Myostatin-2 gene structure and polymorphism of the promoter and first intron in the marine fish Sparus aurata: evidence for DNA duplications and/or translocations

<p>Abstract</p> <p>Background</p> <p>Myostatin (MSTN) is a member of the transforming growth factor-ß superfamily that functions as a negative regulator of skeletal muscle development and growth in mammals. Fish express at least two genes for <it>MSTN</it>: <it>MSTN-1 </it>and <it>MSTN-2</it>. To date, <it>MSTN-2 </it>promoters have been cloned only from salmonids and zebrafish.</p> <p>Results</p> <p>Here we described the cloning and sequence analysis of <it>MSTN-2 </it>gene and its 5' flanking region in the marine fish <it>Sparus aurata </it>(sa<it>MSTN-2</it>). We demonstrate the existence of three alleles of the promoter and three alleles of the first intron. Sequence comparison of the promoter region in the three alleles revealed that although the sequences of the first 1050 bp upstream of the translation start site are almost identical in the three alleles, a substantial sequence divergence is seen further upstream. Careful sequence analysis of the region upstream of the first 1050 bp in the three alleles identified several elements that appear to be repeated in some or all sequences, at different positions. This suggests that the promoter region of sa<it>MSTN-2 </it>has been subjected to various chromosomal rearrangements during the course of evolution, reflecting either insertion or deletion events. Screening of several genomic DNA collections indicated differences in allele frequency, with allele 'b' being the most abundant, followed by allele 'c', whereas allele 'a' is relatively rare. Sequence analysis of sa<it>MSTN-2 </it>gene also revealed polymorphism in the first intron, identifying three alleles. The length difference in alleles '1R' and '2R' of the first intron is due to the presence of one or two copies of a repeated block of approximately 150 bp, located at the 5' end of the first intron. The third allele, '4R', has an additional insertion of 323 bp located 116 bp upstream of the 3' end of the first intron. Analysis of several DNA collections showed that the '2R' allele is the most common, followed by the '4R' allele, whereas the '1R' allele is relatively rare. Progeny analysis of a full-sib family showed a Mendelian mode of inheritance of the two genetic loci. No clear association was found between the two genetic markers and growth rate.</p> <p>Conclusion</p> <p>These results show for the first time a substantial degree of polymorphism in both the promoter and first intron of <it>MSTN-2 </it>gene in a perciform fish species which points to chromosomal rearrangements that took place during evolution.</p

Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model

Gaussian Mixture Models (GMM) are one of the most potent parametric density estimators based on the kernel model that finds application in many scientific domains. In recent years, with the dramatic enlargement of data sources, typical machine learning algorithms, e.g. Expectation Maximization (EM), encounters difficulty with high-dimensional and streaming data. Moreover, complicated densities often demand a large number of Gaussian components. This paper proposes a fast online parameter estimation algorithm for GMM by using first-order stochastic optimization. This approach provides a framework to cope with the challenges of GMM when faced with high-dimensional streaming data and complex densities by leveraging the flexibly-tied factorization of the covariance matrix. A new stochastic Manifold optimization algorithm that preserves the orthogonality is introduced and used along with the well-known Euclidean space numerical optimization. Numerous empirical results on both synthetic and real datasets justify the effectiveness of our proposed stochastic method over EM-based methods in the sense of better-converged maximum for likelihood function, fewer number of needed epochs for convergence, and less time consumption per epoch

Susd2 et Susd4 sont deux nouveaux gènes codant pour des protéines avec domaines CCP (Complement Control Protein) jouant un rôle dans plusieurs étapes du développement des circuits neuronaux au sein de cultures d'hippocampe de rat

