What does reflected onto means in the text below?

The pleura lines the thoracic wall and diaphragm, where it is known as the parietal pleura. It is reflected onto the lung, where it is called the visceral pleura.

"Reflected" here describes the relationship between the continuous, but differently named parts of the pleura. The parietal pleura lines the chest wall and mediastinum (costal, cervical, mediastinal, and diaphragmatic areas marked in the image below). The visceral pleura covers the surface of the lungs (orange area below). There is a transition point at the root of the lung where the parietal pleura and visceral pleura meet. This transition is referred to typically as a reflection.

I think that the terminology is used to emphasize that the layers are continuous, but it is as if the parietal pleura bounces back off the body wall as visceral pleura.

Although not a particularly clear choice of words, because we usually connote "reflection" with light, in this case, "reflected" refers to how the parietal pleura folds back on itself from the inner chest walls and diaphragm to line the lungs.

What does reflected onto means in the text below? - Biology

An active reading strategy for articles or textbooks is annotation. Think for a moment about what that word means. It means to add notes (an-NOTE-tate) to text that you are reading, to offer explanation, comments or opinions to the author’s words. Annotation takes practice, and the better you are at it, the better you will be at reading complicated articles.


In order to understand the living nature Systems Biology develops computational models of biological systems. These models are computational in the sense, that the models are expressed in an appropriate formal language, like the Systems Biology Markup Language (SBML, [1]) and CellML [2], and can be used by computer programs in order to infer statements about its dynamical behaviour (either quantitative or qualitative). In contrast to [3] we also call differential equation models “computational”.

We call a computational model of a biological system a bio-model if it allows for an explanation of the mechanism behind the observed behaviour of the biological system. Therefore the model not only has to imitate the behaviour of the system. In addition, the components of the model must possess a biological meaning with respect to the modelled system. Only if the model has both the same performance (the behaviour) and the same competence (the mechanism) as the biological system, we can understand the living system by means of the model [4].

Today’s high-quality and high-throughput experimentation techniques in molecular biology are the basis for an increasing number of bio-models with growing size and complexity. Understanding biological systems on the system-level requires the integration of bio-models from different abstraction levels and with different paradigms [5]. Obviously, modelling on a system-level will require the very assistance of computers. Although computational bio-models themselves are represented in some formal language their meaning often is only described in natural language. Computer-aided modelling in Systems Biology will be impossible until the meaning of the models is formally described. In this paper we introduce the meaning facets of bio-models which are views of a bio-model from different perspectives. The meaning facets provide a conceptual framework for a systematic specification of the meaning of a bio-model and consequently are the basis for rigorous semantics of the bio-model.

Formal semantics of bio-models which go beyond the usual formal specification of the model structure and comprehends all meaning facets would be desirable to provide computer support in the following tasks:

Semantics based search

Given certain desired model properties find models that exhibit these properties. For example, both example models discussed below should be retrievable by search queries of the types: “Find models describing the cell cycle!”, “Find models related to p34 protein kinase!”, or “Find models that exhibit both steady state and oscillating behaviour!”.

Model comparison

Given two models, do they semantically overlap? Is one model a sub-model of the other? Or is one of them an abstraction of the other? In general, a method for model comparison is needed for many higher level tasks like model matching or model integration. The comparison should apply to all perspectives of the model’s meaning (see below). A comparison of two models can have different kinds of results: e.g. identical, similar, competing, contradictory, or subsuming models.

Annotating models

The annotation of a model can be done in an interactive mode: Starting with some elementary facts about a model an interactive system (see below) infers more facts and asks for missing information. Thereby it suggests possible answers. Furthermore, the system complains about inconsistencies. The result is a complete and consistent annotation of the model.

Beside these tasks related to the storage, retrieval and exchange of models in a collaborative setting formal semantics could be the basis for computer-aided modelling. By means of automatic reasoning it would allow for higher-level tasks like:

Model integration

Given two models that semantically overlap, what would an integrated model look like? Again, the formal semantics of the model’s components is needed in order to automate this task.

