A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting.

Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy issues. In this study, we introduce a novel hierarchical neural network that utilizes recursive PointConv operations on nested subdivisions of point sets.

Glia maturation factor gamma (GMFG): a cytokine-responsive protein during hematopoietic lineage development and its functional genomics analysis.

Human hematopoiesis was evaluated using the techniques of controlled stem cell differentiation, two-dimensional gel electrophoresis-based proteomics, and functional genomics. We provide the first report that glia maturation factor gamma (GMFG) is a cytokine-responsive protein in erythropoietin-induced and granulocyte-colony stimulating factor-induced hematopoietic lineage development. Results from global functional genomics analysis indicate that GMFG possesses several other features: hematopoietic tissue-specific gene expression, a promoter concentrated with high-score hematopoiesis-specific transcription factors, and possible molecular coevolution with a rudimentary blood/immune system.

Pseudo-periodic partitions of biological sequences.

Motivation: Algorithm development for finding typical patterns in sequences, especially multiple pseudo-repeats (pseudo-periodic regions), is at the core of many problems arising in biological sequence and structure analysis. In fact, one of the most significant features of biological sequences is their high quasi-repetitiveness. Variation in the quasi-repetitiveness of genomic and proteomic texts demonstrates the presence and density of different biologically important information.

Discovering simple regions in biological sequences associated with scoring schemes.

Let A denote an alphabet consisting of n types of letters. Given a sequence S of length L with v(i) letters of type i on A, to describe the compositional properties and combinatorial structure of S, we propose a new complexity function of S, called the reciprocal complexity of S, as C(S) = (i=1) product operator (n) (L/nv(i))(vi) Based on this complexity measure, an efficient algorithm is developed for classifying and analyzing simple segments of protein and nucleotide sequence databases associated with scoring schemes. The running time of the algorithm is nearly proportional to the sequence length.