During brain development, several steps precisely coordinated lead to establishment of a functional neuronal network. Many molecules participate to this process, including adhesion proteins mediating interactions between neurons and their environment. Involvement of numerous genes coding for adhesion proteins in neuropsychiatric diseases such as autism argue for usefulness of identifying new ones. During my PhD, I characterized two new genes, Sud2 and Susd4, coding for proteins containing CCP domains (Complement Control Protein), classically described in proteins involved in Complement regulation system. Recently, in mammals, CCP containing proteins were shown to be involved in neuronal development. Identification of several predicted CCP containing proteins without a known function prompted me to characterize Susd2 and Susd4 which are part of them.Susd2 is expressed in neurons from hippocampal cell cultures. Its peak of expression takes place in early post natal period, suggesting a developmental function. Susd2 recombinant protein has a diffuse neuronal localization, but is particularly enriched in excitatory synapses. Decreased expression of Susd2 leads to decreased axonal growth, increased dendritic growth, and specific inhibition of excitatory synaptogenesis. Susd4 is also expressed in neurons, with a peak of expression during embryonic development, and seems to act as a regulator of dendritic growth.Le développement cérébral est une succession d'étapes aboutissant à l'établissement d'un réseau neuronal. Il fait intervenir de nombreuses molécules comme des protéines d'adhésion permettant l'interaction des neurones avec leur environnement. L'implication de nombreux gènes codant des protéines d'adhésion dans la physiopathologie de maladies neuropsychiatriques comme l'autisme souligne l'intérêt à en identifier de nouveaux. Pendant ma thèse, j'ai pu caractériser deux nouveaux gènes, Susd2 et Susd4, codant des protéines contenant des domaines CCP (Complement Control Protein), classiquement connus pour leur présence dans les protéines participant à la régulation du système du Complément. Récemment, des protéines à domaines CCP ont été décrites chez la souris comme ayant une fonction dans le développement neuronal. L'existence de nombreuses protéines prédites à domaines CCP sans fonction connue m'ont conduit à tenter de caractériser Susd2 et Susd4 qui en font partie.Susd2 est exprimé dans les neurones au sein de cultures de cellules d'hippocampe de rat. Son expression atteint un pic à un stade post natal précoce, suggérant une fonction développementale. La protéine Susd2 recombinante a une localisation neuronale diffuse, mais est particulièrement enrichie dans les synapses excitatrices. La diminution de l'expression de Susd2 a pour conséquences un défaut de croissance axonale, une augmentation de la croissance dendritique, et une inhibition spécifique de la synaptogénèse excitatrice. Susd4 est également exprimé dans les neurones, avec un pic d'expression au stade embryonnaire, et semble jouer un rôle de régulation du développement dendritique

Simpuru: Gamifikasi Pembelajaran Bahasa Jepang dalam Aplikasi Berbasis Web

Berdasarkan hasil survei lembaga The Japan Foundation pada tahun 2018, Indonesia menduduki peringkat kedua sebagai negara dengan jumlah pelajar bahasa Jepang terbanyak. Meskipun demikian, ditemukan bahwa terdapat permasalahan dalam pembelajaran di Indonesia, yaitu pelajar tingkat pemula (level N5 dan N4) merasa takut untuk berkomunikasi menggunakan bahasa Jepang dan mengalami kebosanan. Pelajar juga kesulitan untuk menguasai Kanji dikarenakan jumlah huruf yang banyak serta tidak terbiasa dengan huruf tersebut. Untuk mengatasi permasalahan yang dihadapi pelajar tersebut, maka dirancanglah Simpuru yang merupakan aplikasi pembelajaran bahasa Jepang berbasis web dengan menggunakan teknik gamifikasi. Integrasi gamifikasi ke dalam pembelajaran dapat membantu membuat pelajaran menjadi lebih menyenangkan, membantu mengukur seberapa jauh pelajar menguasai suatu materi dan menyesuaikan tingkat latihan dengan tingkat penguasaan materi pelajar. Aplikasi dikembangkan dengan menggunakan metode Extreme Programming. Dari perancangan menghasilkan sebuah aplikasi pembelajaran bahasa Jepang dimana pelajar dapat memilih untuk mempelajari bahasa Jepang dengan flashcards/video atau mengerjakan soal latihan. Pelajar akan mendapatkan points, badges/trophy dan avatar setelah berhasil menyelesaikan latihan. Pelajar juga dapat melihat peringkat dirinya maupun temannya di Leaderboards

The Selection of Outstanding Teachers to the Determination of Ranking on Professional and Intellectual Managerial Performances

The study aims to analyze the selection of outstanding teachers to the determination ranking on professional and intellectual managerial performances. A qualitative research was used where 91 teachers were selected based on the level of education from kindergarten to secondary school and vocational education.  The results showed there are three data categories of professional and managerial performances of teachers, namely, high, medium and low. The three categories of data describe the various factors that affect the ability of the management professional and intellectual teachers among the demands of interpersonal of the profession, carrying out the task of teaching a high quality produces professional development, encouragement and motivation from leaders in the school, and a special confidence in the development of professional, as well as using the four dimensions (the assessment of emotion-self, appraisal of emotions of others, use of emotion, and emotion regulation) in improving the performance of teachers and the determination of the personal goals of the teacher in the academic achievement

Bayesian Dynamic DAG Learning: Application in Discovering Dynamic Effective Connectome of Brain

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization \textbf{(BDyMA)} method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover bidirected edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and baseline methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process