cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35078/pnwot4/btxvok
title: Córrego A.jpg

description: :unav
DOI: 10.35078/pnwot4/opwgmw
title: Lagoa do Peixe B.jpg

description: :unav
DOI: 10.35078/pnwot4/cdqhk4
title: Xavier C.jpg

description: :unav
DOI: 10.35078/pnwot4/mlm3kr
title: São Geraldo A.jpg

description: :unav
DOI: 10.35078/pnwot4/ji7i59
title: Igrejinha B.jpg

description: :unav
DOI: 10.35078/pnwot4/tvw5er
title: Lagoa do São João B.jpg

description: :unav
DOI: 10.35078/pnwot4/az39dz
title: Córrego Santa Maria C.jpg

description: :unav
DOI: 10.35078/pnwot4/prs0ad
title: Poço do Pedro A.jpg

description: :unav
DOI: 10.35078/pnwot4/jwxwbf
title: Malhada Real A.jpg

description: :unav
DOI: 10.35078/pnwot4/80jmyd
title: Santo Antônio B.jpg

description: :unav
DOI: 10.35078/pnwot4/jtafwd
title: Xavier A.jpg

description: :unav
DOI: 10.35078/pnwot4/y2diil
title: Analina B.jpg

description: :unav
DOI: 10.35078/pnwot4/lb7jlb
title: Taboleiro Novo D.jpg

description: :unav
DOI: 10.35078/pnwot4/mgsiyy
title: Barreiro I B.jpg

description: :unav
DOI: 10.35078/pnwot4/l1ym60
title: Lagoa da União D.jpg

description: :unav
DOI: 10.35078/pnwot4/omusch
title: Caraíbas II Cid. D.jpg

description: :unav
DOI: 10.35078/pnwot4/oksfd3
title: Lagoa Nova A.jpg

description: :unav
DOI: 10.35078/pnwot4/k9qslg
title: Capitão Enéas Cid. B.jpg

description: :unav
DOI: 10.35078/pnwot4/zoa8rz
title: Barreiro II C.jpg

description: :unav
DOI: 10.35078/pnwot4/nng8ts
title: Igrejinha A.jpg

description: :unav
DOI: 10.35078/pnwot4/8drr0m
title: Malhada Real B.jpg

description: :unav
DOI: 10.35078/pnwot4/depnvf
title: Barra do Córrego I B.jpg

description: :unav
DOI: 10.35078/pnwot4/01x33p
title: Compressor A.jpg

description: :unav
DOI: 10.35078/pnwot4/2m9sw3
title: Nova Holanda D.jpg

description: :unav
DOI: 10.35078/pnwot4/yppkc3
title: Sant'Ana B.jpg

description: :unav
DOI: 10.35078/pnwot4/r7bfqv
title: Córrego B.jpg

description: :unav
DOI: 10.35078/pnwot4/zjwcbb
title: Lagoa do Peixe A.jpg

description: :unav
DOI: 10.35078/pnwot4/etbkcm
title: Igrejinha D.jpg

description: :unav
DOI: 10.35078/pnwot4/h8kioa
title: Capitão Enéas Cid. D.jpg

description: :unav
DOI: 10.35078/pnwot4/w7frad
title: Capitão Enéas Cid. A.jpg

description: :unav
DOI: 10.35078/pnwot4/juiphn
title: Poço da Pedra B.jpg

description: :unav
DOI: 10.35078/pnwot4/qibamv
title: Três Lagoas C.jpg

description: :unav
DOI: 10.35078/pnwot4/5oyq85
title: Córrego Seco C.jpg

description: :unav
DOI: 10.35078/pnwot4/ymjre7
title: Canaã D.jpg

description: :unav
DOI: 10.35078/pnwot4/ssrkkl
title: Caçarema Pov. D.jpg

description: :unav
DOI: 10.35078/pnwot4/fzph6r
title: Colorado D.jpg

description: :unav
DOI: 10.35078/pnwot4/auqk5b
title: Xavier D.jpg

description: :unav
DOI: 10.35078/pnwot4/rzelxt
title: Santa Ângela B.jpg

description: :unav
DOI: 10.35078/pnwot4/z8fkzq
title: Caraíbas II Cid. B.jpg

description: :unav
DOI: 10.35078/pnwot4/zbxoco
title: Quem Quem B.jpg

description: :unav
DOI: 10.35078/pnwot4/z7zk0t
title: Vereda D.jpg

description: :unav
DOI: 10.35078/pnwot4/hnu69j
title: Norte América B.jpg

description: :unav
DOI: 10.35078/pnwot4/y5sk3l
title: Igrejinha C.jpg

description: :unav
DOI: 10.35078/pnwot4/jullec
title: Nova Holanda B.jpg

description: :unav
DOI: 10.35078/pnwot4/iwojid
title: Poço do Pedro B.jpg

