text:
@article{RN1,
   author = {Baumeler, D. and Born, S. and Burra, N. and Ptak, R.},
   title = {When Left is One and Right is Double: An Experimental Investigation of Visual Allesthesia after Right Parietal Damage},
   journal = {Vision (Basel)},
   volume = {4},
   number = {1},
   ISSN = {2411-5150 (Electronic)
2411-5150 (Linking)},
   DOI = {10.3390/vision4010016},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/32121533},
   year = {2020},
   type = {Journal Article}
}

@article{RN2,
   author = {Kerzel, D. and Burra, N.},
   title = {Capture by Context Elements, Not Attentional Suppression of Distractors, Explains the PD with Small Search Displays},
   journal = {J Cogn Neurosci},
   volume = {32},
   number = {6},
   pages = {1170-1183},
   ISSN = {1530-8898 (Electronic)
0898-929X (Linking)},
   DOI = {10.1162/jocn_a_01535},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/31967520},
   year = {2020},
   type = {Journal Article}
}

@article{RN3,
   author = {Burra, N. and Kerzel, D.},
   title = {Task Demands Modulate Effects of Threatening Faces on Early Perceptual Encoding},
   journal = {Front Psychol},
   volume = {10},
   pages = {2400},
   ISSN = {1664-1078 (Print)
1664-1078 (Linking)},
   DOI = {10.3389/fpsyg.2019.02400},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/31708839},
   year = {2019},
   type = {Journal Article}
}

@article{RN4,
   author = {Burra, N. and Mares, I. and Senju, A.},
   title = {The influence of top-down modulation on the processing of direct gaze},
   journal = {Wiley Interdiscip Rev Cogn Sci},
   volume = {10},
   number = {5},
   pages = {e1500},
   ISSN = {1939-5086 (Electronic)
1939-5078 (Linking)},
   DOI = {10.1002/wcs.1500},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/30864304},
   year = {2019},
   type = {Journal Article}
}

@article{RN5,
   author = {Burra, N. and Massait, S. and Vrticka, P.},
   title = {Differential impact of trait, social, and attachment anxiety on the stare-in-the-crowd effect},
   journal = {Sci Rep},
   volume = {9},
   number = {1},
   pages = {1797},
   ISSN = {2045-2322 (Electronic)
2045-2322 (Linking)},
   DOI = {10.1038/s41598-019-39342-8},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/30742015},
   year = {2019},
   type = {Journal Article}
}

@article{RN6,
   author = {Burra, N. and Kerzel, D. and Munoz Tord, D. and Grandjean, D. and Ceravolo, L.},
   title = {Early spatial attention deployment toward and away from aggressive voices},
   journal = {Soc Cogn Affect Neurosci},
   volume = {14},
   number = {1},
   pages = {73-80},
   ISSN = {1749-5024 (Electronic)
1749-5016 (Linking)},
   DOI = {10.1093/scan/nsy100},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/30418635},
   year = {2019},
   type = {Journal Article}
}

@article{RN7,
   author = {Bagnoud, J. and Burra, N. and Castel, C. and Oakhill, J. and Thevenot, C.},
   title = {Arithmetic word problems describing discrete quantities: E.E.G evidence for the construction of a situation model},
   journal = {Acta Psychol (Amst)},
   volume = {190},
   pages = {116-121},
   ISSN = {1873-6297 (Electronic)
0001-6918 (Linking)},
   DOI = {10.1016/j.actpsy.2018.07.008},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/30071358},
   year = {2018},
   type = {Journal Article}
}

@article{RN8,
   author = {Cheval, B. and Tipura, E. and Burra, N. and Frossard, J. and Chanal, J. and Orsholits, D. and Radel, R. and Boisgontier, M. P.},
   title = {Avoiding sedentary behaviors requires more cortical resources than avoiding physical activity: An EEG study},
   journal = {Neuropsychologia},
   volume = {119},
   pages = {68-80},
   ISSN = {1873-3514 (Electronic)
0028-3932 (Linking)},
   DOI = {10.1016/j.neuropsychologia.2018.07.029},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/30056055},
   year = {2018},
   type = {Journal Article}
}

@article{RN9,
   author = {Burra, N. and Framorando, D. and Pegna, A. J.},
   title = {Early and late cortical responses to directly gazing faces are task dependent},
   journal = {Cogn Affect Behav Neurosci},
   volume = {18},
   number = {4},
   pages = {796-809},
   ISSN = {1531-135X (Electronic)
1530-7026 (Linking)},
   DOI = {10.3758/s13415-018-0605-5},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/29736681},
   year = {2018},
   type = {Journal Article}
}

@article{RN10,
   author = {Framorando, D. and Burra, N. and Bapst, M. and Pegna, A. J.},
   title = {ERP responses greater for faces in the temporal compared to the nasal visual field},
   journal = {Neurosci Lett},
   volume = {665},
   pages = {7-12},
   ISSN = {1872-7972 (Electronic)
0304-3940 (Linking)},
   DOI = {10.1016/j.neulet.2017.11.031},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/29155351},
   year = {2018},
   type = {Journal Article}
}

@article{RN11,
   author = {Burra, N. and Hervais-Adelman, A. and Celeghin, A. and de Gelder, B. and Pegna, A. J.},
   title = {Affective blindsight relies on low spatial frequencies},
   journal = {Neuropsychologia},
   volume = {128},
   pages = {44-49},
   ISSN = {1873-3514 (Electronic)
0028-3932 (Linking)},
   DOI = {10.1016/j.neuropsychologia.2017.10.009},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/28993236},
   year = {2019},
   type = {Journal Article}
}