Model use

In order to simulate and predict the behaviour of a biological system the bio-model has to be implemented in a computer code. This causes further problems: Without formal semantics a biologist must directly modify the code in order to change the model. If the extrinsic meaning of model components and their inter-dependencies with the intrinsic model structure were formalised, it would be possible to modify the model on a more abstract semantic level without the need to refer to the implementation.

Model revision

Given desired behaviours, is the actual dynamics of a model in accordance with them? The diagnostics of a potential discrepancy will suggest possible changes of the model. The corresponding improvement could be used iteratively to “evolve” models.

A formal semantic description of bio-models would not only be useful in corresponding computer-assisted application scenarios, but also would support biologists to access models, their use and their behaviour as well as the underlying assumptions and decisions. A formal description of the involved knowledge would allow to present relevant information about a model to biologists in a familiar way.

The biological scientist does not have to cope with this rather complicated formalisation of the semantics. We envision an interactive system for computer-aided annotation of bio-models. Based on a knowledge representation system working in the background this system can guide the user in entering all the necessary information while constantly checking the consistency of the resulting information. Furthermore, the system will be able to ask for specific kinds of information depending on the information already entered and can provide candidate answers to the user.

Lab report structure

Lab reports can vary in length and format. These range from a form to fill in and submit before leaving the lab, to a formal written report. However, they all usually follow a similar basic structure.



  • provides an overview of the report content, including findings and conclusions
  • usually the last part of the document to be written
  • may not be required in a short lab report


  • provides appropriate background to the experiment and briefly explains any relevant theories
  • states the problem and/or hypothesis and
  • concisely states the objective/s of the experiment


  • describes equipment, materials and procedure(s) used
  • may include flow charts of procedures and/or diagrams of experimental set-up
  • outlines any processing or calculations performed on the collected data (if applicable)

Results and Analysis

  • presents results of the experiment graphically or by using tables. Figures often include error bars where applicable
  • discusses how results were analysed, including error analysis


  • interprets key results in relation to the aims/research question
  • summarises key findings and limitations
  • makes recommendations to overcome limitations and indicate future directions in research


  • reminds the reader what problem was being investigated
  • summarises the findings in relation to the problem/hypothesis
  • briefly identifies big-picture implications of the findings (Answers the question "So What?")


  • lists the publication details of all sources cited in the text, allowing readers to locate sources quickly and easily
  • usually follows a specific referencing style


  • an appendix (plural = appendices) contains material that is too detailed to include in the main report, such as tables of raw data or detailed calculations

Click on the links below to find out more about the different sections of a lab report.


Your title needs to reflect the purpose of the experiment. Check with your demonstrator or lecturer for specific requirements.

PHS1022 Week 5 Laboratory

The Period of a Simple Pendulum


An abstract provides a brief overview of the experiment, including its findings and conclusions. In general the abstract should answer six questions:

  • Why was the experiment conducted? (big-picture/real-world view).
  • What specific problem/research question was being addressed?
  • What methods were used to solve the problem/answer the question?
  • What results were obtained?
  • What do these results mean?
  • How do they answer the overall question or improve our understanding of the problem?

The most important thing to remember when writing the abstract is to be brief and state only what is relevant. No extraneous information should be included. It also must be clear enough so someone who is unfamiliar with your experiment could understand why you did what you did, and the conclusions you reached, without needing to read the rest of the report.

An abstract is usually only one paragraph (200-300 words max).


An abstract should be written last (even though it appears as the first section in your report), as it summarises information from all the other sections of the report.


The Introduction should:

  • provide the context and motivation for the experiment
  • briefly explain relevant theory in sufficient detail
  • introduce any relevant laws, equations or theorems
  • clearly state the aim or research question that the experiment is designed to address.


  • Always write the introduction in your own words don&rsquot just copy from the lab notes.
  • Some brief lab reports do not require an introduction and will just begin with an aim/statement.
  • Always check with your lecturer/demonstrator if you&rsquore not sure what is expected.



The method section is where you describe what you actually did. It includes the procedure that was followed. This should be a report of what you actually did, not just what was planned. A typical procedure usually includes:

  1. How apparatus and equipment were set up (e.g. experimental set-up), usually including a diagram,
  2. A list of materials used,
  3. Steps used to collect the data,
  4. Any experimental difficulties encountered and how they were resolved or worked around.

