Development of a scalable database for recognition of printed mathemematical expressions

[ES] Buscar información en documentos científicos impresos es un reto problemático que recientemente ha recibido atención especial por parte de la comunidad de investigación de Reconocimiento de Formas. Las Expresiones Matemáticas son elementos complejos que aparecen en documentos cientificos, y desarrollar técnicas para localizarlas y reconocerlas requiere preparar data sets que pueden ser utilizados como punto de referencia. La mayoría de las técnicas actuales para lidiar con Expresiones Matemáticas están basadas en técnicas de Reconocimiento de Formas y Aprendizaje Automático y por tanto, estos data sets tienen que ser preparados con información sobre el ground-truth para entrenamiento y test automático. Sin embargo, preparar data sets grandes es muy costoso y requiere mucho tiempo. Este proyecto introduce un data set de documentos científicos que ha sido preparado con el fin de reconocer y buscar Expresiones Matemáticas. Este data set ha sido generado automáticamente a partir de la versión LATEX de los documentos y consecuentemente puede ser aumentado fácilmente. El ground-truth incluye la posición a nivel de página, la versión LATEX de las Expresiones Matemáticas integradas y aisladas del texto y la secuencia de símbolos representados como unicode code points que se han utilizado para definir estas expresiones. En base a este data set, se han extraído estadísticas como por ejemplo el número total y el tipo de las expresiones, el número medio de expresiones por documento y las frecuencias de distribución de todo el conjunto de expresiones. En este documento también se introduce un experimento de clasificación de símbolos matemáticos que puede ser utilizado como punto de partida.[EN] Searching information in printed scientific documents is a challenging problem that has recently received special attention from the Pattern Recognition research community. Mathematical Expressions are complex elements that appear in scientific documents, and developing techniques for locating and recognizing them requires preparation of data sets that can be used as benchmarks. Most of the current techniques for dealing with Mathematical Expressions are based in Machine Intelligent techniques and therefore these data sets have to be prepared with ground-truth information for automatic training and testing. However preparing large data sets with ground-truth is a very expensive and timeconsuming task. This project introduces a data set of scientific documents that has been prepared for Mathematical Expression recognition and searching. This data set has been automatically generated from the LATEX version of the documents and consequently can be enlarged easily. The ground-truth includes the position at page level, the LATEX version for Mathematical Expressions both embedded in the text and displayed and the sequence of mathematical symbols represented as unicode code points used to define these expressions. Based on this data set, statistics such as the total number and type of expressions, the average number of expressions per document and their frequency distribution were extracted. A baseline classification experiment with mathematical symbols from this data set is also reported in this paper.Anitei, D. (2020). Development of a scalable database for recognition of printed mathemematical expressions. Universitat Politècnica de València. http://hdl.handle.net/10251/150390TFG

Designing a Psychologists’ Core Competencies Validation Method using Behaviorally Anchored Rated Scales

AbstractThe current study presents a model for validation of a battery of aptitude tests for psychologists using the criterion method of behavioural anchors based on core competences. The participants were 173 psychologists registered at the Romanian College of Psychologists, aged between 24 and 48 years old (M=36.71; S.D.=3.84), both man and woman, rural and urban areas. Using Communication competences, Focused Attention and Social Skills competences and dependent variable total performances measured by BARS, the results provide a 57.4% reduction in the prediction error relative to using only the predictive model

Reconocimiento automático de un censo histórico impreso sin recursos lingüísticos

