 10.21258/1632902
title: Inventario forestale nazionale svizzero - Tabella n. 898986
DOI: 10.21227/m75f-p767
title: ECG data for deep transfer learning

description: Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks, AlexNet and GoogLeNet, as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using 8 volunteers. The signals are pre-processed using elliptical filter for signal noises like baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECG with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental data set was augmented by adding two different publicly available data sets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel", but also presents a lightweight solution to otherwise computationally heavy problems.
DOI: 10.21258/1648646
title: Inventario forestale nazionale svizzero - Tabella n. 898262
DOI: 10.21258/1644665
title: Inventario forestale nazionale svizzero - Tabella n. 896910
DOI: 10.5281/zenodo.4333642
title: Processed data for the sRNA landscape chapter

description: <strong>Processed data to be used in analyses related to the sRNA landscape. </strong> <strong>1) small RNA processed data from stem trichomes</strong>: 2020-12-17_11-23_results_stem_trichomes.tar.gz Original small RNA-seq fastq files: available at https://doi.org/10.5281/zenodo.4105911 Software: small-rna-seq-pipeline v0.4.4 available at https://zenodo.org/record/4333786 <strong>2) small RNA processed data from bald stem, leaf primordium and leaf: </strong> xxxx === to be added === xxx <strong>3) mRNA-seq processed data (raw and scaled counts) from different tissues (stem trichomes, bald stem, leaf, leaf primordium): </strong> 20201117_snakemake_messenger_rnaseq_trichomes_and_other_tissues.tar.gz This file was obtained from the following original mRNA-seq fastq files: Stem trichomes of Moneymaker: dataset available here Stem trichomes of LA0716: dataset available here Stem trichomes of PI127826: dataset available here Bald stems, leaf primordia and leaves of Moneymaker, LA0716 and PI127826: datasets are available here. Samples S28 to S48 were used. The pipeline used was Snakemake RNA-seq release 0.3.4 The file contains: raw_counts.parsed.tsv: contains the raw counts that can be used for differential expression analysis (e.g. with DESeq2). scaled_counts.tsv: contains counts that are scaled between samples. This can be used for heatmap creation or PCA analysis for instance. NOT for differential analysis. samples.tsv.: a file listing the fastq files analysed. config.yaml: a file that contains the parameters used when running the pipeline.
DOI: 10.21227/1fts-km57
title: ECG data for deep transfer learning

description: Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks, AlexNet and GoogLeNet, as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using 8 volunteers. The signals are pre-processed using elliptical filter for signal noises like baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECG with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental data set was augmented by adding two different publicly available data sets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel", but also presents a lightweight solution to otherwise computationally heavy problems.
DOI: 10.21258/1646709
title: Inventario forestale nazionale svizzero - Tabella n. 902281
DOI: 10.5517/ccdc.csd.cc228dtl
title: CCDC 1914709: Experimental Crystal Structure Determination

description: Related Article: Zhongrong Meng, Qiongqiong Wang, Doudou Lu, Tingting Yue, Pu Ai, Hua Liu, Weiran Yang, Jing Zheng|2020|J.Org.Chem.|85|15026|doi:10.1021/acs.joc.0c01919
DOI: 10.5517/ccdc.csd.cc23b11k
title: CCDC 1946057: Experimental Crystal Structure Determination

description: Related Article: Zhongrong Meng, Qiongqiong Wang, Doudou Lu, Tingting Yue, Pu Ai, Hua Liu, Weiran Yang, Jing Zheng|2020|J.Org.Chem.|85|15026|doi:10.1021/acs.joc.0c01919
DOI: 10.21258/1661822
title: Inventario forestale nazionale svizzero - Tabella n. 899530
DOI: 10.5517/ccdc.csd.cc244t04
title: CCDC 1970825: Experimental Crystal Structure Determination

description: Related Article: Kai Zhao, Ze-Yu Zhang, Xian-Lu Cui, Ying-Xue Wang, Xian-Dan Wu, Wei-Ming Li, Jia-Xu Wu, Li-Li Zhao, Jing-Yu Guo, Teck-Peng Loh|2020|Org.Lett.|22|9029|doi:10.1021/acs.orglett.0c03418
DOI: 10.5517/ccdc.csd.cc246g3y
title: CCDC 1972409: Experimental Crystal Structure Determination

description: Related Article: Georgina E. Shillito, Samantha E. Bodman, Joseph I. Mapley, Christopher M. Fitchett, Keith C. Gordon|2020|Inorg.Chem.|59|16967|doi:10.1021/acs.inorgchem.0c02102
DOI: 10.5517/ccdc.csd.cc246g4z
title: CCDC 1972410: Experimental Crystal Structure Determination