If any aspects of the experimental procedure were likely to contribute systematic error to the data and results, point this out in sufficient detail in this section.

Experimental set-up and materials

Your description of the experimental set-up should be sufficient to allow someone else to replicate the experiment themselves. You will usually begin with a description of the materials used and/or the apparatus set-up accompanied by:

  • an image showing the relevant features of any object or material under investigation
  • a diagram of the experimental setup, with each component clearly labelled


When you carry out an experiment, you usually follow a set of instructions such as these, which may include extra information to guide you through the steps.

Lab handout example

Week 5 Laboratory instructions

  1. Use a clean pipette to measure 25ml of HCl(aq) into the conical flask.
  2. Rinse a burette with standardised NaOH(aq).
  3. Fill the burette to the 0.0ml marking with standardised NaOH(aq). Remember to take the reading from the centre of the meniscus, and from eye level. Record the actual reading in Table 1.
  4. Place a sheet of white paper under the burette. This is to make it easier to observe the colour change during the reaction.
  5. Place the conical flask onto the white paper.

Lab report example

The equipment was arranged as shown in Fig. 2.

25.0ml HCl(aq) was pipetted into a 100ml conical flask. A burette was clamped to a retort stand and filled with standardised NaOH(aq) and the initial measurement was recorded. The conical flask was placed below the burette, on top of a piece of white paper. Five drops of universal indicator solution were added to the flask.

Figure 2. Experimental set-up for titration (taken from Carroll 2017)

Lecturer's comment

When writing up the procedure, you must report what was actually done and what actually happened, and omit any extra information such as helpful hints included in the instructions. Your goal for this section should be to include enough detail for someone else to replicate what you did and achieve a similar outcome. You should also explain any modifications to the original process introduced during the experiment.


In the Procedure section you should use:

While most science units require that you report in the passive voice , some require the active voice . In the example below, the first person is used e.g. "we initiated". This is accepted in some disciplines, but not others. Check your unit information or talk to your unit coordinator.

Initiate the bicarbonate feed pump.

We initiated the bicarbonate feed pump. (active voice)

The bicarbonate feed pump was initiated. (passive voice)


Lecturers have different preferences for using active/passive voice and you will likely have to write in both voices. Read samples of student reports below and identify which examples are written in passive voice, and which use active voice.

Results and analysis

In this section, you present the main data collected during your experiment. Each key measurement needs to be reported appropriately. Data are often presented in graphs, figures or tables.

This section often also includes analysis of the raw data, such as calculations. In some disciplines the analysis is presented under its own heading, in others it is included in the results section. An analysis of the errors or uncertainties in the experiment is also usually included in this section.

Tables, graphs and figures

Most numerical data are presented using tables or graphs. These need to be labelled appropriately to clearly indicate what is shown.

Titles and captions

  • Tables should be labelled numerically as Table 1, Table 2, etc.
  • Everything else (graphs, images, diagrams etc.) is labelled numerically as Figure 1, Figure 2, etc. (References to figures in the main body of the text are usually written in abbreviated form, e.g. &lsquosee Fig. 1&rsquo).
  • Table captions appear above the table. Figure captions appear below the figure.

Note that in Fig. 3, above, the student has omitted error bars on the data points. For most experiments an error analysis is important, and errors should be included in tables and on graphs.

Also, it is always best to draw figures yourself if you can. If you do use figures from another source, indicate in the citation whether you have modified it in any way.

Data can be presented in other formats, such as images:


When showing calculations, it is usual to show the general equation, and one worked example. Where a calculation is repeated many times, the additional detail is usually included in an appendix. Check the requirements given in your unit information or lab manual, or ask your tutor if you are unsure where to place calculations.

In some disciplines, if formulae are used, it is common to number them as equations:

Lecturer's comment

In some schools, like Biology, calculations that are too detailed to go into the main body of the report can be added in an appendix. The purpose of such appendices is to present the data gathered and demonstrate the level of accuracy obtained.