[ES] El reconocimiento automático de documentos históricos impresos es actualmente un problema resuelto para muchas colecciones de datos. Sin embargo, los sistemas de reconocimiento automático de documentos históricos impresos aún deben resolver varios obstáculos inherentes al trabajo con documentos antiguos. La degradación del papel o las manchas pueden aumentar la dificultad del correcto reconocimiento de los caracteres. No obstante, dichos problemas se pueden paliar utilizando recursos lingüísticos para entrenar buenos modelos de lenguaje que disminuyan la tasa de error de los caracteres. En cambio, hay muchas colecciones como la que se presenta en este trabajo, compuestas por tablas que contienen principalmente números y nombres propios, para las que no se dispone. En este trabajo se muestra que el reconocimiento automático puede realizarse con éxito para una colección de documentos sin utilizar ningún recurso lingüístico. Este proyecto cubre la extracción de información y el proceso de OCR dirigido, especialmente diseñados para el reconocimiento automático de un censo español del siglo XIX, registrado en documentos impresos. Muchos de los problemas relacionados con los documentos históricos se resuelven utilizando una combinación de técnicas clásicas de visión por computador y aprendizaje neuronal profundo. Los errores, como los caracteres mal reconocidos, son detectados y corregidos gracias a la información redundante que contiene el censo. Dada la importancia de este censo español para la realización de estudios demográficos, este trabajo da un paso más e introduce un modelo demostrador que facilita la investigación sobre este corpus mediante la indexación de los datos.[EN] Automatic recognition of typeset historical documents is currently a solved problem for many collections of data. However, systems for automatic recognition of typeset historical documents still need to address several issues inherent to working with this kind of documents. Degradation of the paper or smudges can increase the difficulty of correctly recognizing characters, problems that can be alleviated by using linguistic resources for training good language models which decrease the character error rate. Nonetheless, there are many collections such as the one presented in this paper, composed of tables that contain mainly numbers and proper names, for which a language model is neither available nor useful. This paper illustrates that automatic recognition can be done successfully for a collection of documents without using any linguistic resources. The paper covers the information extraction and the targeted OCR process, specially designed for the automatic recognition of a Spanish census from the XIX century, registered in printed documents. Many of the problems related to historical documents are overcame by using a combination of classical computer vision techniques and deep learning. Errors, such as miss-recognized characters, are detected and corrected thanks to redundant information that the census contains. Given the importance of this Spanish census for conducting demographic studies, this paper goes a step forward and introduces a demonstrator model to facilitate researching on this corpus by indexing the data.This work has been partially supported by the BBVA Fundation, as a collaboration between the PRHLT team in charge of the HisClima project and the ESPAREL project.Anitei, D. (2021). Reconocimiento automático de un censo histórico impreso sin recursos lingüísticos. Universitat Politècnica de València. http://hdl.handle.net/10251/172694TFG

El orden público en el Derecho internacional privado rumano en materia de relaciones familiares

The private international law public order of Romanian law in the field of family relations is found in the provisions: of the Romanian Civil Code (contains provisions of substantive law and provisions of private international law), but also in the provisions of the Code of Procedure Civil (as we will exemplify and exemplify during the present study); in a series of provisions of European law (especially European regulations, of immediate, direct application and with priority in the legal order of the Member States) but also the provisions of private international law throughout the world. Later we will analyze the particularizations of public order with an element of foreigners in the field of family relations in the texts of the Romanian Civil Code in Chapter II called „Family” (article 2585-article 2612) of Book VII, entitled „Dispositions of private international law” (article 2557-article 2663) but also of a series of European regulations in which the provisions of the Romanian Civil Code largely find their correspondence.El orden público de derecho internacional privado del derecho rumano en el ámbito de las relaciones familiares se encuentra en las disposiciones: del Código Civil rumano (contiene disposiciones de derecho sustantivo y disposiciones de derecho internacional privado), pero también en las disposiciones del Código de procedimiento Civil (como ejemplificaremos y ejemplificaremos durante el presente estudio); en una serie de disposiciones del derecho europeo (especialmente las normativas europeas, de aplicación inmediata, directa y con prioridad en el ordenamiento jurídico de los Estados miembros) pero también las disposiciones del derecho internacional privado en todo el mundo. Más adelante analizaremos las particularizaciones del orden público con un elemento de extranjería en el campo de las relaciones familiares en los textos del Código civil rumano en el Capítulo II denominado “Familia” (artículo 2585-artículo 2612) del Libro VII, titulado “Disposiciones de derecho internacional privado” (artículo 2557- artículo 2663) sino también de una serie de reglamentos europeos en los que las disposiciones del Código Civil rumano encuentran en gran medida su correspondencia

Discriminative estimation of probabilistic context-free grammars for mathematical expression recognition and retrieval

