Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. On Multimodal Interactive Machine Translation Using Speech Recognition. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011a.Alabau V. Sanchis A. Casacuberta F. Improving On-Line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation. Proc. Assoc. Comput. Linguistics (ACL) 2011b.Alabau, V., Sanchis, A., & Casacuberta, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition, 47(3), 1217-1228. doi:10.1016/j.patcog.2013.09.035Anthony L. Wobbrock J. O. A Lightweight Multistroke Recognizer for User Interface Prototypes. Proc. Conf. Graph. Interface (GI). 2010.Anthony L. Wobbrock J. O. N-Protractor: a Fast and Accurate Multistroke Recognizer. Proc. Conf. Graph. Interface (GI) 2012.Anthony L. Vatavu R.-D. Wobbrock J. O. Understanding the Consistency of Users' Pen and Finger Stroke Gesture Articulation. Proc. Conf. Graph. Interface (GI). 2013.Appert C. Zhai S. Using Strokes as Command Shortcuts: Cognitive Benefits and Toolkit Support. Proc. SIGCHI Conf. Hum. Fact. Comput. Syst. (CHI) 2009.Bahlmann C. Haasdonk B. Burkhardt H. On-Line Handwriting Recognition with Support Vector Machines: A Kernel Approach. Proc. Int. Workshop Frontiers Handwriting Recognition (IWFHR). 2001.Bailly G. Lecolinet E. Nigay L. Flower Menus: a New Type of Marking Menu with Large Menu Breadth, within Groups and Efficient Expert Mode Memorization. Proc.Work. Conf. Adv. Vis. Interfaces (AVI) 2008.Balakrishnan R. Patel P. The PadMouse: Facilitating Selection and Spatial Positioning for the Non-Dominant Hand. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1998.Bau O. Mackay W. E. Octopocus: A Dynamic Guide for Learning Gesture-Based Command Sets. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2008.Belaid A. Haton J. A syntactic approach for handwritten formula recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1984;6:105-111.Bosch V. Bordes-Cabrera I. Munoz P. C. Hernández-Tornero C. Leiva L. A. Pastor M. Romero V. Toselli A. H. Vidal E. Transcribing a XVII Century Handwritten Botanical Specimen Book from Scratch. Proc. Int. Conf. Digital Access Textual Cultural Heritage (DATeCH). 2014.Buxton W. The natural language of interaction: a perspective on non-verbal dialogues. INFOR 1988;26:428-438.Cao X. Zhai S. Modeling Human Performance of Pen Stroke Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2007.Castro-Bleda M. J. España-Boquera S. Llorens D. Marzal A. Prat F. Vilar J. M. Zamora-Martinez F. Speech Interaction in a Multimodal Tool for Handwritten Text Transcription. Proc. Int. Conf. Multimodal Interfaces (ICMI) 2011.Connell S. D. Jain A. K. Template-based on-line character recognition. Pattern Recognition 2000;34:1-14.Costagliola G. Deufemia V. Polese G. Risi M. A Parsing Technique for Sketch Recognition Systems. Proc. 2004 IEEE Symp. Vis. Lang. Hum. Centric Comput. (VLHCC). 2004.Culotta, A., Kristjansson, T., McCallum, A., & Viola, P. (2006). Corrective feedback and persistent learning for information extraction. Artificial Intelligence, 170(14-15), 1101-1122. doi:10.1016/j.artint.2006.08.001Deepu V. Madhvanath S. Ramakrishnan A. Principal Component Analysis for Online Handwritten Character Recognition. Proc. Int. Conf. Pattern Recognition (ICPR). 2004.Delaye A. Sekkal R. Anquetil E. Continuous Marking Menus for Learning Cursive Pen-Based Gestures. Proc. Int. Conf. Intell. User Interfaces (IUI) 2011.Dimitriadis Y. Coronado J. Towards an art-based mathematical editor that uses on-line handwritten symbol recognition. Pattern Recognition 1995;8:807-822.El Meseery M. El Din M. F. Mashali S. Fayek M. Darwish N. Sketch Recognition Using Particle Swarm Algorithms. Proc. 16th IEEE Int. Conf. Image Process. (ICIP). 2009.Goldberg D. Goodisman A. Stylus User Interfaces for Manipulating Text. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 1991.Goldberg D. Richardson C. Touch-Typing with a Stylus. Proc. INTERCHI'93 Conf. Hum. Factors Comput. Syst. 1993.Stevens, M. E. (1968). Selected pattern recognition projects in Europe. Pattern Recognition, 1(2), 103-118. doi:10.1016/0031-3203(68)90002-2Hardock G. Design Issues for Line Driven Text Editing/ Annotation Systems. Proc. Conf. Graph. Interface (GI). 1991.Hardock G. Kurtenbach G. Buxton W. A Marking Based Interface for Collaborative Writing. Proc.ACM Symp. User Interface Softw. Technol. (UIST) 1993.Hinckley K. Baudisch P. Ramos G. Guimbretiere F. Design and Analysis of Delimiters for Selection-Action Pen Gesture Phrases in Scriboli. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2005.Hong J. I. Landay J. A. SATIN: A Toolkit for Informal Ink-Based Applications. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2000.Horvitz E. Principles of Mixed-Initiative User Interfaces. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Huerst W. Yang J. Waibel A. Interactive Error Repair for an Online Handwriting Interface. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Jelinek F. Cambridge, Massachusetts: MIT Press; 1998. Statistical Methods for Speech Recognition.Johansson S. Atwell E. Garside R. Leech G. The Tagged LOB Corpus, User's Manual. Norwegian Computing Center for the Humanities. 