A chromatogram was produced for the unknown compound U, and each of the known compounds, A-E. Rf values for each substance are listed in Table 1.

Table 1: Rf values for known compounds (A-E).

Note: U is the unknown compound.

Error analysis

As well as presenting the main findings of your experiment, it is important that you indicate how accurate your results are. This is usually done through determining the level of uncertainty. The sources of error that you need to consider will vary between experiments, but you will usually need to factor in both random and systematic errors. Your error analysis should identify the main causes of uncertainty in your measurements, note any assumptions, and show how you have calculated any error bars. Check with your demonstrator, tutor or lecturer if you are unsure about how to determine uncertainties or whether error bars are required for your experiment.


The discussion section is where you:

  • comment on the results you obtained
  • interpret what the results mean
  • explain any results which are unexpected.

Your discussion section should demonstrate how well you understand what happened in the experiment. You should:

  • identify and comment on any trends you have observed
  • compare the experimental results with any predictions
  • identify how any sources of error might impact on the interpretation of your results
  • suggest explanations for unexpected results, and
  • where appropriate, suggest how the experiment could have been improved.

The discussion example below is from a first-year Biology unit. The aim of this experiment was to identify decomposition rates of leaf breakdown to establish rates of energy transfer.

It was expected that the leaves would show a far higher rate of decomposition in the shore zone, where there are more chances for sediments to rub against them. However the two zones show no significant difference in leaf breakdown, although these results are non-conclusive due to the limitations of this experiment. The two zones of leaf decomposition were physically too close, and over the incubation period reeds were observed growing close to the limnetic zone. This may have negatively affected the accuracy of the results by reducing the differences in habitat at these sites, as seen in other experiments (Jones et al. 2017). The results also had large standard deviations, possibly due to these physical constraints or human error in weighing leaves. Further studies with more diverse zones and precise procedures should be undertaken in order to explore leaf decomposition and rates of energy transfer more effectively.


Drag each description of each component of the Discussion section to its example. Notice the order in which the components make up a coherent Discussion section.


The conclusion section should provide a take-home message summing up what has been learned from the experiment:

  • Briefly restate the purpose of the experiment (the question it was seeking to answer)
  • Identify the main findings (answer to the research question)
  • Note the main limitations that are relevant to the interpretation of the results
  • Summarise what the experiment has contributed to your understanding of the problem.

Lecturer's tip

In brief lab reports, the conclusion is presented at the end of the discussion, and does not have its own heading. This type of conclusion can also be thought of as the sentence that answers the question &ldquoSo what?&rdquo. Note that a conclusion should never introduce any new ideas or findings, only give a concise summary of those which have already been presented in the report.

Click the icons next to each paragraph to show the lecturer’s comments. Click again to hide the comment.



It is quite possible that you may have in-text citations in your lab reports. Typically these will be included in the introduction to establish evidence of background for current theories or topics. Your discussion section will often include in-text citations, to show how your findings relate to those in the published literature, or to provide evidence-based suggestions or explanations for what you observed.

When in-text citations are incorporated into your lab report, you must always have the full citations included in a separate reference list. The reference list is a separate section that comes after your conclusion (and before any appendices).

Check your lab manual or unit information to determine which referencing style is preferred. Carefully follow that referencing style for your in-text references and reference list. You can find examples and information about common referencing styles in the Citing and referencing Library guide.

The following is an example of a reference list based on the in-text citations used in the Introduction and Conclusion sections in this tutorial. It has been formatted in accordance with the CSIRO referencing style.


Jones T, Smith K, Nguyen P, di Alberto P (2017) Effects of habitat overlap on population sampling. Environmental Ecology Journal 75, 23-29. doi: 10.5432/1111.23

Tian M, Castillo TL (2016) Solar heating uptake in Australia: rates, causes and effects. Energy Efficiency Reports. Report no. 10, The Department of Sustainability and Environment, Canberra.


An appendix (plural = appendices) contains material that is too detailed to include in the main report, such as tables of raw data or detailed calculations.

  • given a number (or letter) and title
  • referred to by number (or letter) at the relevant point in the text.