description: Related Article: Georgina E. Shillito, Samantha E. Bodman, Joseph I. Mapley, Christopher M. Fitchett, Keith C. Gordon|2020|Inorg.Chem.|59|16967|doi:10.1021/acs.inorgchem.0c02102
DOI: 10.5517/ccdc.csd.cc246g50
title: CCDC 1972411: Experimental Crystal Structure Determination

description: Related Article: Georgina E. Shillito, Samantha E. Bodman, Joseph I. Mapley, Christopher M. Fitchett, Keith C. Gordon|2020|Inorg.Chem.|59|16967|doi:10.1021/acs.inorgchem.0c02102
DOI: 10.5517/ccdc.csd.cc246g61
title: CCDC 1972412: Experimental Crystal Structure Determination

description: Related Article: Georgina E. Shillito, Samantha E. Bodman, Joseph I. Mapley, Christopher M. Fitchett, Keith C. Gordon|2020|Inorg.Chem.|59|16967|doi:10.1021/acs.inorgchem.0c02102
DOI: 10.5517/ccdc.csd.cc24g391
title: CCDC 1979762: Experimental Crystal Structure Determination

description: Related Article: Ingredy Bastos Braga, Sandra Milena Bonilla Castañeda, João Vitor de Assis, Amanda Oliveira Barros, Giovanni Wilson Amarante, Ana Karoline Silva Mendanha Valdo, Felipe Terra Martins, Amanda Francisca do Prado Rosolen, Eduardo Pilau, Sergio Antonio Fernandes|2020|J.Org.Chem.|85|15622|doi:10.1021/acs.joc.0c02459
DOI: 10.5517/ccdc.csd.cc24g3b2
title: CCDC 1979763: Experimental Crystal Structure Determination

description: Related Article: Ingredy Bastos Braga, Sandra Milena Bonilla Castañeda, João Vitor de Assis, Amanda Oliveira Barros, Giovanni Wilson Amarante, Ana Karoline Silva Mendanha Valdo, Felipe Terra Martins, Amanda Francisca do Prado Rosolen, Eduardo Pilau, Sergio Antonio Fernandes|2020|J.Org.Chem.|85|15622|doi:10.1021/acs.joc.0c02459
DOI: 10.5517/ccdc.csd.cc24h763
title: CCDC 1980844: Experimental Crystal Structure Determination

description: Related Article: Jin Tong, Hong-Lin Lu, Wen-Qing Sun, Shu-Yan Yu|2020|CrystEngComm|22|8166|doi:10.1039/D0CE00520G
DOI: 10.5517/ccdc.csd.cc24h774
title: CCDC 1980845: Experimental Crystal Structure Determination

description: Related Article: Jin Tong, Hong-Lin Lu, Wen-Qing Sun, Shu-Yan Yu|2020|CrystEngComm|22|8166|doi:10.1039/D0CE00520G
DOI: 10.5517/ccdc.csd.cc24sbt3
title: CCDC 1989605: Experimental Crystal Structure Determination

description: Related Article: Dilip Kumar Maity, Fazle Haque, Arijit Halder, Debajyoti Ghoshal|2020|Cryst.Growth Des.|20|7667|doi:10.1021/acs.cgd.0c00880
DOI: 10.5517/ccdc.csd.cc24sbv4
title: CCDC 1989606: Experimental Crystal Structure Determination

description: Related Article: Dilip Kumar Maity, Fazle Haque, Arijit Halder, Debajyoti Ghoshal|2020|Cryst.Growth Des.|20|7667|doi:10.1021/acs.cgd.0c00880
DOI: 10.5517/ccdc.csd.cc24sbw5
title: CCDC 1989607: Experimental Crystal Structure Determination

description: Related Article: Dilip Kumar Maity, Fazle Haque, Arijit Halder, Debajyoti Ghoshal|2020|Cryst.Growth Des.|20|7667|doi:10.1021/acs.cgd.0c00880
DOI: 10.5517/ccdc.csd.cc24sbx6
title: CCDC 1989608: Experimental Crystal Structure Determination

description: Related Article: Dilip Kumar Maity, Fazle Haque, Arijit Halder, Debajyoti Ghoshal|2020|Cryst.Growth Des.|20|7667|doi:10.1021/acs.cgd.0c00880
DOI: 10.5517/ccdc.csd.cc24sby7
title: CCDC 1989609: Experimental Crystal Structure Determination