@article{RN12,
   author = {Burra, N. and Baker, S. and George, N.},
   title = {Processing of gaze direction within the N170/M170 time window: A combined EEG/MEG study},
   journal = {Neuropsychologia},
   volume = {100},
   pages = {207-219},
   ISSN = {1873-3514 (Electronic)
0028-3932 (Linking)},
   DOI = {10.1016/j.neuropsychologia.2017.04.028},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/28450203},
   year = {2017},
   type = {Journal Article}
}

@article{RN13,
   author = {Burra, N. and Kerzel, D. and Ramon, M.},
   title = {Gaze-cueing requires intact face processing - Insights from acquired prosopagnosia},
   journal = {Brain Cogn},
   volume = {113},
   pages = {125-132},
   ISSN = {1090-2147 (Electronic)
0278-2626 (Linking)},
   DOI = {10.1016/j.bandc.2017.01.008},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/28193545},
   year = {2017},
   type = {Journal Article}
}

@article{RN14,
   author = {Burra, N. and Coll, S. Y. and Barras, C. and Kerzel, D.},
   title = {Electrophysiological evidence for attentional capture by irrelevant angry facial expressions: Naturalistic faces},
   journal = {Neurosci Lett},
   volume = {637},
   pages = {44-49},
   ISSN = {1872-7972 (Electronic)
0304-3940 (Linking)},
   DOI = {10.1016/j.neulet.2016.11.055},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/27899310},
   year = {2017},
   type = {Journal Article}
}

@article{RN15,
   author = {Burra, N. and Kerzel, D. and George, N.},
   title = {Early Left Parietal Activity Elicited by Direct Gaze: A High-Density EEG Study},
   journal = {PLoS One},
   volume = {11},
   number = {11},
   pages = {e0166430},
   ISSN = {1932-6203 (Electronic)
1932-6203 (Linking)},
   DOI = {10.1371/journal.pone.0166430},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/27880776},
   year = {2016},
   type = {Journal Article}
}

@article{RN16,
   author = {Burra, N. and Barras, C. and Coll, S. Y. and Kerzel, D.},
   title = {Electrophysiological evidence for attentional capture by irrelevant angry facial expressions},
   journal = {Biol Psychol},
   volume = {120},
   pages = {69-80},
   ISSN = {1873-6246 (Electronic)
0301-0511 (Linking)},
   DOI = {10.1016/j.biopsycho.2016.08.008},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/27568328},
   year = {2016},
   type = {Journal Article}
}

@article{RN17,
   author = {Burra, N. and Kerzel, D. and de Gelder, B. and Pegna, A. J.},
   title = {Lack of automatic attentional orienting by gaze cues following a bilateral loss of visual cortex},
   journal = {Neuropsychologia},
   volume = {58},
   pages = {75-80},
   ISSN = {1873-3514 (Electronic)
0028-3932 (Linking)},
   DOI = {10.1016/j.neuropsychologia.2014.04.003},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/24732381},
   year = {2014},
   type = {Journal Article}
}

@article{RN18,
   author = {Burra, N. and Kerzel, D.},
   title = {The distractor positivity (Pd) signals lowering of attentional priority: evidence from event-related potentials and individual differences},
   journal = {Psychophysiology},
   volume = {51},
   number = {7},
   pages = {685-96},
   ISSN = {1540-5958 (Electronic)
0048-5772 (Linking)},
   DOI = {10.1111/psyp.12215},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/24707976},
   year = {2014},
   type = {Journal Article}
}

@article{RN19,
   author = {Burra, N. and Hervais-Adelman, A. and Kerzel, D. and Tamietto, M. and de Gelder, B. and Pegna, A. J.},
   title = {Amygdala activation for eye contact despite complete cortical blindness},
   journal = {J Neurosci},
   volume = {33},
   number = {25},
   pages = {10483-9},
   ISSN = {1529-2401 (Electronic)
0270-6474 (Linking)},
   DOI = {10.1523/JNEUROSCI.3994-12.2013},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/23785160},
   year = {2013},
   type = {Journal Article}
}

@article{RN20,
   author = {Burra, N. and Kerzel, D.},
   title = {Attentional capture during visual search is attenuated by target predictability: evidence from the N2pc, Pd, and topographic segmentation},
   journal = {Psychophysiology},
   volume = {50},
   number = {5},
   pages = {422-30},
   ISSN = {1540-5958 (Electronic)
0048-5772 (Linking)},
   DOI = {10.1111/psyp.12019},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/23418888},
   year = {2013},
   type = {Journal Article}
}

@article{RN21,
   author = {Kerzel, D. and Schonhammer, J. and Burra, N. and Born, S. and Souto, D.},
   title = {Saliency changes appearance},
   journal = {PLoS One},
   volume = {6},
   number = {12},
   pages = {e28292},
   ISSN = {1932-6203 (Electronic)
1932-6203 (Linking)},
   DOI = {10.1371/journal.pone.0028292},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/22162760},
   year = {2011},
   type = {Journal Article}
}