[EN] We present a discriminative learning algorithm for the probabilistic estimation of two-dimensional probabilistic context-free grammars (2D-PCFG) for mathematical expressions recognition and retrieval. This algorithm is based on a generalization of the H-criterion as the objective function and the growth transformations as the optimization method. For the development of the discriminative estimation algorithm, the N-best interpretations provided by the 2D-PCFG have been considered. Experimental results are reported on two available datasets: Im2Latex and IBEM. The first experiment compares the proposed discriminative estimation method with the classic Viterbi-based estimation method. The second one studies the performance of the estimated models depending on the length of the mathematical expressions and the number of admissible errors in the metric used.This research has been developed with the support of Grant PID2020-116813RBI00a funded by MCIN/AEI/ 10.13039/501100011033 and FPI grant CIACIF/2021/313 funded by Generalitat Valenciana. Universitat Politecnica de Valencia Grant No. SP20210263Noya García, E.; Benedí Ruiz, JM.; Sánchez Peiró, JA.; Anitei, D. (2023). Discriminative estimation of probabilistic context-free grammars for mathematical expression recognition and retrieval. Pattern Analysis and Applications. 26:1571-1584. https://doi.org/10.1007/s10044-023-01158-81571158426Bahl LR, Jelinek F, Mercer RL (1983) A maximum likelihood approach to continuous speech recognition. IEEE Trans Pattern Anal Machine Intell 5(2):179–190Koehn P (2009) Statistical Machine Translation. Cambridge University Press, ???. https://doi.org/10.1017/CBO9780511815829Graves A, Fernández S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In: ICML, vol 2006, pp 369–376. https://doi.org/10.1145/1143844.1143891Marzal A (1993) Cálculo de las k mejores soluciones a problemas de programación dinámica. PhD thesis, Universidad Politécnica de ValenciaJiménez VM, Marzal A (2000) Computation of the N Best Parse Trees for Weighted and Stochastic Context-Free Grammars. In: Advances in Pattern Recognition. Lecture Notes in Computer Science, 1876, pp 183–192 https://doi.org/10.1007/3-540-44522-6_19Ortmanns S, Ney H, Aubert X (1997) A word graph algorithm for large vocabulary continuous speech recognition. Comput Speech Lang 11(1):43–72. https://doi.org/10.1006/csla.1996.0022Noya E, Sánchez JA, Benedí JM (2021) Generation of Hypergraphs from the N-Best Parsing of 2D-Probabilistic Context-Free Grammars for Mathematical Expression Recognition. In: ICPR, pp 5696–5703. https://doi.org/10.1109/ICPR48806.2021.9412273Ueffing N, Och FJ, Ney H (2002) Generation of word graphs in statistical machine translation. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP 2002), pp 156–163. Association for Computational Linguistics, ???. https://doi.org/10.3115/1118693.1118714. https://aclanthology.org/W02-1021Toselli AH, Vidal E, Puigcerver J, Noya-García E (2019) Probabilistic multi-word spotting in handwritten text images. Pattern Anal Appl 22:23–32. https://doi.org/10.1007/s10044-018-0742-zSánchez-Sáez R, Sánchez JA, Benedí JM (2010) Confidence measures for error discrimination in an interactive predictive parsing framework. In: Coling, pp 1220–1228Benedí JM, Sánchez JA (2005) Estimation of stochastic context-free grammars and their use as language models. Comput Speech Lang 19(3):249–274. https://doi.org/10.1016/j.csl.2004.09.001Awal AM, Mouchère H, Viard-Gaudin C (2012) A global learning approach for an online handwritten mathematical expression recognition system. Pattern Recogn Lett 35:68–77. https://doi.org/10.1016/j.patrec.2012.10.024Álvaro F, Sánchez JA, Benedí JM (2016) An Integrated Grammar-based Approach for Mathematical Expression Recognition. Pattern Recogn 51:135–147. https://doi.org/10.1016/j.patcog.2015.09.013Deng Y, Kanervisto A, Ling J, Rush AM (2017) Image-to-markup generation with coarse-to-fine attention. In: Proceedings of the ICML-17, pp 980–989Anitei D, Sánchez JA, Fuentes JM, Paredes R, Benedí JM (2021) ICDAR2021 Competition on mathematical formula detection. In: ICDAR, pp 783–795. https://doi.org/10.1007/978-3-030-86337-1_52Gopalakrishnan PS, Kanevsky D, Nadas A, Nahamoo D (1991) An inequality for rational functions with applications to some statistical estimation problems. IEEE Trans Inf Theory 37(1):107–113. https://doi.org/10.1109/18.61108Maca M, Benedí JM, Sánchez JA (2021) Discriminative Learning for Probabilistic