description: Related Article: Dilip Kumar Maity, Fazle Haque, Arijit Halder, Debajyoti Ghoshal|2020|Cryst.Growth Des.|20|7667|doi:10.1021/acs.cgd.0c00880
DOI: 10.5517/ccdc.csd.cc24sbz8
title: CCDC 1989610: Experimental Crystal Structure Determination

description: Related Article: Dilip Kumar Maity, Fazle Haque, Arijit Halder, Debajyoti Ghoshal|2020|Cryst.Growth Des.|20|7667|doi:10.1021/acs.cgd.0c00880
DOI: 10.5517/ccdc.csd.cc259j89
title: CCDC 2005150: Experimental Crystal Structure Determination

description: Related Article: Subrata Ghosh, Sujit Kamilya, Mathieu Rouzières, Radovan Herchel, Sakshi Mehta, Abhishake Mondal|2020|Inorg.Chem.|59|17638|doi:10.1021/acs.inorgchem.0c02887
DOI: 10.5517/ccdc.csd.cc259j9b
title: CCDC 2005151: Experimental Crystal Structure Determination

description: Related Article: Subrata Ghosh, Sujit Kamilya, Mathieu Rouzières, Radovan Herchel, Sakshi Mehta, Abhishake Mondal|2020|Inorg.Chem.|59|17638|doi:10.1021/acs.inorgchem.0c02887
DOI: 10.5517/ccdc.csd.cc259jbc
title: CCDC 2005152: Experimental Crystal Structure Determination

description: Related Article: Subrata Ghosh, Sujit Kamilya, Mathieu Rouzières, Radovan Herchel, Sakshi Mehta, Abhishake Mondal|2020|Inorg.Chem.|59|17638|doi:10.1021/acs.inorgchem.0c02887
DOI: 10.5517/ccdc.csd.cc259yk0
title: CCDC 2005562: Experimental Crystal Structure Determination

description: Related Article: Kai Zhao, Ze-Yu Zhang, Xian-Lu Cui, Ying-Xue Wang, Xian-Dan Wu, Wei-Ming Li, Jia-Xu Wu, Li-Li Zhao, Jing-Yu Guo, Teck-Peng Loh|2020|Org.Lett.|22|9029|doi:10.1021/acs.orglett.0c03418
DOI: 10.21258/1629156
title: Inventario forestale nazionale svizzero - Tabella n. 899202
DOI: 10.5517/ccdc.csd.cc25j3zt
title: CCDC 2011496: Experimental Crystal Structure Determination

description: Related Article: Juliana P. da Silva, Otávio Fuganti, M. Gabriela Kramer, Gianella Facchin, Lucas E. N. Aquino, Javier Ellena, Davi F. Back, Ana C. S. Gondim, Eduardo H. S. Sousa, Luiz G. F. Lopes, Silvane Machado, Ivelise D. L. Guimarães, Karen Wohnrath, Márcio P. de Araujo|2020|Dalton Trans.|49|16498|doi:10.1039/D0DT02500C
DOI: 10.5517/ccdc.csd.cc25j40w
title: CCDC 2011497: Experimental Crystal Structure Determination

description: Related Article: Juliana P. da Silva, Otávio Fuganti, M. Gabriela Kramer, Gianella Facchin, Lucas E. N. Aquino, Javier Ellena, Davi F. Back, Ana C. S. Gondim, Eduardo H. S. Sousa, Luiz G. F. Lopes, Silvane Machado, Ivelise D. L. Guimarães, Karen Wohnrath, Márcio P. de Araujo|2020|Dalton Trans.|49|16498|doi:10.1039/D0DT02500C
DOI: 10.5517/ccdc.csd.cc25n534
title: CCDC 2015375: Experimental Crystal Structure Determination

description: Related Article: Xianhuang Zeng, Vyom Shukla, Dale L. Boger|2020|J.Org.Chem.|85|14817|doi:10.1021/acs.joc.0c02493
DOI: 10.5517/ccdc.csd.cc25vc3j
title: CCDC 2021327: Experimental Crystal Structure Determination

description: Related Article: Shaoqun Zhu, Haibo Yang, Along Jiang, Bing Zhou, Ying Han, Chaoguo Yan, Yaocheng Shi, Hong Hou|2020|J.Org.Chem.|85|15667|doi:10.1021/acs.joc.0c02114
DOI: 10.5517/ccdc.csd.cc25xwpp
title: CCDC 2023763: Experimental Crystal Structure Determination

