Local Style Awareness of Font Images

When we compare fonts, we often pay attention to styles of local parts, such as serifs and curvatures. This paper proposes an attention mechanism to find important local parts. The local parts with larger attention are then considered important. The proposed mechanism can be trained in a quasi-self-supervised manner that requires no manual annotation other than knowing that a set of character images is from the same font, such as Helvetica. After confirming that the trained attention mechanism can find style-relevant local parts, we utilize the resulting attention for local style-aware font generation. Specifically, we design a new reconstruction loss function to put more weight on the local parts with larger attention for generating character images with more accurate style realization. This loss function has the merit of applicability to various font generation models. Our experimental results show that the proposed loss function improves the quality of generated character images by several few-shot font generation models.Comment: Accepted at ICDAR WML 202

Towards Diverse and Consistent Typography Generation

In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document. We formulate typography generation as a fine-grained attribute generation for multiple text elements and build an autoregressive model to generate diverse typography that matches the input design context. We further propose a simple yet effective sampling approach that respects the consistency and distinction principle of typography so that generated examples share consistent typographic styling across text elements. Our empirical study shows that our model successfully generates diverse typographic designs while preserving a consistent typographic structure

Expression of steroidogenic enzymes and metabolism of steroids in COS-7 cells known as non-steroidogenic cells

Abstract The COS-7 (CV-1 in Origin with SV40 genes) cells are known as non-steroidogenic cells because they are derived from kidney cells and the kidney is defined as a non-steroidogenic organ. Therefore, COS-7 cells are used for transfection experiments to analyze the actions of functional molecules including steroids. However, a preliminary study suggested that COS-7 cells metabolize [3H]testosterone to [3H]androstenedione. These results suggest that COS-7 cells are able to metabolize steroids. Therefore, the present study investigated the expression of steroidogenic enzymes and the metabolism of steroids in COS-7 cells. RT-PCR analyses demonstrated the expressions of several kinds of steroidogenic enzymes, such as cytochrome P450 side-chain cleavage enzyme, 3β-hydroxysteroid dehydrogenase/Δ5-Δ4 isomerase, cytochrome P450 7α-hydroxylase, cytochrome P450 17α-hydroxylase/17,20-lyase, 17β-hydroxysteroid dehydrogenase, 5α-reductase, cytochrome P450 21-hydroxylase, cytochrome P450 11β-hydroxylase, and cytochrome P450 aromatase in COS-7 cells. In addition, steroidogenic enzymes 3β-HSD, P4507α, 5α-reductase, P450c17, P450c21, P450c11β, and 17β-HSD actively metabolized various steroids in cultured COS-7 cells. Finally, we demonstrated that 17β-HSD activity toward androstenedione formation was greater than other steroidogenic enzyme activities. Our results provide new evidence that COS-7 cells express a series of steroidogenic enzyme mRNAs and actively metabolize a variety of steroids

Mutation profiles of diffuse large B‐cell lymphoma transformation of splenic B‐cell lymphoma/leukemia, unclassifiable on whole‐exome sequencing

Abstract A 58‐year‐old male was diagnosed with splenic B‐cell lymphoma/leukemia, unclassifiable (SPLL‐U). The lymphoma transformed into diffuse large B‐cell lymphoma (DLBCL), and multidrug chemotherapy and autologous stem cell transplantation achieved complete remission. Two years later, the lymphoma relapsed as SPLL‐U. Serial whole‐exome sequencing indicated that the mutation profiles were similar between the onset and relapsed samples while those in DLBCL were partially distinctive, which was in line with the clinical course. Hierarchical clustering revealed that an IGLL5 mutation was the founder mutation proceeding the development of the diseases and suggested that KRAS and other mutations might contribute to the transformation