Example text

The calculated values are shown in Table 3 below. For detailed calculations, see Appendix 1.

The evolution of life on Earth over the past 4 billion years has resulted in a huge variety of species. For more than 2,000 years, humans have been trying to classify the great diversity of life. The science of classifying organisms is called taxonomy. Classification is an important step in understanding the present diversity and past evolutionary history of life on Earth.

All modern classification systems have their roots in the Linnaean classification system. It was developed by Swedish botanist Carolus Linnaeus in the 1700s. He tried to classify all living things that were known at his time. He grouped together organisms that shared obvious physical traits, such as number of legs or shape of leaves. For his contribution, Linnaeus is known as the &ldquofather of taxonomy.&rdquo You can learn more about Linnaeus and his system of classification by watching the video at this link:

The Linnaean system of classification consists of a hierarchy of groupings, called taxa(singular, taxon). Taxa range from the kingdom to the species (see Figure below). The kingdom is the largest and most inclusive grouping. It consists of organisms that share just a few basic similarities. Examples are the plant and animal kingdoms. The species is the smallest and most exclusive grouping. It consists of organisms that are similar enough to produce fertile offspring together. Closely related species are grouped together in a genus.

Linnaean Classification System: Classification of the Human Species. This chart shows the taxa of the Linnaean classification system. Each taxon is a subdivision of the taxon below it in the chart. For example, a species is a subdivision of a genus. The classification of humans is given in the chart as an example.

Binomial Nomenclature

Perhaps the single greatest contribution Linnaeus made to science was his method of naming species. This method, called binomial nomenclature, gives each species a unique, two-word Latin name consisting of the genus name and the species name. An example is Homo sapiens, the two-word Latin name for humans. It literally means &ldquowise human.&rdquo This is a reference to our big brains.

Why is having two names so important? It is similar to people having a first and a last name. You may know several people with the first name Michael, but adding Michael&rsquos last name usually pins down exactly whom you mean. In the same way, having two names uniquely identifies a species.

Revisions in Linnaean Classification

Linnaeus published his classification system in the 1700s. Since then, many new species have been discovered. The biochemistry of many organisms has also become known. Eventually, scientists realized that Linnaeus&rsquos system of classification needed revision.

A major change to the Linnaean system was the addition of a new taxon called the domain. Adomain is a taxon that is larger and more inclusive than the kingdom. Most biologists agree there are three domains of life on Earth: Bacteria, Archaea, and Eukaryota (see Figure below). Both Bacteria and Archaea consist of single-celled prokaryotes. Eukaryota consists of all eukaryotes, from single-celled protists to humans. This domain includes the Animalia (animals), Plantae (plants), Fungi (fungi), and Protista (protists) kingdoms.

This phylogenetic tree is based on comparisons of ribosomal RNA base sequences among living organisms. The tree divides all organisms into three domains: Bacteria, Archaea, and Eukarya. Humans and other animals belong to the Eukarya domain. From this tree, organisms that make up the domain Eukarya appear to have shared a more recent common ancestor with Archaea than Bacteria.

The Difference Between R-Squared and Adjusted R-Squared

R-Squared only works as intended in a simple linear regression model with one explanatory variable. With a multiple regression made up of several independent variables, the R-Squared must be adjusted. The adjusted R-squared compares the descriptive power of regression models that include diverse numbers of predictors. Every predictor added to a model increases R-squared and never decreases it. Thus, a model with more terms may seem to have a better fit just for the fact that it has more terms, while the adjusted R-squared compensates for the addition of variables and only increases if the new term enhances the model above what would be obtained by probability and decreases when a predictor enhances the model less than what is predicted by chance. In an overfitting condition, an incorrectly high value of R-squared is obtained, even when the model actually has a decreased ability to predict. This is not the case with the adjusted R-squared.

What does reflected onto means in the text below? - Biology

What are the pedagogical and physiological foundations of reflection for learning? Why is reflection important for learning? What does the literature say about how reflection supports learning?