description: Related Article: Isa Kallweit, Michael Laue, Christoph Schneider|2020|Org.Lett.|22|9065|doi:10.1021/acs.orglett.0c03452
DOI: 10.21258/1660912
title: Inventario forestale nazionale svizzero - Tabella n. 897025
DOI: 10.21258/1632449
title: Inventario forestale nazionale svizzero - Tabella n. 898164
DOI: 10.21258/1647296
title: Inventario forestale nazionale svizzero - Tabella n. 899541
DOI: 10.21258/1649839
title: Inventario forestale nazionale svizzero - Tabella n. 899426
DOI: 10.21258/1648461
title: Inventario forestale nazionale svizzero - Tabella n. 897475
DOI: 10.21258/1631753
title: Inventario forestale nazionale svizzero - Tabella n. 899353
DOI: 10.21258/1662374
title: Inventario forestale nazionale svizzero - Tabella n. 897855
DOI: 10.21258/1667220
title: Inventario forestale nazionale svizzero - Tabella n. 897293
DOI: 10.21258/1639301
title: Inventario forestale nazionale svizzero - Tabella n. 898492
DOI: 10.21258/1637449
title: Inventario forestale nazionale svizzero - Tabella n. 891867
DOI: 10.25405/data.ncl.13387280
title: Active Building Data

description: Files included in this data repository are the input of a mixed integer linear programming approach that is utilised to investigate the role of active building in a paper entitled ''Optimising Building-to-Building and Buildings-for-Grid Services in Residential Microgrids: A Robust Rolling Horizon Approach''<br>
DOI: 10.25405/data.ncl.13387280.v2
title: Active Building Data

description: Files included in this data repository are the input of a mixed integer linear programming approach that is utilised to investigate the role of active building in a paper entitled ''Optimising Building-to-Building and Buildings-for-Grid Services in Residential Microgrids: A Robust Rolling Horizon Approach''<br>
DOI: 10.25405/data.ncl.13387280.v1
title: Energy scheduling

description: Files included in this data repository are the input of a mixed integer linear programming approach that is utilised to investigate the role of active building in a paper entitled ''Optimising Building-to-Building and Buildings-for-Grid Services in Residential Microgrids: A Robust Rolling Horizon Approach''<br>
DOI: 10.6084/m9.figshare.12481322
title: Metabolomics and lipidomics Palma et al.

description: A strong relationship between metabolic state and susceptibility to <i>Mycobacterium Tuberculosis</i> (MTB) infection exists. Indeed, intracellular energy metabolism sets the basis for an exaggerated immuno-inflammatory response which concur to pathogenesis of MTB. Herein, we show that controlled caloric restriction (CR), not leading to malnutrition, induces control of MTB infection by dramatically reducing both bacterial load and lung immunopathology in highly susceptible DBA/2 mice. CR reduced significantly the production of pro-inflammatory cytokines in lung granulomas as well as formation of foam cells, known to represent a major reservoir for MTB replication. Mechanistically, CR induced a shift of the glucose metabolism towards glycolysis in splenocytes and in T cells that associated with reduction of the mechanistic target of rapamycin (mTOR) pathway in the lungs. Further, an integrated multi-OMICS approach revealed a specific CR-induced metabolomic, trascriptomic and proteomic signature leading to both reduced lung damage and slow disease progression. Taken together our data suggest that CR represents a feasible immunometabolic manipulation able to reverse a state of susceptibility towards resistance to MTB infection, and that our approach offers a novel and unexpected strategy to boost therapeutic interventions against MTB infection.<b></b>
DOI: 10.6084/m9.figshare.12481322.v1
title: Metabolomics and lipidomics Palma et al.

description: A strong relationship between metabolic state and susceptibility to <i>Mycobacterium Tuberculosis</i> (MTB) infection exists. Indeed, intracellular energy metabolism sets the basis for an exaggerated immuno-inflammatory response which concur to pathogenesis of MTB. Herein, we show that controlled caloric restriction (CR), not leading to malnutrition, induces control of MTB infection by dramatically reducing both bacterial load and lung immunopathology in highly susceptible DBA/2 mice. CR reduced significantly the production of pro-inflammatory cytokines in lung granulomas as well as formation of foam cells, known to represent a major reservoir for MTB replication. Mechanistically, CR induced a shift of the glucose metabolism towards glycolysis in splenocytes and in T cells that associated with reduction of the mechanistic target of rapamycin (mTOR) pathway in the lungs. Further, an integrated multi-OMICS approach revealed a specific CR-induced metabolomic, trascriptomic and proteomic signature leading to both reduced lung damage and slow disease progression. Taken together our data suggest that CR represents a feasible immunometabolic manipulation able to reverse a state of susceptibility towards resistance to MTB infection, and that our approach offers a novel and unexpected strategy to boost therapeutic interventions against MTB infection.<b></b>
DOI: 10.21258/1645040
title: Inventario forestale nazionale svizzero - Tabella n. 893456
DOI: 10.6084/m9.figshare.12666416
title: Cost-free lifespan extension via optimisation of gene expression in adulthood aligns with the developmental theory of ageing

