Re: Vector NTI Advance 11.5 -RECOiL
Matthew Pendergrass
Adding will only work with random access iterators. std::advance will work with all sorts of iterators. As long as you're only dealing with iterators into vectors, it makes no real difference, but std::advance keeps your code more generic (e.g. you could substitute a list for the vector, and that part would still work).
Since only random access iterators provide + and - operators, the library provides two function templates advance and distance. These function templates use + and - for random access iterators (and are, therefore, constant time for them); for input, forward and bidirectional iterators they use ++ to provide linear time implementations.
It depends on the iterator. it=it+5 is faster if it's supported (it's only supported on random access iterators). If you want to advance a less-capable iterator (e.g. a forward iterator, or a bidirectional iterator), then you can use std::advance, but it's slower because it actually walks across all of the intermediate elements.
Yet it is efficient: std::advance will do an optimisation if it passed an RandomAccessIterator (like one from std::vector) and will increase iterator in loop for ForwardAccessIterator (as like one in std::list).
As a general rule, I don't worry about changing container types because I've found that when I do have to change a container type, I end up revisiting everywhere that container is used anyway, just to be sure I'm not doing anything that's suddenly stupid (like randomly plucking elements out of the middle of a list).
Gene delivery vehicles have helped realize the concept of treating human diseases by introducing normal alleles of genes into appropriate target cells. These gene delivery vehicles include recombinant and nonrecombinant lentiviral vectors and adeno-associated virus (AAV) vectors.
Recombinant retroviral vectors have been used in clinical trials for nearly three decades. Initial results with retroviral vectors were encouraging,1,2 but the use of these vectors in nonhuman primate studies was reported to lead to T-cell lymphoma.3 These vectors were also implicated in the development of T-cell leukemia in several children who received gene therapy for X-linked severe combined immunodeficiency in clinical trials.4,5
Adenoviral vectors in gene therapy of cystic fibrosis were reported to lack efficacy.6 Also, an adenoviral vector was suspected of playing a role in the death of a patient in a trial for gene therapy of ornithine transcarbamylase deficiency.7
Despite the good safety profile of third-generation lentiviral vectors in gene therapy trials, the insertion of vector genomes in transcriptionally active sites harbors an inherent genotoxic potential, especially in patients with preexisting acquired somatic mutations, and when ubiquitously active strong promoters are used, which can activate the expression of nearby genes. Development of optimized gene expression cassettes with high and restricted activity in differentiated or specialized cells is, therefore, an important safety feature of gene therapy vectors.
Although gene therapy with AAV vectors continues to be a promising treatment modality, it has also become increasingly clear that none of the first generation of AAV vectors currently in use is ideal for the following reasons:
In vivo evolution of large AAV libraries in combination with massive parallel screening of barcoded AAV vectors are now being actively employed to identify AAV vectors with further enhanced transduction properties and improved specificity.37,38
For therapeutic vector design, optimization of the size of the packaged therapeutic expression cassette can be achieved. Expression cassettes that are oversized (>5 kb) can result in the packaging of truncated genomes,41 whereas packaging of expression cassettes that are undersized can result in increased cross-packaging of plasmid-derived prokaryotic sequences that incorporate antibiotic resistance genes.42 Inclusion of large non-immunogenic inverted terminal repeat (ITR)-flanking spacer sequences significantly reduces unwanted packaging of plasmid-derived prokaryotic sequences.
The design, development, and production workflow for gene therapy vectors such as AAV is similar to the workflow for classical large-molecule biotherapeutics. Living producer systems that support an efficient, sustainable, and scalable growth environment for the vectors are desirable. Such systems can drive the development of the surrounding infrastructure for workflow management, automation, and regulatory compliance.
A commonly used cell system for AAV transient transfection and production is HEK293. For other producer biological model systems, either mammalian or insect cell lines have been used to scale up AAV sequences containing the transgene. For stable mammalian cell lines, either BHK cells or HeLa cells are used. With the insect system, Sf6 cells infected with recombinant baculovirus have been used as well to scale up recombinant AAV. With either of the above cell systems, production levels of 104 to 105 genome copies/L are obtained.