Context-Free Grammars based on Generalized H-Criterion. Preprint arXiv:2103.08656arXiv:2103.08656 [cs.CL]Woodland PC, Povey D (2002) Large scale discriminative training of hidden Markov models for speech recognition. Comput Speech Lang 16(1):25–47. https://doi.org/10.1006/csla.2001.0182Noya E, Benedí JM, Sánchez JA, Anitei D (2022) Discriminative learning of two-dimensional probabilistic context-free grammars for mathematical expression recognition and retrieval. In: IbPRIA, pp 333–347. https://doi.org/10.1007/978-3-031-04881-4_27Zanibbi R, Blostein D (2011) Recognition and Retrieval of Mathematical Expressions. IJDAR 15:331–357. https://doi.org/10.1007/s10032-011-0174-4Huang J, Tan J, Bi N (2020) Overview of mathematical expression recognition. In: Pattern recognition and artificial intelligence, pp 41–54. https://doi.org/10.1007/978-3-030-59830-3_4Mahdavi M, Zanibbi R, Mouchere H, Viard-Gaudin C, Garain U (2019) ICDAR 2019 CROHME + TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection. In: ICDAR, pp 1533–1538. https://doi.org/10.1109/ICDAR.2019.00247Wang DH, Yin F, Wu JW, Yan YP, Huang ZC, Chen GY, Wang Y, Liu CL (2020) ICFHR 2020 Competition on offline recognition and spotting of handwritten mathematical expressions - OffRaSHME. In: ICFHR, pp. 211–215. https://doi.org/10.1109/ICFHR2020.2020.00047Wan Z, Fan K, Wang Q, Zhang S (2019) Recognition of printed mathematical formula symbols based on convolutional neural network. DEStech Transactions on Computer Science and Engineering. https://doi.org/10.12783/dtcse/ica2019/30711Wu J-W, Yin F, Zhang Y-M, Zhang X-Y, Liu C-L (2020) Handwritten mathematical expression recognition via paired adversarial learning. Int J Comput Vis 128:2386–401. https://doi.org/10.1007/s11263-020-01291-5Peng S, Gao L, Yuan K, Tang Z (2021) Image to LaTeX with Graph Neural Network for Mathematical Formula Recognition. In: ICDAR, pp 648–663. https://doi.org/10.1007/978-3-030-86331-9_42Zhao W, Gao L, Yan Z, Peng S, Du L, Zhang Z (2021) Handwritten mathematical expression recognition with bidirectionally trained transformer. In: Document analysis and recognition – ICDAR 2021, pp 570–584. https://doi.org/10.1007/978-3-030-86331-9_37Davila K, Joshi R, Setlur S, Govindaraju V, Zanibbi R (2019) Tangent-V: Math formula image search using line-of-sight graphs, pp 681–695. https://doi.org/10.1007/978-3-030-15712-8_44Zhong W, Zanibbi R (2019) Structural similarity search for formulas using leaf-root paths in operator subtrees, pp 116–129. https://doi.org/10.1007/978-3-030-15712-8_8Mansouri B, Zanibbi R, Oard D (2019) Characterizing searches for mathematical concepts, pp 57–66. https://doi.org/10.1109/JCDL.2019.00019Chou PA (1989) Recognition of equations using a two-dimensional stochastic context-free grammar. In: Visual communications and image processing IV, vol 1199, pp 852–863. https://doi.org/10.1117/12.970095Pr$$\mathring{u}$$ša D, Hlaváč V (2007) Mathematical Formulae Recognition Using 2D Grammars. ICDAR 2, 849–853. https://doi.org/10.1109/ICDAR.2007.4377035Lari K, Young SJ (1991) Applications of stochastic context-free grammars using the inside-outside algorithm. Comput Speech Lang 5(3):237–257. https://doi.org/10.1016/0885-2308(91)90009-FNey H (1992) Stochastic grammars and pattern recognition. In: Laface, P., De Mori, R. (eds.) Speech recognition and understanding, pp 319–344. https://doi.org/10.1007/978-3-642-76626-8_34Baum LE, Sell GR (1968) Growth transformation for functions on manifolds. Pac J Math 27(2):211–227Casacuberta F (1996) Growth transformations for probabilistic functions of stochastic grammars. IJPRAI 10(3):183–201. https://doi.org/10.1142/S0218001496000153Gopalakrishnan P, Kanevsky D, Nadas A, Nahamoo D, Picheny M (1988) Decoder selection based on cross-entropies. In: ICASSP-88, vol 1, pp 20–23. https://doi.org/10.1109/ICASSP.1988.196499Papineni K, Roukos S, Ward T, Zhu WJ (2002) BLEU: a method for automatic evaluation of machine translation. In: ACL, pp 311–318. https://doi.org/10.3115/1073083.1073135Suzuki M, Tamari F, Fukuda R, Uchida S, Kanahori T (2003) Infty: an integrated ocr system for mathematical documents, pp 95–104. https://doi.org/10.1145/958220.958239Shi B, Bai X, Yao C (2017) An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. TPAMI 39–11:2298–2304. https://doi.org/10.1109/TPAMI.2016.2646371Singh S (2018) Teaching machines to code: neural markup generation with visual attention. Preprint arXiv:1802.05415arXiv:1802.05415 [cs.CL