description: :unav
DOI: 10.35078/pnwot4/6jidaf
title: Analina D.jpg

description: :unav
DOI: 10.35078/pnwot4/gj2qsh
title: Sant'Ana Pov. A.jpg

description: :unav
DOI: 10.35078/pnwot4/ywdmsf
title: Vereda B.jpg

description: :unav
DOI: 10.35078/pnwot4/azoc2f
title: Sabonete A.jpg

description: :unav
DOI: 10.35078/pnwot4/nasruv
title: Barra do Córrego II A.jpg

description: :unav
DOI: 10.35078/pnwot4/fh73gq
title: DICIONARIO_KARDEX_PROGRAMA_CONTROLE_DOENÇA_CHAGAS.pdf

description: Dicionário para interpretação da ficha epidemiologica (Kardex)
DOI: 10.5281/zenodo.6458667
title: DIRECTLib - a library of global optimization problems for DIRECT-type methods

description: <strong>DIRECTLib - a library of a box and generally-constrained global optimization problems for DIRECT-type methods</strong> In this library, we present an extended collection of a box and generally constrained global optimization test problems (in MATLAB format) typically used in benchmarking various DIRECT-type [1] methods in the relevant literature (see, e.g., [2-6] and references given therein). File: <strong>WCGO_Test_results.xlsx </strong>contains<strong> </strong>experimental results presented in: https://arxiv.org/abs/2109.14912 <strong>References</strong> Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993) ‘Lipschitzian optimization without the Lipschitz constant’, <em>Journal of Optimization Theory and Applications</em>, 79(1), pp. 157–181. <strong>doi</strong><strong>: 10.1007/BF00941892</strong>. R. Paulavičius, J. Žilinskas. (2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. <strong>doi:10.1007/978-1-4614-9093-7</strong> L. Stripinis, R. Paulavičius, J. Žilinskas. (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT, Optimization Letters 12 (7) 1699–1712. <strong>doi:10.1007/s11590-017-1228-4</strong> L. Stripinis, R. Paulavičius, J. Žilinskas. (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization, Structural and Multidisciplinary Optimization 59 (6) 2155–2175. <strong>doi:10.1007/s00158-018-2181-2</strong>. L. Stripinis, J. Žilinskas, L. G. Casado, R. Paulavičius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. <em>Applied Mathematics and Computation</em>, DOI: 10.1016/j.amc.2020.125596 L. Stripinis, R. Paulavičius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. <em>Optimization Letters</em>, 15, p. 1865-1884, DOI: 10.1007/s11590-021-01726-z
description: {"references": ["L. Stripinis, R. Paulavi\u010dius, J. \u017dilinskas (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT. Optimization Letters, 12 (7), p. 1699-1712, ISSN: 1862-4472, DOI: 10.1007/s11590-017-1228-4", "L. Stripinis, R. Paulavi\u010dius, J. \u017dilinskas (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization. Structural and Multidisciplinary Optimization 59 (6), p. 2155-2175, DOI: 10.1007/s00158-018-2181-2", "L. Stripinis, J. \u017dilinskas, L. G. Casado, R. Paulavi\u010dius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. Applied Mathematics and Computation, DOI: 10.1016/j.amc.2020.125596", "L. Stripinis, R. Paulavi\u010dius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. Optimization Letters, 15, p. 1865-1884, DOI: 10.1007/s11590-021-01726-z"]}
DOI: 10.5281/zenodo.1218980
title: DIRECTLib - a library of global optimization problems for DIRECT-type methods

