Anni Albers: Pre-Columbian Resonances: The Significance of Pre-Columbian Art in Her Textiles and Writings

Anni Albers is known primarily for her contribution to the Bauhaus Weaving Workshop and her woven prototypes for industrial production; she has rarely been acknowledged for her role in reviving and redefining the Pre-Columbian fiber art tradition. She researched, analyzed, collected, and extensively wrote about Pre-Columbian textiles. Her seminal text, On Weaving, 1965, is not only dedicated to Andean weavers, my great teachers, the weavers of ancient Peru, but is essentially a textbook of Andean weaving techniques, revived and meticulously analyzed by Albers. Furthermore, she and her husband, Josef Albers, amassed an important collection of ancient Mesoamerican sculpture, acquired during and after their many trips to that region. Her pictorial weavings, (a term she preferred over tapestry, even though they are not pictorial in a figurative or narrative way), and her industrial designs (often one and the same) owe a great deal to the monuments of ancient Mesoamerica as well as to Andean textiles. The technical and philosophical approaches that Albers developed at the Bauhaus from 1922 to 1933 are surprisingly similar to those of her Pre-Columbian counterparts; both succeeded in maintaining the inherent truth to their chosen materials, and in creating a visual grammar based on abstraction. Although Albers doubtless had knowledge of the extensive non-western collections in Munich and Berlin, it was only after her emigration to the United States in 1933 and her subsequent direct contact with Pre-Columbian art and source material that her theories were fully refined. This paper will illuminate and analyze some of the essential issues shared by Albers and her Pre-Columbian counterparts

Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma

SummaryWe report a comprehensive molecular characterization of pheochromocytomas and paragangliomas (PCCs/PGLs), a rare tumor type. Multi-platform integration revealed that PCCs/PGLs are driven by diverse alterations affecting multiple genes and pathways. Pathogenic germline mutations occurred in eight PCC/PGL susceptibility genes. We identified CSDE1 as a somatically mutated driver gene, complementing four known drivers (HRAS, RET, EPAS1, and NF1). We also discovered fusion genes in PCCs/PGLs, involving MAML3, BRAF, NGFR, and NF1. Integrated analysis classified PCCs/PGLs into four molecularly defined groups: a kinase signaling subtype, a pseudohypoxia subtype, a Wnt-altered subtype, driven by MAML3 and CSDE1, and a cortical admixture subtype. Correlates of metastatic PCCs/PGLs included the MAML3 fusion gene. This integrated molecular characterization provides a comprehensive foundation for developing PCC/PGL precision medicine

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis