Representasi Superhero Dalam Film X-Men: the Days of the Future Past

Penelitian ini untuk mengetahui bagaimana representasi figur superhero dan villain dalam Film X-Men: Days of The Future Past dan mengetahui mengapa karakteristik superhero dan villain dalam X-Men: Days of The Future Past divisualkan menggunakan citraan yang tidak mengikuti ‘tradisi\u27 superhero Amerika. Hal ini dilatarbelakangi karena munculnya banyak film-film superhero yang sangat menonjolkan kekuatan dan superioritas Amerika terhadap musuh-musuh ‘historisnya\u27. Film X-Men The Days of The Future Past tidak lagi menunjukkan figur superhero ikonik khas Amerika dengan bintang dan warna putih biru mengacu bendera Amerika, atau memvisualkan villain sebagaimana musuh dalamhistoris Amerika (Nazi, komunis Rusia-Cina, dan teroris timur tengah). Metode penelitian yang digunakan adalah deskriptif kualitatif dengan memahami bahwa film adalah bentuk hiperealitas yang didalamnya menjadi suatu representasi. Semiologi Roland Barthes dipakai sebagai cara yang memudahkan dalam menganalisis, dimana didalamnya terdapat denotatif, konotatif, dan mitos. Teknik menganalisis dilakukan dengan cara mengumpulkan data tentang film X-Men The Days of The Future Past kemudian dianalisis menggunakan Teori Praktik Pierre Bourdieu yakni habitus: nilai–nilai yang terinternalisasi dalam individu atau kelompok; Kapital: potensi yang dimiliki oleh individu atau kelompok untuk mendapatkan kesempatan di arena; Arena: sebuah tempat dimana didalamnya terdapat berbagai habitus dan kapital yangbersaing; Distinction: sebuah pembeda yang dilakukan untuk menunjukan kelas yang berbeda; Dominasi simbolik: kapital simbolik yang digunakan untuk menindas orang lain; Doxa: pandangan penguasa yang dianggap sebagai kebenaran. Dari deskripsi yang dianalisis menggunakan teori akan memunculkan pemaknaan mengenai film X -Men The Days of The Future Past

Dynamic Cell Model with Cellular Signaling Network and Mechanical Forces for Tissue Pattern Formation

Cells are the basic functional elements of living bodies. Cell-cell and cell-environment interactions largely maintain and regulate the processes of tissue formation and tissue regeneration, which involve collective cell migration and proliferation at large scale. Understanding the mechanisms behind cellular physiological processes such as embryo development, wound healing, and tumor metastasis requires study of cell-cell and cell-environment interactions, and their effects on cellular behaviors. As many underlying subcellular processes such as the generation of physical forces by cytoskeleton and transmitted mechanical forces through intercellular adhesion are difficult to access through direct experiments, computational cell model is useful for gaining insight into the mechanisms of cellular processes and aid in design of further investigations. A number of computational cell models have been developed to study cellular processes. However, all have limitations. They either lack accurate descriptions of cell shapes or cell mechanics, or have limited flexibility in modeling cell movements. These limitations prevent effective modeling of dynamic changes in cell shapes and mechanics in biological processes involving large scale cell migration. Here I develop a novel computational cell model called dyCelFEM. It accounts for detailed changes in cellular shapes and mechanics of individual cells in a large population of interacting cells. In addition, it can model the full range of cell motions, from free movement of individual cells to large scale collective cell migration. Furthermore, the transmission of mechanical forces via intercellular adhesion and its rupture is also modeled. With the intercellular protein signaling networks embedded in individual cells, biochemical control of cell behaviors can also be modeled. The dyCelFEM model is then employed to study two cellular processes, namely, the wound healing and cell movement on ECM. Wound healing is a complex process to repair the injured tissue through the communication and collaboration of multiple different types of cells and multiple growth factors and cytokines. Due to its complexity, the underlying cellular mechanisms, such as how the large scale collective cell motions during wound healing are regulated by different type of signals, are still not fully understood. Here I studied the effects of both biochemical and mechanical cues in regulating human skin wound healing and explored their roles in determining the tissue patterns. The cell movement under the effect of cell-ECM interaction is a process of environment sensing of living cell through cellular interaction. In the past decade, it has been found that cell behaves in response to a variety of physical cues from environment through the cell-ECM adhesions. Here I studied the specific role of the ECM geometry on regulating cell elongation and directing cell migration. The overall findings from this study establishes quantitative biological relevance of biochemical and mechanical effects on wound healing and effect of cell-ECM interaction on cell movement. It leads to a better understanding of the mechanisms behind complex processes of wound healing and cell movement on ECM

Fusion of cells and tissues.