description: <strong>DIRECTLib - a library of a box and generally-constrained global optimization problems for DIRECT-type methods</strong> In this library, we present an extended collection of a box and generally constrained global optimization test problems (in MATLAB format) typically used in benchmarking various DIRECT-type [1] methods in the relevant literature (see, e.g., [2-6] and references given therein). File: <strong>WCGO_Test_results.xlsx </strong>contains<strong> </strong>experimental results presented in: https://arxiv.org/abs/2109.14912 <strong>References</strong> Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993) ‘Lipschitzian optimization without the Lipschitz constant’, <em>Journal of Optimization Theory and Applications</em>, 79(1), pp. 157–181. <strong>doi</strong><strong>: 10.1007/BF00941892</strong>. R. Paulavičius, J. Žilinskas. (2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. <strong>doi:10.1007/978-1-4614-9093-7</strong> L. Stripinis, R. Paulavičius, J. Žilinskas. (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT, Optimization Letters 12 (7) 1699–1712. <strong>doi:10.1007/s11590-017-1228-4</strong> L. Stripinis, R. Paulavičius, J. Žilinskas. (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization, Structural and Multidisciplinary Optimization 59 (6) 2155–2175. <strong>doi:10.1007/s00158-018-2181-2</strong>. L. Stripinis, J. Žilinskas, L. G. Casado, R. Paulavičius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. <em>Applied Mathematics and Computation</em>, DOI: 10.1016/j.amc.2020.125596 L. Stripinis, R. Paulavičius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. <em>Optimization Letters</em>, 15, p. 1865-1884, DOI: 10.1007/s11590-021-01726-z
description: {"references": ["L. Stripinis, R. Paulavi\u010dius, J. \u017dilinskas (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT. Optimization Letters, 12 (7), p. 1699-1712, ISSN: 1862-4472, DOI: 10.1007/s11590-017-1228-4", "L. Stripinis, R. Paulavi\u010dius, J. \u017dilinskas (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization. Structural and Multidisciplinary Optimization 59 (6), p. 2155-2175, DOI: 10.1007/s00158-018-2181-2", "L. Stripinis, J. \u017dilinskas, L. G. Casado, R. Paulavi\u010dius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. Applied Mathematics and Computation, DOI: 10.1016/j.amc.2020.125596", "L. Stripinis, R. Paulavi\u010dius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. Optimization Letters, 15, p. 1865-1884, DOI: 10.1007/s11590-021-01726-z"]}
DOI: 10.5281/zenodo.6458690
title: The protein organization of a red blood cell

description: Elution profiles, feature matrix, train and test ppis, and metadata for the paper "The protein organization of a red blood cell". Sae-Lee et al.,
DOI: 10.5517/ccdc.csd.cc25nxkb
title: CCDC 2016102: Experimental Crystal Structure Determination

description: Related Article: Patrycja Męcik, Bartłomiej Pigulski, Sławomir Szafert|2021|Org.Lett.|23|1066|doi:10.1021/acs.orglett.0c04275
DOI: 10.5517/ccdc.csd.cc25nxj9
title: CCDC 2016101: Experimental Crystal Structure Determination

description: Related Article: Patrycja Męcik, Bartłomiej Pigulski, Sławomir Szafert|2021|Org.Lett.|23|1066|doi:10.1021/acs.orglett.0c04275
DOI: 10.5517/ccdc.csd.cc26bctr
title: CCDC 2035764: Experimental Crystal Structure Determination

description: Related Article: Chad M. Bernier, Christine M. DuChane, Justin S. Martinez, Joseph O. Falkinham, Joseph S. Merola|2021|Organometallics|40|1670|doi:10.1021/acs.organomet.1c00166
DOI: 10.5517/ccdc.csd.cc26bcwt
title: CCDC 2035766: Experimental Crystal Structure Determination

description: Related Article: Chad M. Bernier, Christine M. DuChane, Justin S. Martinez, Joseph O. Falkinham, Joseph S. Merola|2021|Organometallics|40|1670|doi:10.1021/acs.organomet.1c00166
DOI: 10.5517/ccdc.csd.cc26bd0z
title: CCDC 2035770: Experimental Crystal Structure Determination

description: Related Article: Chad M. Bernier, Christine M. DuChane, Justin S. Martinez, Joseph O. Falkinham, Joseph S. Merola|2021|Organometallics|40|1670|doi:10.1021/acs.organomet.1c00166
DOI: 10.5517/ccdc.csd.cc26bd10
title: CCDC 2035771: Experimental Crystal Structure Determination

description: Related Article: Chad M. Bernier, Christine M. DuChane, Justin S. Martinez, Joseph O. Falkinham, Joseph S. Merola|2021|Organometallics|40|1670|doi:10.1021/acs.organomet.1c00166
DOI: 10.5517/ccdc.csd.cc26bd32
title: CCDC 2035773: Experimental Crystal Structure Determination