@article{RN22,
   author = {Righart, R. and Burra, N. and Vuilleumier, P.},
   title = {Face perception in the mind's eye},
   journal = {Brain Topogr},
   volume = {24},
   number = {1},
   pages = {9-18},
   ISSN = {1573-6792 (Electronic)
0896-0267 (Linking)},
   DOI = {10.1007/s10548-010-0164-8},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/21052814},
   year = {2011},
   type = {Journal Article}
}


text:
@article{de_luca_sturmian_2015,
 author = {de Luca, Aldo and De Luca, Alessandro},
 doi = {10.1016/j.tcs.2015.02.043},
 issn = {0304-3975},
 journal = {Theoretical Computer Science},
 keywords = {Calkin-Wilf tree, Christoffel words, Stern sequence, Sturmian words},
 month = {may},
 pages = {26--44},
 title = {Sturmian words and the Stern sequence},
 volume = {581},
 year = {2015}
}

license: MIT

repository: aledeluca/academic-kickstart

license_type: permissive

repo_name: aledeluca/academic-kickstart

branch_name: refs/heads/master

extension: bib


text:
@ARTICLE{Waman2019-ki,
  title     = "Mycobacterial genomics and structural bioinformatics:
               opportunities and challenges in drug discovery",
  author    = "Waman, Vaishali P and Vedithi, Sundeep Chaitanya and Thomas,
               Sherine E and Bannerman, Bridget P and Munir, Asma and Skwark,
               Marcin J and Malhotra, Sony and Blundell, Tom L",
  abstract  = "Of the more than 190 distinct species of Mycobacterium genus,
               many are economically and clinically important pathogens of
               humans or animals. Among those mycobacteria that infect humans,
               three species namely Mycobacterium tuberculosis (causative agent
               ...",
  journal   = "Emerg. Microbes Infect.",
  publisher = "Taylor \& Francis",
  volume    =  8,
  number    =  1,
  pages     = "109",
  year      =  2019,
  language  = "en"
}

@ARTICLE{Golkov2017-za,
  title         = "{3D} Deep Learning for Biological Function Prediction from
                   Physical Fields",
  author        = "Golkov, Vladimir and Skwark, Marcin J and Mirchev, Atanas
                   and Dikov, Georgi and Geanes, Alexander R and Mendenhall,
                   Jeffrey and Meiler, Jens and Cremers, Daniel",
  abstract      = "Predicting the biological function of molecules, be it
                   proteins or drug-like compounds, from their atomic structure
                   is an important and long-standing problem. Function is
                   dictated by structure, since it is by spatial interactions
                   that molecules interact with each other, both in terms of
                   steric complementarity, as well as intermolecular forces.
                   Thus, the electron density field and electrostatic potential
                   field of a molecule contain the ``raw fingerprint'' of how
                   this molecule can fit to binding partners. In this paper, we
                   show that deep learning can predict biological function of
                   molecules directly from their raw 3D approximated electron
                   density and electrostatic potential fields. Protein function
                   based on EC numbers is predicted from the approximated
                   electron density field. In another experiment, the activity
                   of small molecules is predicted with quality comparable to
                   state-of-the-art descriptor-based methods. We propose
                   several alternative computational models for the GPU with
                   different memory and runtime requirements for different
                   sizes of molecules and of databases. We also propose
                   application-specific multi-channel data representations.
                   With future improvements of training datasets and neural
                   network settings in combination with complementary
                   information sources (sequence, genomic context, expression
                   level), deep learning can be expected to show its
                   generalization power and revolutionize the field of
                   molecular function prediction.",
  month         =  apr,
  year          =  2017,
  archivePrefix = "arXiv",
  primaryClass  = "q-bio.BM",
  eprint        = "1704.04039"
}

@ARTICLE{Viklund2008-gs,
  title    = "{{{S}{P}{O}{C}{T}{O}{P}{U}{S}}: a combined predictor of signal
              peptides and membrane protein topology}",
  author   = "Viklund, H{\aa}kan and Bernsel, Andreas and Skwark, Marcin and
              Elofsson, Arne",
  abstract = "SUMMARY: SPOCTOPUS is a method for combined prediction of signal
              peptides and membrane protein topology, suitable for genome-scale
              studies. Its objective is to minimize false predictions of
              transmembrane regions as signal peptides and vice versa. We
              provide a description of the SPOCTOPUS algorithm together with a
              performance evaluation where SPOCTOPUS compares favorably with
              state-of-the-art methods for signal peptide and topology
              predictions. AVAILABILITY: SPOCTOPUS is available as a web server
              and both the source code and benchmark data are available for
              download at http://octopus.cbr.su.se/",
  journal  = "Bioinformatics",
  volume   =  24,
  number   =  24,
  pages    = "2928--2929",
  month    =  dec,
  year     =  2008,
  language = "en"
}

@ARTICLE{Skwark2014-yd,
  title     = "{Improved Contact Predictions Using the Recognition of Protein
               Like Contact Patterns}",
  author    = "Skwark, Marcin J and Raimondi, Daniele and Michel, Mirco and
               Elofsson, Arne",
  abstract  = "Author Summary Here, we introduce a novel protein contact
               prediction method PconsC2 that, to the best of our knowledge,
               outperforms earlier methods. PconsC2 is based on our earlier
               method, PconsC, as it utilizes the same set of contact
               predictions from plmDCA and PSICOV. However, in contrast to
               PconsC, where each residue pair is analysed independently, the
               initial predictions are analysed in context of neighbouring
               residue pairs using a deep learning approach, inspired by
               earlier work. We find that for each layer the deep learning
               procedure improves the predictions. At the end, after five
               layers of deep learning and inclusion of a few extra features
               provides the best performance. An improvement can be seen for
               all types of proteins, independent on length, number of
               homologous sequences and structural class. However, the
               improvement is largest for $\beta$-sheet containing proteins.
               Most importantly the improvement brings for the first time
               sufficiently accurate predictions to some protein families with
               less than 1000 homologous sequences. PconsC2 outperforms as well
               state of the art machine learning based predictors for protein
               families larger than 100 effective sequences. PconsC2 is
               licensed under the GNU General Public License v3 and freely
               available from http://c2.pcons.net/.",
  journal   = "PLoS Comput. Biol.",
  publisher = "Public Library of Science",
  volume    =  10,
  number    =  11,
  pages     = "e1003889",
  month     =  nov,
  year      =  2014,
  language  = "en"
}