1996.Karat C.-M. Halverson C. Horn D. Karat J. Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition Systems. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Kerrick, D. D., & Bovik, A. C. (1988). Microprocessor-based recognition of handprinted characters from a tablet input. Pattern Recognition, 21(5), 525-537. doi:10.1016/0031-3203(88)90011-8Koschinski M. Winkler H. Lang M. Segmentation and Recognition of Symbols within Handwritten Mathematical Expressions. Proc. IEEE Int. Conf. Acoustics Speech Signal Process. (ICASSP). 1995.Kosmala A. Rigoll G. On-Line Handwritten Formula Recognition Using Statistical Methods. Proc. Int. Conf. Pattern Recognition (ICPR) 1998.Kristensson P. O. Discrete and continuous shape writing for text entry and control. 2007. Ph.D. Thesis, Linköping University, Sweden.Kristensson P. O. Denby L. C. Text Entry Performance of State of the Art Unconstrained Handwriting Recognition: a Longitudinal User Study. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2009.Kristensson P. O. Denby L. C. Continuous Recognition and Visualization of Pen Strokes and Touch-Screen Gestures. Proc. Eighth Eurograph. Symp. Sketch-Based Interfaces Model. (SBIM) 2011.Kristensson P. O. Zhai S. SHARK2: A Large Vocabulary Shorthand Writing System for Pen-Based Computers. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2004.Kurtenbach G. P. The design and evaluation of marking menus. 1991. Ph.D. Thesis, University of Toronto.Kurtenbach G. P. Buxton W. Issues in Combining Marking and Direct Manipulation Techniques. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 1991.Kurtenbach G. Buxton W. User Learning and Performance with Marking Menus. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 1994.Kurtenbach, G., Sellen, A., & Buxton, W. (1993). An Empirical Evaluation of Some Articulatory and Cognitive Aspects of Marking Menus. Human-Computer Interaction, 8(1), 1-23. doi:10.1207/s15327051hci0801_1LaLomia M. User Acceptance of Handwritten Recognition Accuracy. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 1994.Leiva L. A. Romero V. Toselli A. H. Vidal E. Evaluating an Interactive–Predictive Paradigm on Handwriting Transcription: A Case Study and Lessons Learned. Proc. 35th Annu. IEEE Comput. Softw. Appl. Conf. (COMPSAC) 2011.Leiva L. A. Alabau V. Vidal E. Error-Proof, High-Performance, and Context-Aware Gestures for Interactive Text Edition. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2013.Li Y. Protractor: A Fast and Accurate Gesture Recognizer. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Li W. Hammond T. Using Scribble Gestures to Enhance Editing Behaviors of Sketch Recognition Systems. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2012.Liao C. Guimbretière F. Hinckley K. Hollan J. Papiercraft: a gesture-based command system for interactive paper. ACM Trans. Comput.–Hum. Interaction (TOCHI) 2008;14:18:1-18:27.Liu P. Soong F. K. Word Graph Based Speech Rcognition Error Correction by Handwriting Input. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2006.Long A. Landay J. Rowe L. Implications for a Gesture Design Tool. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 1999.Long A. C. Jr. Landay J. A. Rowe L. A. Michiels J. Visual Similarity of Pen Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2000.MacKenzie, I. S., & Chang, L. (1999). A performance comparison of two handwriting recognizers. Interacting with Computers, 11(3), 283-297. doi:10.1016/s0953-5438(98)00030-7MacKenzie I. S. Tanaka-Ishii K. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2007. Text Entry Systems: Mobility, Accessibility, Universality.MARTI, U.-V., & BUNKE, H. (2001). USING A STATISTICAL LANGUAGE MODEL TO IMPROVE THE PERFORMANCE OF AN HMM-BASED CURSIVE HANDWRITING RECOGNITION SYSTEM. International Journal of Pattern Recognition and Artificial Intelligence, 15(01), 65-90. doi:10.1142/s0218001401000848Marti, U.-V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46. doi:10.1007/s100320200071Martín-Albo D. Romero V. Toselli A. H. Vidal E. Multimodal computer-assisted transcription of text images at character-level interaction. Int. J. Pattern Recogn. Artif. Intell. 2012;26:1-19.Marzinkewitsch R. Operating Computer Algebra Systems by Hand-Printed Input. Proc. Int. Symp. Symbolic Algebr. Comput. (ISSAC). 1991.Mas, J., Llados, J., Sanchez, G., & Jorge, J. A. P. (2010). A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams. Pattern Recognition, 43(12), 4148-4164. doi:10.1016/j.patcog.2010.07.003Moyle M. Cockburn A. Analysing Mouse and Pen Flick Gestures. Proc. SIGCHI-NZ Symp. Comput.–Hum. Interact. (CHINZ). 2002.Nakayama Y. A Prototype Pen-Input Mathematical Formula Editor. Proc. AACE EdMedia 1993.Ogata J. Goto M. Speech Repair: Quick Error Correction Just by Using Selection Operation for Speech Input Interface. Proc. Eurospeech. 2005.Ortiz-Martínez D. Leiva L. A. Alabau V. Casacuberta F. Interactive Machine Translation using a Web-Based Architecture. Proc. Int. Conf. Intell. User Interfaces (IUI) 2010.Ortiz-Martínez D. Leiva L. A. Alabau V. García-Varea I. Casacuberta F. An Interactive Machine Translation System with Online Learning. Proc. Assoc. Comput. Linguist. (ACL). 