The IBEM dataset: A large printed scientific image dataset for indexing and searching mathematical expressions

[EN] Searching for information in printed scientific documents is a challenging problem that has recently received special attention from the Pattern Recognition research community. Mathematical expressions are complex elements that appear in scientific documents, and developing techniques for locating and recognizing them requires the preparation of datasets that can be used as benchmarks. Most current techniques for dealing with mathematical expressions are based on Machine Learning techniques which require a large amount of annotated data. These datasets must be prepared with ground-truth information for automatic training and testing. However, preparing large datasets with ground-truth is a very expensive and time-consuming task. This paper introduces the IBEM dataset, consisting of scientific documents that have been prepared for mathematical expression recognition and searching. This dataset consists of 600 documents, more than 8200 page images with more than 160000 mathematical expressions. It has been automatically generated from the Image 1 version of the documents and can be enlarged easily. The ground-truth includes the position at the page level and the Image 1 transcript for mathematical expressions both embedded in the text and displayed. This paper also reports a baseline classification experiment with mathematical symbols and a baseline experiment of Mathematical Expression Recognition performed on the IBEM dataset. These experiments aim to provide some benchmarks for comparison purposes so that future users of the IBEM dataset can have a baseline framework.This work has been partially supported by MCIN/AEI/10.13039/50110 0 011033 under the grant PID2020-116813RB-I00; the Generalitat Valenciana under the FPI grant CIACIF/2021/313; and by the support of the Valencian Graduate School and Research Network of Artificial Intelligence.Anitei, D.; Sánchez Peiró, JA.; Benedí Ruiz, JM.; Noya García, E. (2023). The IBEM dataset: A large printed scientific image dataset for indexing and searching mathematical expressions. Pattern Recognition Letters. 172:29-36. https://doi.org/10.1016/j.patrec.2023.05.033293617

ICDAR 2021 competition on mathematical formula detection

[EN] This paper introduces the Competition on Mathematical Formula Detection that was organized for the ICDAR 2021. The main goal of this competition was to provide the researchers and practitioners a common framework to research on this topic. A large dataset was prepared for this contest where the GT was automatically generated and manually reviewed. Fourteen participants submitted their results for this competition and these results show that there is still room for improvement especially for the detection of embedded mathematical expressions.This work has been partially supported by the Ministerio de Ciencia y Tecnologia under the grant TIN2017-91452-EXP (IBEM) and by the Generalitat Valenciana under the grant PROMETEO/2019/121 (DeepPattern).Anitei, D.; Sánchez Peiró, JA.; Fuentes-López, JM.; Paredes Palacios, R.; Benedí Ruiz, JM. (2021). ICDAR 2021 competition on mathematical formula detection. Springer. 783-795. https://doi.org/10.1007/978-3-030-86337-1_52783795Deng, Y., Kanervisto, A., Rush, A.M.: What you get is what you see: a visual markup decompiler. arXiv abs/1609.04938 (2016)Gehrke, J., Ginsparg, P., Kleinberg, J.: Overview of the 2003 KDD cup. SIGKDD Explor. Newsl. (2), 149–151 (2003)Oberdiek, H.: The zref package. https://osl.ugr.es/CTAN/macros/latex/contrib/zref/zref.pdfLi, X., et al.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection (2020)Mahdavi, M., Zanibbi, R., MouchÚre, H., Viard-Gaudin, C., Garain, U.: ICDAR 2019 CROHME + TFD: competition on recognition of handwritten mathematical expressions and typeset formula detection. In: International Conference on Document Analysis and Recognition (2019)Ohyama, W., Suzuki, M., Uchida, S.: Detecting mathematical expressions in scientific document images using a U-Net trained on a diverse dataset. IEEE Access 7, 144030–144042 (2019)Phillips, I.: Methodologies for using UW databases for OCR and image understanding systems. In: Proceedings of the SPIE, Document Recognition V, vol. 3305, pp. 112–127 (1998)Pizzini, K., Bonzini, P., Meyering, J., Gordon, A.: GNUsed, a stream editor. https://www.gnu.org/software/sed/manual/sed.pdfSolovyev, R., Wang, W., Gabruseva, T.: Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021)Suzuki, M., Uchida, S., Nomura, A.: A ground-truthed mathematical character and symbol image database. In: Proceedings of the 8th International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 675–679 (2005)Zanibbi, R., Blostein, D.: Recognition and retrieval of mathematical expressions. Int. J. Doc. Anal. Recogn. 14, 331–357 (2011)Zanibbi, R., Oard, D.W., Agarwal, A., Mansouri, B.: Overview of ARQMath 2020: CLEF lab on answer retrieval for questions on math. In: Arampatzis, A., et al. (eds.) CLEF 2020. LNCS, vol. 12260, pp. 169–193. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58219-7_1