@ARTICLE{Michel2014-rp,
  title    = "{PconsFold}: improved contact predictions improve protein models",
  author   = "Michel, Mirco and Hayat, Sikander and Skwark, Marcin J and
              Sander, Chris and Marks, Debora S and Elofsson, Arne",
  abstract = "MOTIVATION: Recently it has been shown that the quality of
              protein contact prediction from evolutionary information can be
              improved significantly if direct and indirect information is
              separated. Given sufficiently large protein families, the contact
              predictions contain sufficient information to predict the
              structure of many protein families. However, since the first
              studies contact prediction methods have improved. Here, we ask
              how much the final models are improved if improved contact
              predictions are used. RESULTS: In a small benchmark of 15
              proteins, we show that the TM-scores of top-ranked models are
              improved by on average 33\% using PconsFold compared with the
              original version of EVfold. In a larger benchmark, we find that
              the quality is improved with 15-30\% when using PconsC in
              comparison with earlier contact prediction methods. Further,
              using Rosetta instead of CNS does not significantly improve
              global model accuracy, but the chemistry of models generated with
              Rosetta is improved. AVAILABILITY: PconsFold is a fully automated
              pipeline for ab initio protein structure prediction based on
              evolutionary information. PconsFold is based on PconsC contact
              prediction and uses the Rosetta folding protocol. Due to its
              modularity, the contact prediction tool can be easily exchanged.
              The source code of PconsFold is available on GitHub at {\v
              e}+https://www.github.com/ElofssonLab/pcons-fold+ under the MIT
              license. PconsC is available from {\v e}+http://c.pcons.net/.+
              SUPPLEMENTARY INFORMATION: Supplementary data are available at
              Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   = "in press",
  number   =  17,
  pages    = "i482--8",
  month    =  sep,
  year     =  2014,
  language = "en"
}

@ARTICLE{Feinauer2014-pr,
  title    = "{Improving Contact Prediction along Three Dimensions}",
  author   = "Feinauer, Christoph and Skwark, Marcin J and Pagnani, Andrea and
              Aurell, Erik",
  abstract = "Correlation patterns in multiple sequence alignments of
              homologous proteins can be exploited to infer information on the
              three-dimensional structure of their members. The typical
              pipeline to address this task, which we in this paper refer to as
              the three dimensions of contact prediction, is to: (i) filter and
              align the raw sequence data representing the evolutionarily
              related proteins; (ii) choose a predictive model to describe a
              sequence alignment; (iii) infer the model parameters and
              interpret them in terms of structural properties, such as an
              accurate contact map. We show here that all three dimensions are
              important for overall prediction success. In particular, we show
              that it is possible to improve significantly along the second
              dimension by going beyond the pair-wise Potts models from
              statistical physics, which have hitherto been the focus of the
              field. These (simple) extensions are motivated by multiple
              sequence alignments often containing long stretches of gaps
              which, as a data feature, would be rather untypical for
              independent samples drawn from a Potts model. Using a large test
              set of proteins we show that the combined improvements along the
              three dimensions are as large as any reported to date.",
  journal  = "PLoS Comput. Biol.",
  volume   =  10,
  number   =  10,
  pages    = "e1003847",
  month    =  oct,
  year     =  2014,
  language = "en"
}

@ARTICLE{Larsson2009-er,
  title    = "{{A}ssessment of global and local model quality in
              {{C}{A}{S}{P}8} using {P}cons and {P}ro{Q}}",
  author   = "Larsson, Per and Skwark, Marcin J and Wallner, Bj{\"o}rn and
              Elofsson, Arne",
  abstract = "Model Quality Assessment Programs (MQAPs) are programs developed
              to rank protein models. These methods can be trained to predict
              the overall global quality of a model or what local regions in a
              model that are likely to be incorrect. In CASP8, we participated
              with two predictors that predict both global and local quality
              using either consensus information, Pcons, or purely structural
              information, ProQ. Consistently with results in previous CASPs,
              the best performance in CASP8 was obtained using the Pcons
              method. Furthermore, the results show that the modification
              introduced into Pcons for CASP8 improved the predictions against
              GDT\_TS and now a correlation coefficient above 0.9 is achieved,
              whereas the correlation for ProQ is about 0.7. The correlation is
              better for the easier than for the harder targets, but it is not
              below 0.5 for a single target and below 0.7 only for three
              targets. The correlation coefficient for the best local quality
              MQAP is 0.68 showing that there is still clear room for
              improvement within this area. We also detect that Pcons still is
              not always able to identify the best model. However, we show that
              using a linear combination of Pcons and ProQ it is possible to
              select models that are better than the models from the best
              single server. In particular, the average quality over the hard
              targets increases by about 6\% compared with using Pcons alone.",
  journal  = "Proteins",
  volume   = "77 Suppl 9",
  pages    = "167--172",
  year     =  2009,
  language = "en"
}

@ARTICLE{Skwark2013-fr,
  title    = "{{{P}cons{C}}: {C}ombination of direct information methods and
              alignments improves contact prediction}",
  author   = "Skwark, Marcin J and Abdel-Rehim, Abbi and Elofsson, Arne",
  abstract = "SUMMARY: Recently, several new contact prediction methods have
              been published. They use (i) large sets of multiple aligned
              sequences and (ii) assume that correlations between columns in
              these alignments can be the results of indirect interaction.
              These methods are clearly superior to earlier methods when it
              comes to predicting contacts in proteins. Here, we demonstrate
              that combining predictions from two prediction methods, PSICOV
              and plmDCA, and two alignment methods, HHblits and jackhmmer at
              four different e-value cut-offs, provides a relative improvement
              of 20\% in comparison with the best single method, exceeding 70\%
              correct predictions for one contact prediction per residue.
              AVAILABILITY: The source code for PconsC along with supplementary
              data is freely available at http://c.pcons.net/ CONTACT:
              arne@bioinfo.se SUPPLEMENTARY INFORMATION: Supplementary data are
              available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  29,
  number   =  14,
  pages    = "1815--1816",
  month    =  jul,
  year     =  2013,
  language = "en"
}