Viral vector characterization is also an important application in gene therapy. It facilitates the evaluation of critical quality attributes (CQAs) such as identity, potency, purity, and stability. To evaluate CQAs and comply with regulatory guidelines, companies engaged in gene therapy need to adopt reproducible and robust methods of development and validation.
A number of analytical techniques enable chemical and physical characterization. Viral structure and particle integrity and aggregation can be assessed with transmission electron microscopy (TEM), contributing to the quality control of viral vectors. TEM can also be used in combination with molecular techniques such as droplet digital PCR (ddPCR) or quantitative real time PCR (qPCR) to determine AAV identity and purity.
Recent attention has been focused on analytical ultracentrifugation and mass spectrometry as well. However, some of these technologies have limited throughput. Higher throughput microfluidic capillary electrophoresis platforms, such as the LabChip GXII Touch with the ProteinEXact assay, can enable AAV capsid protein (VP1, VP2, VP3) analysis.
Artificial intelligence and machine learning, along with analytical data management solutions, are significantly supporting analytical techniques to achieve automation, predictability, and decision making along the vector development, characterization, scale up, and QA/QC workflows.
Advanced tools, methods, applications, and services can facilitate the design, manufacture, and characterization of gene therapy vectors. End-to-end solutions can incorporate platforms for liquid handling and microfluidics; platforms for the analysis of macromolecules; platforms for cellular analysis and in vivo imaging; as well as platforms for bioinformatics applications. In addition, enhanced security software tools that enable 21 CFR Part 11 compliance to meet the regulatory requirements within the GMP lot release environment are critical.
Therapeutic gene and cell therapy research and development can progress more readily when advanced technologies and services for viral vector design and manufacturing are adopted. Such technologies, in combination with technologies for CRISPR-based gene editing, RNA interference, base editing, and prime editing, can move innovative therapeutics forward.
Anis H. Khimani, PhD, is the senior strategy leader for pharmaceutical development at PerkinElmer Life Sciences. Christian Thirion, PhD, is the CEO of Sirion Biotech. Arun Srivastava, PhD, is the George H. Kitzman Professor of Genetics at the University of Florida College of Medicine.
The issues are terminology of courses. Someone can technically say that calculus is real analysis. But it doesn't mean anything in terms of courses you take, books you read, etc. So the issue is somewhat of a terminology concern, not a formal mathematical one.
Calc 2 = integral calculus. Same thing as above but for integrals. Note, you may do a little baby diff EQs or series. And the border of differential and integral may not be 100% at the semester break. But close to it.
This pretty much finishes the curriculum for a basic science major. Engineers or physicists may have another semester or two of "math methods", which will be a whirlwind tour of partial differential equations, linear algebra, and perhaps complex analysis.
"Real analysis" is theoretical calculus. You prove a lot of the things you already learned in regular calculus. It's a math major course. Engineers, physicists, etc. won't bother taking it. You won't learn many new techniques that are useful to applied problems or following physics derivations (maybe a little in series).
Advanced calculus is another term for real analysis. Usually used in titles of older books. Usually a bit less emphasis on proofs and disdain for applications. But still mostly covering territory that is not that useful for applications.
Multi-variable calculus deals with properties of differentiable functions of more than one independent variable, and it can include the study of functions from $\BbbR^n \to \BbbR^mt$. Vector calculus studies the same functions but focuses on objects that have certain properties under linear transformations of variables. (And since it specializes in this way, vector calculus can in a beginning class afford to go deeper into subtle properties; for example, Greene's and Stokes' theorems.)
Vector calculus is in a very real sense a prelude to tensor calculus. Here is an example of an object you might well study in multi-variable calculus, but would not fit well with the methods of vector calculus. Let $f(x,y,z)$ be a sufficiently differentiable function of three real variables. Then let$$H(x,y,z) \in \BbbR^2 = \bigg( \frac\partial f\partial x,\frac\partial^2 f\partial y \partial z \bigg)$$$H$ does not meet any of the transformation properties we assume in vector calculus; yet it is a perfectly good (if boring) functional of $f$ we could study in multivariable calculus.
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