This readme contains the latest information regarding the installation and use of Autodesk 3ds Max 2011 / Autodesk 3ds Max Design 2011 Hot Fix 4. It is strongly recommended that you read this entire document before installing this Hot Fix. For reference, you should save this readme to your hard drive. For complete installation and networking instructions refer to the Installation Guide on the DVD or with the electronic download of 3ds Max 2011 / 3ds Max Design 2011.
NOTE: This hot fix includes the fixes found in the Autodesk 3ds Max 2011 / Autodesk 3ds Max Design 2011-2010.06.07, 2010.07.21 Hot Fixes, and the Autodesk 3ds Max 2011 / Autodesk 3ds Max Design 2011 Service Pack 1, but you can install this hot fix without having to remove the previous hot fixes or Service Pack.
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* Denotes fixes from previous hot fix: Autodesk 3ds Max 2011 / Autodesk 3ds Max Design 2011-2010.06.07 Hot Fix. ** Denotes fixes from previous hot fix: Autodesk 3ds Max 2011 / Autodesk 3ds Max Design 2011-2010.07.21 Hot Fix.
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In this course, you will learn from several Digital-Tutors instructors, each of whom will be guiding you through a portion of the entire production pipeline used to create our final motion graphics piece. We will begin by exploring many of the powerful modeling tools found in 3ds Max, and utilizing these tools to build our 3D motion graphics elements. From there, we will look at a number of animation tools and features that will allow us to quickly add movement and life to our 3ds Max elements. Next, we will learn how to incorporate dynamic elements such as flowing cloth, smoke, and sparks into our scene. Finally, we will learn how to render out our elements from 3ds Max, and utilize After Effects for final compositing and sweetening of our motion graphics spot. Software required: 3ds Max 2011, MatchMover, After Effects CS4.
In the biological sciences, microscopy is generally used to translate information contained within a tissue or cell specimen into a more useful format, such as a processed digital image. Although the degradation of data due to imaging and image analysis is generally well understood and accepted (North, 2006), the loss of information resulting from image selection and presentation is frequently ignored. Certainly, one of the key strengths of high-throughput automated and quantitative imaging studies is that they circumvent these issues and allow the presentation of large quantities of data without interference from direct bias (Zhan et al., 2015). Many imaging workflows, however, are not amenable to such approaches. Imaging of Plasmodium falciparum merozoites caught during invasion of the human erythrocyte (Boyle et al., 2010b; Riglar et al., 2011) is one such example.
Given these challenges we sought to adapt our methods for analysing images of invading merozoites (Boyle et al., 2010b; Riglar et al., 2011), developing a computational workflow towards a more quantitative and unbiased determination of protein distribution during the process of merozoite invasion. In particular, we focussed on the longitudinal distribution of proteins with respect to the tight junction as the merozoite enters the erythrocyte and on the variability shown across individual parasites.
As a first test to explore the reproducibility and utility of this workflow, invading merozoites were labelled using rabbit antiserum and mouse monoclonal antibodies, both raised against RON4 (Richard et al., 2010) [Fig. 1C; Fig. S1A; total number of merozoites analysed (n)=16]. A single, clear RON4 peak was present in almost every line profile, particularly using the monoclonal, confirming the successful realignment of data in the z-direction. Peak intensity in both channels was consistently found at the same point (e.g. Fig. 1C right panel, example parasites 4 and 11). As previously observed (Riglar et al., 2011), background fluorescence was noticeable in many samples labelled using rabbit RON4 antisera, both within the parasite and within the erythrocyte, visible as a higher normalised baseline labelling than that of the monoclonal and in common spurious minor peaks within intensity profiles (e.g. Fig. 1C right panel, example parasites 13 and 16). In all cases the intensity profile matched features that were visible within the 3D image stacks (data not shown), although these were not always apparent in individual slices, such as those depicted in the example merozoite figures displayed in the right panel of Fig. 1C.