@ARTICLE{Skwark2017-hx,
  title     = "Interacting networks of resistance, virulence and core machinery
               genes identified by genome-wide epistasis analysis",
  author    = "Skwark, Marcin J and Croucher, Nicholas J and Puranen, Santeri
               and Chewapreecha, Claire and Pesonen, Maiju and Xu, Ying Ying
               and Turner, Paul and Harris, Simon R and Beres, Stephen B and
               Musser, James M and Parkhill, Julian and Bentley, Stephen D and
               Aurell, Erik and Corander, Jukka",
  abstract  = "Recent advances in the scale and diversity of population genomic
               datasets for bacteria now provide the potential for genome-wide
               patterns of co-evolution to be studied at the resolution of
               individual bases. Here we describe a new statistical method,
               genomeDCA, which uses recent advances in computational
               structural biology to identify the polymorphic loci under the
               strongest co-evolutionary pressures. We apply genomeDCA to two
               large population data sets representing the major human
               pathogens Streptococcus pneumoniae (pneumococcus) and
               Streptococcus pyogenes (group A Streptococcus). For pneumococcus
               we identified 5,199 putative epistatic interactions between
               1,936 sites. Over three-quarters of the links were between sites
               within the pbp2x, pbp1a and pbp2b genes, the sequences of which
               are critical in determining non-susceptibility to beta-lactam
               antibiotics. A network-based analysis found these genes were
               also coupled to that encoding dihydrofolate reductase, changes
               to which underlie trimethoprim resistance. Distinct from these
               antibiotic resistance genes, a large network component of 384
               protein coding sequences encompassed many genes critical in
               basic cellular functions, while another distinct component
               included genes associated with virulence. The group A
               Streptococcus (GAS) data set population represents a clonal
               population with relatively little genetic variation and a high
               level of linkage disequilibrium across the genome. Despite this,
               we were able to pinpoint two RNA pseudouridine synthases, which
               were each strongly linked to a separate set of loci across the
               chromosome, representing biologically plausible targets of
               co-selection. The population genomic analysis method applied
               here identifies statistically significantly co-evolving locus
               pairs, potentially arising from fitness selection
               interdependence reflecting underlying protein-protein
               interactions, or genes whose product activities contribute to
               the same phenotype. This discovery approach greatly enhances the
               future potential of epistasis analysis for systems biology, and
               can complement genome-wide association studies as a means of
               formulating hypotheses for targeted experimental work.",
  journal   = "PLoS Genet.",
  publisher = "Public Library of Science",
  volume    =  13,
  number    =  2,
  pages     = "e1006508",
  month     =  feb,
  year      =  2017,
  language  = "en"
}

@ARTICLE{Skwark2013-fm,
  title    = "{{PconsD}: ultra rapid, accurate model quality assessment for
              protein structure prediction}",
  author   = "Skwark, Marcin J and Elofsson, Arne",
  abstract = "SUMMARY: Clustering methods are often needed for accurately
              assessing the quality of modeled protein structures. Recent blind
              evaluation of quality assessment methods in CASP10 showed that
              there is little difference between many different methods as far
              as ranking models and selecting best model are concerned. When
              comparing many models, the computational cost of the model
              comparison can become significant. Here, we present PconsD, a
              fast, stream-computing method for distance-driven model quality
              assessment that runs on consumer hardware. PconsD is at least one
              order of magnitude faster than other methods of comparable
              accuracy. AVAILABILITY: The source code for PconsD is freely
              available at http://d.pcons.net/. Supplementary benchmarking data
              are also available there. CONTACT: arne@bioinfo.se SUPPLEMENTARY
              INFORMATION: Supplementary data are available at Bioinformatics
              online.",
  journal  = "Bioinformatics",
  volume   =  29,
  number   =  14,
  pages    = "1817--1818",
  month    =  jul,
  year     =  2013,
  keywords = "Cluster Analysis,Computational Biology,Models- Molecular,Protein
              Conformation,Proteins,Software",
  language = "eng"
}

@ARTICLE{Waman2019-qn,
  title     = "Mycobacterial genomics and structural bioinformatics:
               opportunities and challenges in drug discovery",
  author    = "Waman, Vaishali P and Vedithi, Sundeep Chaitanya and Thomas,
               Sherine E and Bannerman, Bridget P and Munir, Asma and Skwark,
               Marcin J and Malhotra, Sony and Blundell, Tom L",
  abstract  = "ABSTRACTOf the more than 190 distinct species of Mycobacterium
               genus, many are economically and clinically important pathogens
               of humans or animals. Among those mycobacteria that infect
               humans, three species namely Mycobacterium tuberculosis
               (causative agent of tuberculosis), Mycobacterium leprae
               (causative agent of leprosy) and Mycobacterium abscessus
               (causative agent of chronic pulmonary infections) pose concern
               to global public health. Although antibiotics have been
               successfully developed to combat each of these, the emergence of
               drug-resistant strains is an increasing challenge for treatment
               and drug discovery. Here we describe the impact of the rapid
               expansion of genome sequencing and genome/pathway annotations
               that have greatly improved the progress of structure-guided drug
               discovery. We focus on the applications of comparative genomics,
               metabolomics, evolutionary bioinformatics and structural
               proteomics to identify potential drug targets. The opportunities
               and challenges for the design of drugs for M. tuberculosis, M.
               leprae and M. abscessus to combat resistance are discussed.",
  journal   = "Emerg. Microbes Infect.",
  publisher = "Taylor \& Francis",
  volume    =  8,
  number    =  1,
  pages     = "109--118",
  month     =  jan,
  year      =  2019
}