<p>(a) and (b) shows two separate growing cells come into contact and become fused together. A new edge is formed between the two cells. (c) and (d) shows the case of three growing cells fusion. Three new edges and a new vertex are formed after fusion. (e) and (f) shows the fusion of two growing tissues. Two separate tissues contact with each other and become fused together to form a continuous tissue. The new edges and vertices formed are highlighted.</p

Tension force and pressure.

<p>Forces applied to the junction vertex of three cells <i>a</i>, <i>b</i>, and <i>c</i>. The <i>tension force</i> <b><i>T</i></b>(<b><i>e</i></b>_{<i>i</i>,<i>j</i>}) exerts along the direction <b><i>e</i></b>_{<i>i</i>,<i>j</i>} of an inner edge (interior cell boundary), or along the tangent direction of outer edge <b><i>e</i></b>_<i>i</i> (free cell boundary), where (<i>i</i>,<i>j</i>) are the two indices of cells <i>a</i>,<i>b</i>, or <i>c</i>. The <i>pressure force</i> <b><i>P</i></b>(<b><i>e</i></b>_{<i>i</i>,<i>j</i>}) acts along the direction normal to the cell boundary, in the direction from the cell with higher pressure to that with lower pressure.</p

Algorithm 1. UpdateCellPattern (<b><i>V</i></b>(<i>t</i>), Δ (<i>t</i>), Δ<i>η</i>(<i>t</i>), <i>σ</i>, <i>k</i>).

<p>Algorithm 1. UpdateCellPattern (<b><i>V</i></b>(<i>t</i>), Δ (<i>t</i>), Δ<i>η</i>(<i>t</i>), <i>σ</i>, <i>k</i>).</p

Steady state spatial gradients of <i>Dl</i> of different diffusion rates.

<p>The steady state spatial gradients of <i>Dl</i> are due to diffusion and degradation. The red solid line with triangular markers is the steady state gradient formed with the diffusion coefficient 1.2 <i>μm</i>² <i>s</i>⁻¹. The green dash line with square markers is the gradient of the diffusion coefficient 4.8 <i>μm</i>² <i>s</i>⁻¹. The blue dotted line with circle markers is of the diffusion coefficient 9.6 <i>μm</i>² <i>s</i>⁻¹. The black straight lines represents the 0.05 <i>Dl</i> concentration threshold for cellular response.</p

Model of feedback circuits for tissue size control.

<p>a) Division types of stem cells and progenitor cells. Red sphere labeled with (S) indicates stem cells, blue hexagon (P) indicates progenitor cells, and white diamond (D) indicates differentiated cell. The same color code is used for illustration of resulting tissues. b) Feedback controls of stem cell model. Blue arrows indicate self-renewal or proliferation divisions. Black arrows indicate symmetric differentiation divisions. Red arrows indicate asymmetric divisions. Flat-head arrows extending from differentiated cell with corresponding colors indicate inhibitions to respective type of divisions.</p

Model of tissue development starting from a single cell.

<p>(a) A single cell and its plane of first division; (b) Two daughter cells after the first division, each is slight deformed from its shape in (a); (c) The formation of four cells after two cell divisions.</p

Bristle Plotting Puzzle.

<p>Simulation results of bristle pattern formation using different models. a) The pattern of gene expression used for the stripe models. Green stripes have almost equal expression of <i>Dl</i> and <i>N</i> genes but <i>ac</i> is not expressed. Blue stripes have high expression of <i>ac</i> and <i>Dl</i> genes but low expression of <i>N</i> gene. Red stripes have high expression of <i>ac</i> and <i>N</i> genes but low expression of <i>Dl</i> gene. Bristles only form in the blue stripes. b) Lateral inhibition with stripes does not ensure equal spacing or good alignment. c) Inhibition field with out stripes ensures proper spacing but does not produce a good alignment. d) Inhibition field with stripes produces equal spacing as well as good alignment.</p

Data Structure.

<p>The boundary of a cell is formed by connected edges. It is modeled as an oriented closed curve in the counterclockwise direction. Each physical inner edge is represented twice using two half-edges, once each in opposite directions for each of the two contacting cells. Each physical outer edge is also represented twice with two half-edges, once for the cell, and once for the outside space.</p