Learning/Process Portfolios involve the focus on Plato’s directive, “know thyself” which can lead to a lifetime of investigation. Self-knowledge becomes an outcome of learning. John Zubizaretta (2004, 2009), in his insightful books on Learning Portfolios in higher education, describes the primary motive of a learning portfolio: “to improve student learning by providing a structure for students to reflect systematically over time on the learning process and to develop the aptitudes, skills and habits that come from critical reflection.” (2004, p.15)

The major theoretical roots of reflection can be found in John Dewey, Jürgen Habermas, David Kolb, and Donald Schön. John Dewey has stated, “We do not learn from experience…we learn from reflecting on experience.” The Learning Cycle, developed by David Kolb, based Dewey, Piaget, and Lewin, is based on the belief that deep learning (learning for real comprehension) comes from a sequence of experience, reflection, abstraction, and active testing. James Zull's (2002) fascinating book on the biology of learning, points out evidence that the learning cycle arises naturally from the structure of the brain (p.19).

Zull’s overlay of Kolb’s Experiential Learning Model over the structure of the brain (p.18, shown above), and Jennifer Moon’s further elaboration (shown on the right), provide further support for the importance of reflection in supporting deep learning. Zull points out, “Even if we experience something that has happened to us before, it is hard to make meaning of it unless it engages our emotions.” (p.166) He also points out that reflection is a search for connections (p. 167) and suggests that we have to seriously consider the role of emotion if we want to foster deep learning (p. 169).

Even if we were able to decrease our emphasis on speed and information and increase the possibilities for reflection, we still would have to give our students the kind of experience that would produce dreams-- experiences that engage their emotions. (p.168)

Roger Schank (1991) points out the importance of stories in learning, that recalling and creating stories are part of learning. In fact, stories engage all parts of the brain Zull points out that learning is deepest when it engages the most parts of the brain. Jennifer Moon, the most recent researcher on reflective practice, provides the following definition:

  • Ill-structured, ‘messy’ or real-life situations
  • Asking the ‘right’ kinds of questions – there are no clear-cut answers
  • Setting challenges can promote reflection
  • Tasks that challenge learners to integrate new learning into previous learning
  • Tasks that demand the ordering of thoughts
  • Tasks that require evaluation

Portfolios provide a powerful environment in which students can collect and organize the artifacts that result from engaging in these challenging, real-life tasks, and write the reflections through which students draw meaning. Part of the reflective process is to have students tell stories about their experiences which brain research shows can help students embed these experiences into their long term memory.

Donald Schön (1988) discussed storytelling as a mode of reflection:

For those who consider the term “storytelling” to be too informal, Mattingly (1991) recommends using the term “narrative inquiry.” He points out Aristotle’s use of narrative as the natural framework for representing the world of action. Mattingly also elaborates on the “everyday sense-making role of storytelling,” that stories reveal the way ideas look in action. Narrative provides explanation. Our motivation for telling stories is to wrest meaning from experiences.

Clandinin & Connelly in Schön (1991) call stories “unpretentious narrative.” Stories are a fundamental method of personal growth through reflection, which is preparation for the future, and deliberation, of past considerations. Reflection does not always have to be in written form. For some students, reflections can be oral, shared with peers or teachers. However, as Schön notes, we need to capture those stories in our portfolios to make them objects of reflection. With the addition of multimedia technologies, these stories can be captured, in either audio or video formats.

Janice McDrury and Maxine Alterio (2002), two educators from New Zealand have written a book called Learning through Storytelling in which they outline their theory of storytelling as an effective learning tool. They have linked the art of storytelling with reflective learning processes supported by the literature on both reflection and learning as well as making meaning through storytelling.

McDrury, J., Alterio, M. (2003) Learning through Storytelling in Higher Education. London: Kogan Page.

Moon, J. (1999) Reflection in Learning and Professional Development. London: Kogan Page.

Schank, R. (1991) Tell Me a Story: A New Look at Real and Artificial Memory. Atheneum

Schön, D. (1988) “Coaching Reflective Teaching” in P. Grimmett & G. Erickson (1988). Reflection in Teacher Education (pp. 19-29). New York: Teachers College Press.