description: Data for the following manuscript: https://www.biorxiv.org/content/10.1101/739490v1<br>Please see manuscript for methods and analyses.<br><br>Provided are files for Lifespan, Reproduction and Egg size for C. elegans worms subjected to Control (empty vector) or RNAi against one of five five different genes: age-1, ife-2, ifg-1, nuo-6 or raga-1. RNAi have either been lifelong (L), during adulthood (D1=from day1 of adulthood) or post-reproductive (D6=from day 6 of adulthood).<br>In addition, we also provide QPCR data on gene expression downregulation of the five genes when using RNAi, see methods for details.<br><br>Some data provided may not be included in the analysis, please see supplementary methods for details.<br>For the lifespan files, Event is coded as follows:Event: 1=Dead (natural death or matricide), 0=Censored<br>Matricide: 1=Matricide<br>EventWOM: 1=Dead (natural death), 0=Censored (including matricide as censored)<br>EventWOM is the one used in analyses.<br>
DOI: 10.6084/m9.figshare.12666416.v1
title: Cost-free lifespan extension via optimisation of gene expression in adulthood aligns with the developmental theory of ageing

description: Data for the following manuscript: https://www.biorxiv.org/content/10.1101/739490v1<br>Please see manuscript for methods and analyses.<br><br>Provided are files for Lifespan, Reproduction and Egg size for C. elegans worms subjected to Control (empty vector) or RNAi against one of five five different genes: age-1, ife-2, ifg-1, nuo-6 or raga-1. RNAi have either been lifelong (L), during adulthood (D1=from day1 of adulthood) or post-reproductive (D6=from day 6 of adulthood).<br>Some data provided may not be included in the analysis, please see supplementary methods for details.<br>For the lifespan files, Event is coded as follows:Event: 1=Dead (natural death or matricide), 0=Censored<br>Matricide: 1=Matricide<br>EventWOM: 1=Dead (natural death), 0=Censored (including matricide as censored)<br>EventWOM is the one used in analyses.<br>
DOI: 10.21258/1654297
title: Inventario forestale nazionale svizzero - Tabella n. 899622
DOI: 10.21258/1656517
title: Inventario forestale nazionale svizzero - Tabella n. 898261
DOI: 10.5286/isis.e.rb2070001-2
title: Magnetism and Superconductivity in under-doped region of T*-type La1-x/2Eu1-x/2SrxCuO4-yFy

description: We propose the muSR measurements on La0.91Eu0.91Sr0.18CuO4-yFy, which has CuO5 pyramid structure with five coordination of Cu ions, to investigate the ground state of the parent compound and the doping evolution of magnetism in T*-type cuprate. After the discovery of superconductivity in parent T’-type RE2CuO4 (RE = Rare earth) without cation substitution, the relationship between Cu coordination and the physical properties of lamellar cuprate oxide have been intensively studied. T*-type cuprate provides a unique opportunity to complement the lack of basic knowledge for this issue. However, no experimental study on magnetism in the under-doped region of T*-type cuprate has been reported. In this study, combined with the magnetic susceptibility measurement and our previous works, we complete the phase diagram for both as-sintere and oxygen annealed samples of T*-type cuprate.
DOI: 10.21258/1652434
title: Inventario forestale nazionale svizzero - Tabella n. 891824
DOI: 10.21258/1641069
title: Inventario forestale nazionale svizzero - Tabella n. 898952
DOI: 10.21258/1664949
title: Inventario forestale nazionale svizzero - Tabella n. 899403
DOI: 10.21258/1669579
title: Inventario forestale nazionale svizzero - Tabella n. 892840
DOI: 10.21258/1657347
title: Inventario forestale nazionale svizzero - Tabella n. 899119
DOI: 10.21258/1666727
title: Inventario forestale nazionale svizzero - Tabella n. 898725
DOI: 10.21258/1633286
title: Inventario forestale nazionale svizzero - Tabella n. 891708