@ARTICLE{Ge2014-ko,
  title    = "Membrane remodeling capacity of a vesicle-inducing
              glycosyltransferase",
  author   = "Ge, Changrong and G{\'o}mez-Llobregat, Jordi and Skwark, Marcin J
              and Ruysschaert, Jean-Marie and Wieslander, Ake and Lind{\'e}n,
              Martin",
  abstract = "Intracellular vesicles are abundant in eukaryotic cells but
              absent in the Gram-negative bacterium Escherichia coli. However,
              strong overexpression of a monotopic glycolipid-synthesizing
              enzyme, monoglucosyldiacylglycerol synthase from Acholeplasma
              laidlawii (alMGS), leads to massive formation of vesicles in the
              cytoplasm of E. coli. More importantly, alMGS provides a model
              system for the regulation of membrane properties by
              membrane-bound enzymes, which is critical for maintaining
              cellular integrity. Both phenomena depend on how alMGS binds to
              cell membranes, which is not well understood. Here, we carry out
              a comprehensive investigation of the membrane binding of alMGS by
              combining bioinformatics methods with extensive biochemical
              studies, structural modeling and molecular dynamics simulations.
              We find that alMGS binds to the membrane in a fairly upright
              manner, mainly by residues in the N-terminal domain, and in a way
              that induces local enrichment of anionic lipids and a local
              curvature deformation. Furthermore, several alMGS variants
              resulting from substitution of residues in the membrane anchoring
              segment are still able to generate vesicles, regardless of
              enzymatic activity. These results clarify earlier theories about
              the driving forces for vesicle formation, and shed new light on
              the membrane binding properties and enzymatic mechanism of alMGS
              and related monotopic GT-B fold glycosyltransferases.",
  journal  = "FEBS J.",
  volume   =  281,
  number   =  16,
  pages    = "3667--3684",
  month    =  aug,
  year     =  2014,
  keywords = "glycosyltransferase; lipid-protein interactions; membrane
              deformation; molecular dynamics; monotopic protein",
  language = "en"
}

@ARTICLE{Larsson2011-qe,
  title    = "{Improved predictions by Pcons.net using multiple templates}",
  author   = "Larsson, Per and Skwark, Marcin J and Wallner, Bj{\"o}rn and
              Elofsson, Arne",
  abstract = "UNLABELLED: Multiple templates can often be used to build more
              accurate homology models than models built from a single
              template. Here we introduce PconsM, an automated protocol that
              uses multiple templates to build protein models. PconsM has been
              among the top-performing methods in the recent CASP experiments
              and consistently perform better than the single template models
              used in Pcons.net. In particular for the easier targets with many
              alternative templates with a high degree of sequence identity,
              quality is readily improved with a few percentages over the
              highest ranked model built on a single template. PconsM is
              available as an additional pipeline within the Pcons.net protein
              structure prediction server. AVAILABILITY AND IMPLEMENTATION:
              PconsM is freely available from http://pcons.net/.",
  journal  = "Bioinformatics",
  volume   =  27,
  number   =  3,
  pages    = "426--427",
  month    =  feb,
  year     =  2011,
  keywords = "Computational Biology,Models- Molecular,Proteins,Software",
  language = "eng"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Zhang2017-om,
  title     = "Epistasis Analysis Goes {Genome-Wide}",
  author    = "Zhang, Jianzhi",
  abstract  = "… 11. Skwark MJ, Croucher NJ, Puranen S, Chewapreecha C, Pesonen
               M, et al. (2017) Interacting networks of resistance, virulence
               and core machinery genes identified by genome-wide epistasis
               analysis. PLoS Genet in press …",
  journal   = "PLoS Genet.",
  publisher = "journals.plos.org",
  volume    =  13,
  number    =  2,
  pages     = "e1006558",
  month     =  feb,
  year      =  2017,
  language  = "en"
}

@INCOLLECTION{Van_Dijl2017-wm,
  title     = "Ins and Outs of the Bacillus subtilis Membrane Proteome",
  booktitle = "Bacillus : Cellular and Molecular Biology (Third edition)",
  author    = "van Dijl, Jan Maarten and Dreisbach, Annette and Skwark, Marcin
               J and Sibbald, Mark J J B and Tjalsma, Harold and Zweers,
               Jessica C and Buist, Girbe",
  publisher = "Caister Academic Press",
  year      =  2017
}