Schön, D. (1991) The Reflective Turn: Case Studies in and on Educational Practice. New York: Teachers College Press

Zubizarreta, J. (2004). The Learning Portfolio. Bolton, MA: Anker Publishing

Zubizarreta, J. (2009). The Learning Portfolio: Reflective Practice for Improving Student Learning, Second Edition. Jossey-Bass

Zull, J. (2002) The Art of Changing the Brain. Sterling, VA: Stylus Publishing

Excerpts for this document are from Helen Barrett's White Paper: Researching Electronic Portfolios and Learner Engagement, created initially for The REFLECT Initiative and adapted for a paper published in IRA's Journal of Adolescent and Adult Literacy, March 2007.

Is human blood ever any color other than red?

Yes, human blood is green in the deep ocean. We have to be careful about what we mean by color. Objects don't really have an intrinsic color. Rather, the color of an object is determined by three factors: 1) the color content of the incident light that is illuminating the object 2) the way the object reflects, absorbs, and transmits the incident colors of light and 3) the way in which the detector such as your eye or a camera detects and interprets the colors of light coming from the object. In everyday life, the incident light (such as from the sun or from a light bulb) typically contains all colors of visible light in nearly equal proportions. Furthermore, the healthy human eye can detect all colors of visible light. For these two reasons, in typical circumstances, we can treat the color of an object as only depending on the properties of the object itself. However, once we move away from typical circumstances, we have to use the more complete description of color, which involves the light source, the object, and the detector. With this in mind, let's turn to the color of blood.

As reported in the journal Applied Spectroscopy, Martina Meinke and her collaborators measured the diffuse reflectance of human blood and found the spectrum which is shown below. This particular spectrum is for blood with a hematocrit (the percent of the blood's volume taken up by red blood cells) of 33% and oxygen saturation of 100%. These researchers also measured the reflectance spectrum of blood for other hematocrit values and oxygen saturation values. They found that although the spectrum slightly changes for different hematrocrit and oxygen saturation values, the overall trend shown below remains the same. Therefore, in terms of the overall trend, the image below is a good representation of the reflectance of any human's blood. (Note that even deoxygenated blood follows these trends and is dominantly red, not blue.)

As we see in the image above, blood mostly reflects red light. Interestingly, though, blood also reflects a little bit of green light. If we shine white light (which contains all colors) onto the blood, blood looks red since it reflects so much more red light than green light. However, if we use a light source that contains all of the visible colors except red and shine it onto the blood, the blood will be green. With no red light present in the first place, the blood can't reflect any red light. The only thing left that it can reflect is the green light. The blood is therefore green. Note that this is not a trick of the eyes. The blood is literally green. In fact, human blood is always a little bit green. We usally don't notice the green color of blood because there is typically so much more red light being reflected by the blood. But if you shine a light on the blood that contains green light but no red light, the green color of blood becomes obvious.

This is exactly what happens deep in the ocean. Water is naturally very slightly blue colored because it absorbs some of the red light passing through. The deeper you go in the ocean, the less red light there is in the sunlight that reaches you. Without red color in the sunlight, only green light reflects from the blood. This fact can be startling to divers who get a cut while diving. Again, the blood does not change when in the deep ocean. Rather, the green color of blood that is always there becomes obvious once the brighter red color is no longer present. Since the green reflectance peak of blood is always there, blood can be obviously green anytime you have a light source with no red color, and not just in the deep ocean.

Echinoderm Characteristics

An adult echinoderm is radially symmetrical, meaning their body parts extend outward from the mouth. An echinoderm usually has 5 parts, making them pentamerous. Curiously, echinoderm larva are bilaterally symmetrical and must convert to radial symmetry. Typically, the mouth is surrounded by a central disc, which lead to outward to grooves housing rows of podia. These grooves are called ambulacral grooves and may lead to individual legs as in a starfish, or can be simple slits like in a sand dollar. The endoskeleton of an echinoderm is made up of individual pieces, known as ossicles. The ossicles are covered by epidermis, or skin. In some echinoderms, like sand dollars and sea urchins, the ossicles form a rigid shell known as a test. On the other end of the spectrum, sea cucumbers have very few ossicles and they are separated from each other. These ossicles may also fuse to form various structures, such as the brittle spines of the sea urchin.