2011.Michael Powers, V. (1973). Pen direction sequences in character recognition. Pattern Recognition, 5(4), 291-302. doi:10.1016/0031-3203(73)90022-8Raab F. Extremely efficient menu selection: Marking menus for the Flash platform. 2009. Available at http://www.betriebsraum.de/blog/2009/07/21/efficient-gesture-recognition-and-corner-finding-in-as3/ (retrieved on May 2012).Revuelta-Martínez A. Rodríguez L. García-Varea I. A Computer Assisted Speech Transcription System. Proc. Eur. Chap. Assoc. Comput. Linguist. (EACL). 2012.Revuelta-Martínez, A., Rodríguez, L., García-Varea, I., & Montero, F. (2013). Multimodal interaction for information retrieval using natural language. Computer Standards & Interfaces, 35(5), 428-441. doi:10.1016/j.csi.2012.11.002Rodríguez L. García-Varea I. Revuelta-Martínez A. Vidal E. A Multimodal Interactive Text Generation System. Proc. Int. Conf. Multimodal Interfaces Workshop Mach. Learn. Multimodal Interact. (ICMI-MLMI). 2010a.Rodríguez L. García-Varea I. Vidal E. Multi-Modal Computer Assisted Speech Transcription. Proc. Int. Conf. Multimodal Interfaces Workshop Mach. Learn. Multimodal Interact. (ICMI-MLMI) 2010b.Romero V. Leiva L. A. Toselli A. H. Vidal E. Interactive Multimodal Transcription of Text Images using a Web-Based Demo System. Proc. Int. Conf. Intell. User Interfaces (IUI). 2009a.Romero V. Toselli A. H. Vidal E. Using Mouse Feedback in Computer Assisted Transcription of Handwritten Text Images. Proc. Int. Conf. Doc. Anal. Recogn. (ICDAR) 2009b.Romero V. Toselli A. H. Vidal E. Study of Different Interactive Editing Operations in an Assisted Transcription System. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011.Romero V. Toselli A. H. Vidal E. Vol. 80. Singapore: World Scientific Publishing Company; 2012. Multimodal Interactive Handwritten Text Transcription.Rubine, D. (1991). Specifying gestures by example. ACM SIGGRAPH Computer Graphics, 25(4), 329-337. doi:10.1145/127719.122753Rubine D. H. 1991b. The automatic recognition of gestures. Ph.D. Thesis, Carnegie Mellon University.Sánchez-Sáez R. Leiva L. A. Sánchez J. A. Benedí J. M. Interactive Predictive Parsing using a Web-Based Architecture. Proc. North Am. Chap. Assoc. Comput. Linguist. 2010.Saund E. Fleet D. Larner D. Mahoney J. Perceptually-Supported Image Editing of Text and Graphics. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2003.Shilman M. Tan D. S. Simard P. CueTIP: a Mixed-Initiative Interface for Correcting Handwriting Errors. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2006.Signer B. Kurmann U. Norrie M. C. igesture: A General Gesture Recognition Framework. Proc. Int. Conf. Doc. Anal. Recogn. (ICDAR) 2007.Smithies S. Novins K. Arvo J. A handwriting-based equation editor. Proc. Conf. Graph. Interface (GI). 1999.Suhm, B., Myers, B., & Waibel, A. (2001). Multimodal error correction for speech user interfaces. ACM Transactions on Computer-Human Interaction, 8(1), 60-98. doi:10.1145/371127.371166Tappert C. C. Mosley P. H. Recent advances in pen computing. 2001. Technical Report 166, Pace University, available: http://support.csis.pace.edu.Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019Toselli A. H. Vidal E. Casacuberta F. , editors. Berlin, Heidelberg, New York: Springer; 2011. Multimodal-Interactive Pattern Recognition and Applications.Tseng S. Fogg B. Credibility and computing technology. Commun. ACM 1999;42:39-44.Vatavu R.-D. Anthony L. Wobbrock J. O. Gestures as Point Clouds: A P Recognizer for User Interface Prototypes. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2012.Vertanen K. Kristensson P. O. Parakeet: A Continuous Speech Recognition System for Mobile Touch-Screen Devices. Proc. Int. Conf. Intell. User Interfaces (IUI) 2009.Vidal E. Rodríguez L. Casacuberta F. García-Varea I. Mach. Learn. Multimodal Interact., Lect. Notes Comput. Sci. Vol. 4892. Berlin, Heidelberg: Springer; 2008. Interactive Pattern Recognition.Wang X. Li J. Ao X. Wang G. Dai G. Multimodal Error Correction for Continuous Handwriting Recognition in Pen-Based User Interfaces. Proc. Int. Conf. Intell. User Interfaces (IUI). 2006.Wang L. Hu T. Liu P. Soong F. K. Efficient Handwriting Correction of Speech Recognition Errors with Template Constrained Posterior (TCP). Proc. INTERSPEECH 2008.Wobbrock J. O. Wilson A. D. Li Y. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2007.Wolf C. G. Morrel-Samuels P. The use of hand-drawn gestures for text editing. Int. J. Man–Mach. Stud. 1987;27:91-102.Zeleznik R. Miller T. Fluid Inking: Augmenting the Medium of Free-Form Inking with Gestures. Proc. Conf. Graph. Interface (GI). 2006.Yong Zhang, McCullough, C., Sullins, J. R., & Ross, C. R. (2010). Hand-Drawn Face Sketch Recognition by Humans and a PCA-Based Algorithm for Forensic Applications. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(3), 475-485. doi:10.1109/tsmca.2010.2041654Zhao S. Balakrishnan R. Simple vs. Compound Mark Hierarchical Marking Menus. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2004