description: Related Article: Chad M. Bernier, Christine M. DuChane, Justin S. Martinez, Joseph O. Falkinham, Joseph S. Merola|2021|Organometallics|40|1670|doi:10.1021/acs.organomet.1c00166
DOI: 10.5517/ccdc.csd.cc26r279
title: CCDC 2047960: Experimental Crystal Structure Determination

description: Related Article: Takaaki Morise, Atsuya Muranaka, Hayato Ban, Mei Harada, Masaya Naito, Kazuyuki Yoshida, Nagao Kobayashi, Masanobu Uchiyama, Yuji Tokunaga|2021|Org.Lett.|23|2120|doi:10.1021/acs.orglett.1c00271
DOI: 10.5517/ccdc.csd.cc26rxrn
title: CCDC 2048782: Experimental Crystal Structure Determination

description: Related Article: Xinya Zhang, Siqiang Chen, Liping Zhang, Qingbo Zhang, Wenjun Zhang, Yuchan Chen, Weimin Zhang, Haibo Zhang, Changsheng Zhang|2021|Org.Lett.|23|2858|doi:10.1021/acs.orglett.1c00328
DOI: 10.5517/ccdc.csd.cc26rxsp
title: CCDC 2048783: Experimental Crystal Structure Determination

description: Related Article: Xinya Zhang, Siqiang Chen, Liping Zhang, Qingbo Zhang, Wenjun Zhang, Yuchan Chen, Weimin Zhang, Haibo Zhang, Changsheng Zhang|2021|Org.Lett.|23|2858|doi:10.1021/acs.orglett.1c00328
DOI: 10.5517/ccdc.csd.cc26sltf
title: CCDC 2049435: Experimental Crystal Structure Determination

description: Related Article: Antony Sekar Kulandai Raj, Akshay Subhash Narode, Rai-Shung Liu|2021|Org.Lett.|23|1378|doi:10.1021/acs.orglett.1c00038
DOI: 10.5517/ccdc.csd.cc25nxh8
title: CCDC 2016100: Experimental Crystal Structure Determination

description: Related Article: Patrycja Męcik, Bartłomiej Pigulski, Sławomir Szafert|2021|Org.Lett.|23|1066|doi:10.1021/acs.orglett.0c04275
DOI: 10.5517/ccdc.csd.cc29clt3
title: CCDC 2126315: Experimental Crystal Structure Determination

description: Related Article: Sergey V. Baykov, Marina V. Tarasenko, Artem V. Semenov, Evgeniy A. Katlenok, Anton A. Shetnev, Vadim P. Boyarskiy|2022|J.Mol.Struct.|1262|132974|doi:10.1016/j.molstruc.2022.132974
DOI: 10.5517/ccdc.csd.cc29kbz6
title: CCDC 2131838: Experimental Crystal Structure Determination

description: Related Article: Dengfu Lu|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc29kgy9
title: CCDC 2131961: Experimental Crystal Structure Determination

description: Related Article: Zhipeng Liu|2022|CSD Communication|||
DOI: 10.6084/m9.figshare.19294340
title: Selective Imputation of Covariates in High Dimensional Censored Data

description: Efficient modeling of censored data, that is, data which are restricted by some detection limit or truncation, is important for many applications. Ignoring the censoring can be problematic as valuable information may be missing and restoration of these censored values may significantly improve the quality of models. There are many scenarios where one may encounter censored data: survival data, interval-censored data or data with a lower limit of detection. Strategies to handle censored data are plenty, however, little effort has been made to handle censored data of high dimension. In this article, we present a selective multiple imputation approach for predictive modeling when a larger number of covariates are subject to censoring. Our method allows for iterative, subject-wise selection of covariates to impute in order to achieve a fast and accurate predictive model. The algorithm furthermore selects values for imputation which are likely to provide important information if imputed. In contrast to previously proposed methods, our approach is fully nonparametric and therefore, very flexible. We demonstrate that, in comparison to previous work, our model achieves faster execution and often comparable accuracy in a simulated example as well as predicting signal strength in radio network data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19294340.v1
title: Selective Imputation of Covariates in High Dimensional Censored Data