@ARTICLE{Bendz2013-mx,
  title    = "Membrane protein shaving with thermolysin can be used to evaluate
              topology predictors",
  author   = "Bendz, Maria and Skwark, Marcin and Nilsson, Daniel and Granholm,
              Viktor and Cristobal, Susana and K{\"a}ll, Lukas and Elofsson,
              Arne",
  abstract = "Topology analysis of membrane proteins can be obtained by
              enzymatic shaving in combination with MS identification of
              peptides. Ideally, such analysis could provide quite detailed
              information about the membrane spanning regions. Here, we examine
              the ability of some shaving enzymes to provide large-scale
              analysis of membrane proteome topologies. To compare different
              shaving enzymes, we first analyzed the detected peptides from two
              over-expressed proteins. Second, we analyzed the peptides from
              non-over-expressed Escherichia coli membrane proteins with known
              structure to evaluate the shaving methods. Finally, the
              identified peptides were used to test the accuracy of a number of
              topology predictors. At the end we suggest that the usage of
              thermolysin, an enzyme working at the natural pH of the cell for
              membrane shaving, is superior because: (i) we detect a similar
              number of peptides and proteins using thermolysin and trypsin;
              (ii) thermolysin shaving can be run at a natural pH and (iii) the
              incubation time is quite short. (iv) Fewer detected peptides from
              thermolysin shaving originate from the transmembrane regions.
              Using thermolysin shaving we can also provide a clear separation
              between the best and the less accurate topology predictors,
              indicating that using data from shaving can provide valuable
              information when developing new topology predictors.",
  journal  = "Proteomics",
  volume   =  13,
  number   =  9,
  pages    = "1467--1480",
  month    =  may,
  year     =  2013,
  language = "en"
}

@ARTICLE{Teixeira2017-yi,
  title    = "Membrane protein contact and structure prediction using
              co-evolution in conjunction with machine learning",
  author   = "Teixeira, Pedro L and Mendenhall, Jeff L and Heinze, Sten and
              Weiner, Brian and Skwark, Marcin J and Meiler, Jens",
  abstract = "De novo membrane protein structure prediction is limited to small
              proteins due to the conformational search space quickly expanding
              with length. Long-range contacts (24+ amino acid
              separation)-residue positions distant in sequence, but in close
              proximity in the structure, are arguably the most effective way
              to restrict this conformational space. Inverse methods for
              co-evolutionary analysis predict a global set of position-pair
              couplings that best explain the observed amino acid
              co-occurrences, thus distinguishing between evolutionarily
              explained co-variances and these arising from spurious transitive
              effects. Here, we show that applying machine learning approaches
              and custom descriptors improves evolutionary contact prediction
              accuracy, resulting in improvement of average precision by 6
              percentage points for the top 1L non-local contacts. Further, we
              demonstrate that predicted contacts improve protein folding with
              BCL::Fold. The mean RMSD100 metric for the top 10 models folded
              was reduced by an average of 2 {\AA} for a benchmark of 25
              membrane proteins.",
  journal  = "PLoS One",
  volume   =  12,
  number   =  5,
  pages    = "e0177866",
  month    =  may,
  year     =  2017,
  language = "en"
}

@ARTICLE{Michel2017-wj,
  title    = "Predicting accurate contacts in thousands of Pfam domain families
              using {PconsC3}",
  author   = "Michel, Mirco and Skwark, Marcin J and Men{\'e}ndez Hurtado,
              David and Ekeberg, Magnus and Elofsson, Arne",
  abstract = "Motivation: A few years ago it was shown that by using a maximum
              entropy approach to describe couplings between columns in a
              multiple sequence alignment it is possible to significantly
              increase the accuracy of residue contact predictions. For very
              large protein families with more than 1000 effective sequences
              the accuracy is sufficient to produce accurate models of proteins
              as well as complexes. Today, for about half of all Pfam domain
              families no structure is known, but unfortunately most of these
              families have at most a few hundred members, i.e. are too small
              for such contact prediction methods. Results: To extend accurate
              contact predictions to the thousands of smaller protein families
              we present PconsC3, a fast and improved method for protein
              contact predictions that can be used for families with even 100
              effective sequence members. PconsC3 outperforms direct coupling
              analysis (DCA) methods significantly independent on family size,
              secondary structure content, contact range, or the number of
              selected contacts. Availability and implementation: PconsC3 is
              available as a web server and downloadable version at
              http://c3.pcons.net . The downloadable version is free for all to
              use and licensed under the GNU General Public License, version 2.
              At this site contact predictions for most Pfam families are also
              available. We do estimate that more than 4000 contact maps for
              Pfam families of unknown structure have more than 50\% of the
              top-ranked contacts predicted correctly. Contact:
              arne@bioinfo.se. Supplementary information: Supplementary data
              are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  33,
  number   =  18,
  pages    = "2859--2866",
  month    =  sep,
  year     =  2017,
  language = "en"
}

@ARTICLE{Borza2017-aq,
  title    = "Discoidin domain receptor 1 kinase activity is required for
              regulating collagen {IV} synthesis",
  author   = "Borza, Corina M and Su, Yan and Tran, Truc-Linh and Yu, Ling and
              Steyns, Nick and Temple, Kayla J and Skwark, Marcin J and Meiler,
              Jens and Lindsley, Craig W and Hicks, Brennan R and Leitinger,
              Birgit and Zent, Roy and Pozzi, Ambra",
  abstract = "Discoidin domain receptor 1 (DDR1) is a receptor tyrosine kinase
              that binds to and is activated by collagens. DDR1 expression
              increases following kidney injury and accumulating evidence
              suggests that it contributes to the progression of injury. To
              this end, deletion of DDR1 is beneficial in ameliorating kidney
              injury induced by angiotensin infusion, unilateral ureteral
              obstruction, or nephrotoxic nephritis. Most of the beneficial
              effects observed in the DDR1-null mice are attributed to reduced
              inflammatory cell infiltration to the site of injury, suggesting
              that DDR1 plays a pro-inflammatory effect. The goal of this study
              was to determine whether, in addition to its pro-inflammatory
              effect, DDR1 plays a deleterious effect in kidney injury by
              directly regulating extracellular matrix production. We show that
              DDR1-null mice have reduced deposition of glomerular collagens I
              and IV as well as decreased proteinuria following the partial
              renal ablation model of kidney injury. Using mesangial cells
              isolated from DDR1-null mice, we show that these cells produce
              significantly less collagen compared to DDR1-null cells
              reconstituted with wild type DDR1. Moreover, mutagenesis analysis
              revealed that mutations in the collagen binding site or in the
              kinase domain significantly reduce DDR1-mediated collagen
              production. Finally, we provide evidence that blocking DDR1
              kinase activity with an ATP-competitive small molecule inhibitor
              reduces collagen production. In conclusion, our studies indicate
              that the kinase activity of DDR1 plays a key role in DDR1-induced
              collagen synthesis and suggest that blocking collagen-mediated
              DDR1 activation may be beneficial in fibrotic diseases.",
  journal  = "Matrix Biol.",
  volume   = "57-58",
  pages    = "258--271",
  month    =  jan,
  year     =  2017,
  keywords = "Collagen receptors; Fibrosis; Kidney injury; Mesangial cells;
              Partial renal ablation",
  language = "en"
}