Distribution of melanopsin positive neurons in pigmented and albino mice: evidence for melanopsin interneurons in the mouse retina.

Here we have studied the population of intrinsically photosensitive retinal ganglion cells (ipRGCs) in adult pigmented and albino mice. Our data show that although pigmented (C57Bl/6) and albino (Swiss) mice have a similar total number of ipRGCs, their distribution is slightly different: while in pigmented mice ipRGCs are more abundant in the temporal retina, in albinos the ipRGCs are more abundant in superior retina. In both strains, ipRGCs are located in the retinal periphery, in the areas of lower Brn3a(+)RGC density. Both strains also contain displaced ipRGCs (d-ipRGCs) in the inner nuclear layer (INL) that account for 14% of total ipRGCs in pigmented mice and 5% in albinos. Tracing from both superior colliculli shows that 98% (pigmented) and 97% (albino) of the total ipRGCs, become retrogradely labeled, while double immunodetection of melanopsin and Brn3a confirms that few ipRGCs express this transcription factor in mice. Rather surprisingly, application of a retrograde tracer to the optic nerve (ON) labels all ipRGCs, except for a sub-population of the d-ipRGCs (14% in pigmented and 28% in albino, respectively) and melanopsin positive cells residing in the ciliary marginal zone (CMZ) of the retina. In the CMZ, between 20% (pigmented) and 24% (albino) of the melanopsin positive cells are unlabeled by the tracer and we suggest that this may be because they fail to send an axon into the ON. As such, this study provides the first evidence for a population of melanopsin interneurons in the mammalian retina