description: Efficient modeling of censored data, that is, data which are restricted by some detection limit or truncation, is important for many applications. Ignoring the censoring can be problematic as valuable information may be missing and restoration of these censored values may significantly improve the quality of models. There are many scenarios where one may encounter censored data: survival data, interval-censored data or data with a lower limit of detection. Strategies to handle censored data are plenty, however, little effort has been made to handle censored data of high dimension. In this article, we present a selective multiple imputation approach for predictive modeling when a larger number of covariates are subject to censoring. Our method allows for iterative, subject-wise selection of covariates to impute in order to achieve a fast and accurate predictive model. The algorithm furthermore selects values for imputation which are likely to provide important information if imputed. In contrast to previously proposed methods, our approach is fully nonparametric and therefore, very flexible. We demonstrate that, in comparison to previous work, our model achieves faster execution and often comparable accuracy in a simulated example as well as predicting signal strength in radio network data. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.6458385
title: SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs

description: This is a GeoTIFF version of the SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture data set v1.1 (Brocca et al. 2019). Conversion steps are available <strong>here</strong>. Few important notes: Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place. The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted). P05, P50 and P95 indicate quantiles derived per pixel. Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong>python tutorial</strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images. Monthly averages and s.d. of precipitation are available in the files: clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm, clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used), Downscaled monthly averages (1 km) are also available (https://doi.org/10.5281/zenodo.1435912). To cite this data set please refer to the <strong>original copy</strong> of the data set. Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong>SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</strong>. Earth Syst. Sci. Data, 11, 1583–1601. https://doi.org/10.5194/essd-11-1583-2019
description: {"references": ["Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267-280, https://doi.org/10.5194/essd-10-267-2018, 2018.", "Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Sch\u00fcller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN\u2013ASCAT (2007\u20132018): global daily satellite rainfall data from ASCAT soil moisture observations. Earth Syst. Sci. Data, 11, 1583\u20131601."]}
DOI: 10.25345/c5r49gd28
title: MassIVE MSV000089252 - Human HEK293T and Pig cardiac top-down proteomes from Bruker maXis II
title: These data, derived from PXD019368 (PMID 33231430) and PXD010825 (PMID 30988469) represent a test of deconvolution algorithms for top-down proteomics experiments on the Bruker maXis II Q-TOF. Three LC-MS/MS experiments are available from pig cardiac tissue in both raw .d directories and in deconvolved msAlign files produced by Bruker DataAnalysis 5.3, TopFD 1.4.13, FLASHDeconv 2.0, and Mascot Distiller 2.8. Eight LC-MS/MS experiments are available from human HEK293T cell lysates, with four produced using Triton and four produced using Tergitol. The inferred precursor charges, precursor masses, and numbers of tandem mass spectra diverge considerably depending upon deconvolution algorithm.
DOI: 10.25345/c5mc8rk8h
title: MassIVE MSV000089253 - Activity-induced Cortical Pyramidal Cell Nascent Proteins
title: Neuronal activity initiates signaling cascades that culminate in diverse outcomes including synaptic and circuit plasticity, modifications in dendritic and axonal structure, and metabolic changes. Recent studies have revealed neuronal cell type specific transcriptional changes induced by activity, however quantitative analysis of cell specific activity-induced dynamics in newly synthesized proteins synthesis in vivo has been complicated by the brain's cellular heterogeneity and relative low abundance of newly synthetized proteins (NSP) within the proteome. Here we combined expression of mutant MetRS in genetically defined cortical pyramidal neurons with tight temporal control of treatment with the non-canonical amino acid, azidonorleucine, ANL, to label proteins that are newly synthesized within a brief period after a single PTZ-induced seizure. By purifying biotinylated peptides and using direct MS/MS detection of the labeled peptides, we selectively quantified activity-induced changes in cortical pyramidal neuron NSPs without contamination from other brain cell populations. We identified changes in 500 NSPs 20 h after seizure, with 20% of the NSPs significantly decreased by seizure. Proteins mediating excitatory and inhibitory synaptic plasticity, including SynGAP1, Pak3, GEPH1, Copine-6 and collybistin, as well as DNA and chromatin remodeling proteins, Rad21, Smarca2, Ddb1 are differentially synthesized in response to activity. Proteins likely to play homeostatic roles in response to activity, such as regulators of proteastasis, intracellular ion control and cytoskeleton remodeling proteins are activity-induced. Conversely, seizure decreased newly synthetized NCAM among others, suggesting seizure induced degradation. Overall, we identified quantitative changes in the activity-induced nascent proteome from genetically defined cortical pyramidal neurons as a strategy to discover downstream mediators of neuronal plasticity and generate hypotheses regarding their function.