To readdress the population-level variability of AMA1 localisation during P. falciparum merozoite invasion using longitudinal intensity profiling, invading merozoites were co-labelled with either the RON4 monoclonal (Richard et al., 2010) and rabbit AMA1 antiserum (Healer et al., 2002) or rabbit RON4 antiserum (Richard et al., 2010) with the well-characterised AMA1 1F9 monoclonal antibodies (Coley et al., 2001). Consistent with our previous results (Riglar et al., 2011), rabbit AMA1 antiserum labelled a portion of AMA1 localised directly within the RON4 annulus in all parasites imaged (Fig. 2A; Fig. S1B; n=19). This junctional AMA1 population varied in its relative intensity compared with global AMA1 labelling, which regularly included apical and broad surface localisations. Importantly, however, even the lowest level of junctional AMA1 labelling was appreciably higher than background labelling levels (e.g. Fig. 2A, example 14).
In contrast to polyclonal labelling, AMA1 1F9 monoclonal antibodies showed a clear absence of signal within the RON4-demarked tight junction, evident as local minima in intensity profiles across the parasites imaged (Fig. 2B; Fig. S1C; n=17). AMA1 1F9 antibodies bind the hydrophobic cleft of the AMA1 molecule (Coley et al., 2007), the same groove with which RON2 interacts (Lamarque et al., 2011; Tyler and Boothroyd, 2011). When viewed together, these results suggest that AMA1 is indeed present at the tight junction of invading P. falciparum merozoites, with micronemal and surface populations also regularly present, but that selection of the appropriate immune label is critical. The 1F9 epitope is clearly masked by interactions occurring at the tight junction, most likely with RON2, under the fixation and permeabilisation conditions used. This is likely to explain the absence of labelling seen in previous studies with T. gondii as the result of epitope masking (Giovannini et al., 2011) and highlights the importance of using multiple antibodies to assign protein localisation, in particular polyclonal antibodies with multiple epitopes. Various attempts to expose masked epitopes, including heat, organic solvent based-permeabilisation (e.g. methanol or acetone) and other detergents (e.g. saponin) were unsuccessful. This remains an important limitation for the imaging of invading P. falciparum merozoites using our current method.
The possibility remained that the cleavage detected might derive from breakthrough or fast-growing parasites that had already undergone egress and invasion. To determine the dependency of cleavage on merozoite invasion and rupture, we treated parasites that had been synchronised simultaneously with those above with either the carbohydrate heparin, which is an inhibitor of merozoite invasion (Boyle et al., 2010a), or the cysteine protease inhibitor E-64, which is an inhibitor of merozoite egress (Salmon et al., 2001). These parasites were allowed to grow and rupture in the presence of the respective drugs before being harvested simultaneously with mixed ring-schizont samples (Fig. 6A, lanes 5 and 6). Strikingly, the presence of all major MTRAP cleavage products in drug-treated samples suggested that MTRAP cleavage occurred independently of both invasion and schizont rupture. Although it remains possible that MTRAP cleavage was occurring aberrantly from the surface of the heparin-inhibited merozoites that remained in the medium (Fig. 6A, lane 6), only low levels of breakthrough invasion were seen following E-64 treatment (data not shown), thus, making it highly unlikely that a similar degree of cleavage could be attributed to only those parasites escaping E-64 inhibition (Fig. 6A, lane 5).
Here we have tried to address image bias and biological variability in the application of 3D immunofluorescence microscopy to merozoite invasion. Using recent advances in methods to capture and image the process of invasion of erythrocytes (Boyle et al., 2010b; Riglar et al., 2011), we have developed a workflow that creates a standardisation of imaging events permitting comparisons between samples to assess the distribution of various test proteins during invasion. Although at the resolution limits of deconvolution microscopy, our goal has been to provide a workflow that is possible to replicate using best-practice fluorescence imaging on a conventional widefield microscope. The imaging requires no more than the ability to make a 3D z-stack and access to good antibodies (Bordeaux et al., 2010). Using this approach, which we refer to as longitudinal intensity profiling, we assessed whether the fluorescence signals associated with labelled proteins on the invading merozoite fit with the hypothesized function of these proteins. 2ff7e9595c
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