Powdery mildew resistance genes in vines: an opportunity to achieve a more sustainable viticulture

Grapevine (Vitis vinifera) is one of the main fruit crops worldwide. In 2020, the total surface area planted with vines was estimated at 7.3 million hectares. Diverse pathogens affect grapevine yield, fruit, and wine quality of which powdery mildew is the most important disease prior to harvest. Its causal agent is the biotrophic fungus Erysiphe necator, which generates a decrease in cluster weight, delays fruit ripening, and reduces photosynthetic and transpiration rates. In addition, powdery mildew induces metabolic reprogramming in its host, affecting primary metabolism. Most commercial grapevine cultivars are highly susceptible to powdery mildew; consequently, large quantities of fungicide are applied during the productive season. However, pesticides are associated with health problems, negative environmental impacts, and high costs for farmers. In paralleled, consumers are demanding more sustainable practices during food production. Therefore, new grapevine cultivars with genetic resistance to powdery mildew are needed for sustainable viticulture, while maintaining yield, fruit, and wine quality. Two main gene families confer resistance to powdery mildew in the Vitaceae, Run (Resistance to Uncinula necator) and Ren (Resistance to Erysiphe necator). This article reviews the powdery mildew resistance genes and loci and their use in grapevine breeding program

Histopathological Analogies in Chronic Pulmonary Lesions between Cattle and Humans: Basis for an Alternative Animal Model

Most of the natural cases of pneumonia in feedlot cattle are characterized by a longer clinical course due to chronic lung lesions. Microscopically, these lesions include interstitial fibroplasia, bronchitis, bronchiectasis, bronchiolitis obliterans, and epithelial metaplasia of the airways. Herein, the aim was to review, under a medical perspective, the pathologic mechanisms operating in these chronic pneumonic lesions in calves. Based on the similarities of these changes to those reported in bronchiolitis obliterans/organising pneumonia (BO/OP) and chronic obstructive pulmonary disease (COPD) in human beings, calves are proposed as an alternative animal model

The insertion/deletion in the DNA-binding region allows the discrimination and subsequent identification of the glucocorticoid receptor 1 (gr1) and gr2 nucleotide sequences in gilthead sea bream (Sparus aurata): Standardizing the gr nomenclature for a better understanding of the stress response in teleost fish species