repository: mskwark/academic-kickstart

repo_name: mskwark/academic-kickstart


text:
@Article{BCRL79,
  Title                    = { {$O(n^{2.7799})$} complexity for {$n \times n$} approximate matrix multiplication},
  Author                   = {D. Bini and M. Capovani and F. Romani and G. Lotti},
  Journal                  = {{Information Processing Letters}},
  Year                     = {1979},
  Number                   = {5},
  Pages                    = {234--235},
  Volume                   = {8},

  Doi                      = {DOI: 10.1016/0020-0190(79)90113-3},
  File                     = {:BCRL79.pdf:PDF},
  ISSN                     = {0020-0190},
  Keywords                 = {Analysis of algorithms; computational complexity; numerical mathematics},
  Url                      = {http://www.sciencedirect.com/science/article/B6V0F-45FCWS8-2J/2/5fe4a4c2f661f7d574409b8999cb3914}
}


text:
DESCRIPTION = "GNOME NetworkManager port"
SECTION = "network/misc"
LICENSE = "GPL"
HOMEPAGE = "http://www.gnome.org"
PRIORITY = "optional"
DEPENDS = "libnl dbus dbus-glib libhal-nm libgpewidget gnome-keyring gconf-dbus wireless-tools libglade"
RDEPENDS = "wpa-supplicant iproute2 dhcdbd gnome-keyring hicolor-icon-theme"

PR = "r5"

SRC_URI="http://www.handhelds.org/~mmp/files/NetworkManager-${PV}-gpe.tar.gz \
	file://dbus-api-fix.patch;patch=1 \
	file://dbus-1.0-fix.patch;patch=1 \
        file://99_networkmanager \
	file://NetworkManager \
	file://nm-applet.desktop"

EXTRA_OECONF = " \
		--without-gnome \
		--with-gpe \
		--with-distro=debian \
		--without-gcrypt \
 		--with-wpa_supplicant=/usr/sbin/wpa_supplicant \
		--disable-hal  \
		--with-dhcdbd=/sbin/dhcdbd"

S = "${WORKDIR}/NetworkManager-${PV}"

inherit autotools

do_staging () {
	autotools_stage_includes
	oe_libinstall -C libnm-util libnm-util ${STAGING_LIBDIR}
	oe_libinstall gnome/libnm_glib libnm_glib ${STAGING_LIBDIR}
}

do_install () {
	oe_libinstall -C libnm-util libnm-util ${D}/usr/lib
	oe_libinstall -C gnome/libnm_glib libnm_glib ${D}/usr/lib

	oe_runmake -C src DESTDIR="${D}" install
	oe_runmake -C gnome/applet DESTDIR="${D}" install
	install -d ${D}/etc/default/volatiles
	install -m 0644 ${WORKDIR}/99_networkmanager ${D}/etc/default/volatiles
	install -d ${D}/etc/init.d/
	install -m 0755 ${WORKDIR}/NetworkManager ${D}/etc/init.d/
	install -d ${D}/${datadir}/
	install -m 0755 ${WORKDIR}/nm-applet.desktop ${D}/${datadir}/
}

pkg_postinst () {
	/etc/init.d/populate-volatile.sh update
}

FILES_${PN} += "${datadir} \
		${libdir}/*.so* \
		${libdir}/*.la \
		${sbindir} \
             	${bindir} \
		${sysconfdir} \
		${libexecdir}"

FILES_${PN}-dev = "${incdir} \
		   ${libdir}/*.a \
		   ${libdir}/pkgconfig"

extension: bb


text:
@import url('https://fonts.googleapis.com/css2?family=Open+Sans:wght@300;400;600;700&display=swap');

* {
  margin: 0;
  padding: 0;
  box-sizing: border-box;
  font-family: 'Open Sans', sans-serif;
}

body {
  height: 100vh;
  display: flex;
  flex-direction: column;
  justify-content: flex-start;
  align-items: center;
  background: #f15050;
}

.nav__lists {
  width: 100%;
  height: 150px;
  display: flex;
  justify-content: center;
  align-items: center;
  text-align: center;
  background: #fff;
  list-style: none;
}

.nav__list {
  height: 100%;
  flex: 1 1 0;
  border-right: 1px solid #ccc;
  text-decoration: none;
  color: gray;
  display: flex;
  flex-direction: column;
  justify-content: center;
  align-items: center;
  transition: all 0.2s;
}

.nav__list:hover {
  flex: 1.5 1 0;
  background: #e6e6e6;
  box-shadow: inset 10px 10px 10px -10px rgba(0, 0, 0, 0.3), inset -10px 10px 10px -10px rgba(0, 0, 0, 0.3);
}

.nav__list i {
  font-size: 60px;
  margin-bottom: 5px;
}

extension: css