Aggressive gist with gastric location – case report

Spital Sf Spiridon Iași, Clinica III Chirurgie, Al XI-lea Congres al Asociației Chirurgilor „Nicolae Anestiadi” din Republica Moldova și cea de-a XXXIII-a Reuniune a Chirurgilor din Moldova „Iacomi-Răzeșu” 27-30 septembrie 2011Rezumat: Tumorile stromale gastrointestinale (GIST) sunt definite printr-un ansamblu de argumente clinice, morfologice și imunohistologice. Prezentăm cazul unei paciente în varstă de 39 ani cu GIST localizată la nivelul fornixului gastric cu metastază hepatică, cu simptomatologie clinică nespecifică. Investigatiile paraclinice - ecografia abdominală și examenul computer tomografic (CT) - au evidențiat masele tumorale intraperitoneale fără a putea sugera diagnosticul. Examenul anatomo- patologic extemporaneu stabilește diagnosticul de tumora stromală gastrică și impune conduita chirurgicală corespunzatoare.Abstract: Gastrointestinal stromal tumors (GIST) are defined by a set of clinical, morphological and imunohistological elements. We present a patient, 39 years old, female, with GIST located in the upper part of the stomach with liver metastase, with nonspecific clinical symptoms. Paraclinical - abdominal ultrasound examination and computer tomography (CT) - have shown intraperitoneal tumor masses without suggested the diagnosis of GIST. Anatomic-pathological examination, made during the operation, diagnosed stromal tumors and gastric surgery requires proper conduct

Life and times:synthesis, trafficking, and evolution of VSG

Evasion of the acquired immune response in African trypanosomes is principally mediated by antigenic variation, the sequential expression of distinct variant surface glycoproteins (VSGs) at extremely high density on the cell surface. Sequence diversity between VSGs facilitates escape of a subpopulation of trypanosomes from antibody-mediated killing. Significant advances have increased understanding of the mechanisms underpinning synthesis and maintenance of the VSG coat. In this review, we discuss the biosynthesis, trafficking, and turnover of VSG, emphasising those unusual mechanisms that act to maintain coat integrity and to protect against immunological attack. We also highlight new findings that suggest the presence of unique or highly divergent proteins that may offer therapeutic opportunities, as well as considering aspects of VSG biology that remain to be fully explored

The N-Myc Down Regulated Gene1 (NDRG1) Is a Rab4a Effector Involved in Vesicular Recycling of E-Cadherin

Cell to cell adhesion is mediated by adhesion molecules present on the cell surface. Downregulation of molecules that form the adhesion complex is a characteristic of metastatic cancer cells. Downregulation of the N-myc down regulated gene1 (NDRG1) increases prostate and breast metastasis. The exact function of NDRG1 is not known. Here by using live cell confocal microscopy and in vitro reconstitution, we report that NDRG1 is involved in recycling the adhesion molecule E-cadherin thereby stabilizing it. Evidence is provided that NDRG1 recruits on recycling endosomes in the Trans Golgi network by binding to phosphotidylinositol 4-phosphate and interacts with membrane bound Rab4aGTPase. NDRG1 specifically interacts with constitutively active Rab4aQ67L mutant protein and not with GDP-bound Rab4aS22N mutant proving NDRG1 as a novel Rab4a effector. Transferrin recycling experiments reveals NDRG1 colocalizes with transferrin during the recycling phase. NDRG1 alters the kinetics of transferrin recycling in cells. NDRG1 knockdown cells show a delay in recycling transferrin, conversely NDRG1 overexpressing cells reveal an increase in rate of transferrin recycling. This novel finding of NDRG1 as a recycling protein involved with recycling of E-cadherin will aid in understanding NDRG1 role as a metastasis suppressor protein