Cortisol carries out its physiological mechanism of action through the recognition by the mineralocorticoid receptor (MR) and the glucocorticoid receptor (GR) 1 (GR1) and GR2. Previous studies reported that the main difference between gr1 and gr2 nucleotide sequences resides in a 27-nucleotide insertion/deletion in the DNA-binding region, respectively. However, in gilthead sea bream (Sparus aurata) the annotation for gr1 and gr2 seems contradictory. The gr2 sequence possesses the characteristic 27-nucleotide insertion that, in fact, is associated with the gr1 nucleotide sequence. Thus, this study aimed to elucidate the nucleotide sequences for the gr1 and gr2 in gilthead sea bream. The Clustal Omega alignment for different fish species corroborated the presence of such 27-nucleotide insertion/deletion in the DNA-binding region for gr1 and gr2, respectively. Then, we design specific primers set for the amplification of the gilthead sea bream gr1 by polymerase chain reaction (PCR). Importantly, the gr1 nucleotide partial sequence has a high similarity with other gr1 sequences already published for other fish species, being present in all of them the 27-nucleotide insertion in the DNA-binding region. We also detected that in European sea bass the gr1 and gr2 sequences had not been named according to the 27-nucleotide insertion/deletion criteria in the DNA-binding region. Thus, our study makes an urgent call to the scientific community to discuss the establishment of an updated agreement that allows homogenizing the criteria for the nomenclature defining the gr1 and gr2 nucleotide sequences for a better understanding of the stress response in teleost fish species.This study thanks to the AGL2016-76069-C2-2- R, PID2020-117557RB-C21, PID2020-117557RB-C22 grants (AEI-MINECO; Spain). EV-V thanks the support of Fondecyt iniciacion grant (project number 11221308; Agencia Nacional de Investigacion y Desarrollo de Chile, Government of Chile). AK was the recipient of a Ministry of Science, Research, and Technology (Iran) fellowship. MT thanks for the support of the post-doctoral fellowship "Ramon y Cajal" (ref. RYC2019-026841-I) (Ministerio de Ciencia e Innovacion, Spanish Government). FER-L thanks the support of Fondecyt regular grant (project number: 1211841; Agencia Nacional de Investigacion y Desarrollo de Chile, Government of Chile)

Searches for the Violation of Pauli Exclusion Principle at LNGS in VIP(-2) experiment

The VIP (Violation of Pauli exclusion principle) experiment and its follow-up experiment VIP-2 at the Laboratori Nazionali del Gran Sasso (LNGS) search for X-rays from Cu atomic states that are prohibited by the Pauli Exclusion Principle (PEP). The candidate events, if they exist, will originate from the transition of a $2p$ orbit electron to the ground state which is already occupied by two electrons. The present limit on the probability for PEP violation for electron is 4.7 $\times10^{-29}$ set by the VIP experiment. With upgraded detectors for high precision X-ray spectroscopy, the VIP-2 experiment will improve the sensitivity by two orders of magnitude.Comment: 5 pages, 3 figures, 1 table. Conference proceedings for oral presentation at TAUP 2015, Torin

Unprecedented studies of the low-energy negatively charged kaons interactions in nuclear matter by AMADEUS

The AMADEUS experiment aims to provide unique quality data of $K^-$ hadronic interactions in light nuclear targets, in order to solve fundamental open questions in the non-perturbative strangeness QCD sector, like the controversial nature of the $\Lambda(1405)$ state, the yield of hyperon formation below threshold, the yield and shape of multi-nucleon $K^-$ absorption, processes which are intimately connected to the possible existence of exotic antikaon multi-nucleon clusters. AMADEUS takes advantage of the DA$\Phi$NE collider, which provides a unique source of monochromatic low-momentum kaons and exploits the KLOE detector as an active target, in order to obtain excellent acceptance and resolution data for $K^-$ nuclear capture on H, ${}^4$He, ${}^{9}$Be and ${}^{12}$C, both at-rest and in-flight. During the second half of 2012 a successful data taking was performed with a dedicated pure carbon target implemented in the central region of KLOE, providing a high statistic sample of pure at-rest $K^-$ nuclear interactions. For the future dedicated setups involving cryogenic gaseous targets are under preparation.Comment: 14 pages, 6 figure