The water vascular system is an essential part of echinoderm biology. While the system differs in different classes of echinoderm, its basic operation is the same. The system consists of a series of fluid-bearing tubes that connect in a ring-like structure throughout the organism. The system connects to the podia, and can be used to fill them with fluid which elongates and stiffens the podia. This is accomplished by a series of sacs and muscles within the ring canal, lateral canals, and Polian vesicles, some of which can be seen in the image below.

An echinoderm uses this unique system for a number of lifestyles. The podia can be used as feet, to move in a coordinated fashion to direct the echinoderm. The podia can also be used to hold on to the substrate, small stones for protection, or a number of objects to use as camouflage. Some echinoderms are sessile filter feeders, while others actively hunt their prey. While some filter feeders direct food to their mouths, sea stars are known for pushing their stomach outside of their body to feed on prey. Other echinoderms have a complex mouth structure known as Aristotle’s lantern, which houses teeth and allow them to bite and scrape algae from the surface of rocks.

An echinoderm generally has simple circulatory and nervous systems, which circle through their bodies. Their hemal system is open to the environment and allows for gas exchange through a serious of channels throughout the body. The nervous system is a ring of nerves which connect to all parts of the organisms. This is thought to help an echinoderm interact with all directions it faces equally, maximizing the benefits of its radial symmetry.

COVID-19 Testing: Understanding the “Percent Positive”

As COVID-19 outbreaks continue to flare up across the U.S., the need for coronavirus testing remains urgent.

Individuals rely on test results to guide their medical treatment and decisions on whether to self-isolate. Public health officials rely on the results to track the state of the pandemic, and policymakers use this information to guide decisions on reopening schools and businesses.

One number—the “percent positive”—is often cited in these decisions. In this Q&A, Epidemiology faculty David Dowdy, MD, PhD ’08, ScM ’02, and Gypsyamber D’Souza, PhD ’07, MPH, MS, explain what this term means and why it matters.

What is the “percent positive” and why does it matter?

The percent positive is exactly what it sounds like: the percentage of all coronavirus tests performed that are actually positive, or: (positive tests)/(total tests) x 100%. The percent positive (sometimes called the “percent positive rate” or “positivity rate”) helps public health officials answer questions such as:

  • What is the current level of SARS-CoV-2 (coronavirus) transmission in the community?
  • Are we doing enough testing for the amount of people who are getting infected?

The percent positive will be high if the number of positive tests is too high, or if the number of total tests is too low. A higher percent positive suggests higher transmission and that there are likely more people with coronavirus in the community who haven’t been tested yet.

The percent positive is a critical measure because it gives us an indication how widespread infection is in the area where the testing is occurring—and whether levels of testing are keeping up with levels of disease transmission.

What does a high percent positive mean?

A high percent positive means that more testing should probably be done—and it suggests that it is not a good time to relax restrictions aimed at reducing coronavirus transmission. Because a high percentage of positive tests suggests high coronavirus infection rates (due to high transmission in the community), a high percent positive can indicate it may be a good time to add restrictions to slow the spread of disease.

How high is too high?

The higher the percent positive is, the more concerning it is. As a rule of thumb, however, one threshold for the percent positive being “too high” is 5%. For example, the World Health Organization recommended in May that the percent positive remain below 5% for at least two weeks before governments consider reopening. If we are successful in bringing coronavirus transmission under control, this threshold might be lowered over time. To further relax social restrictions and allow very large gatherings or meetings of people traveling from many different areas, for example, we would want a lower threshold.

As of July 2020, some countries (for example, Australia, South Korea, and Uruguay) and U.S. states (for example, New York, Maine, and Connecticut) were well below the 5% threshold, with 1% of tests or fewer being positive—while other countries (for example, Mexico and Nigeria) and states (for example, Mississippi, Nevada, and Florida) had percent positive levels higher than 15%, far above this cutoff. (See Becker’s Hospital Review and the Johns Hopkins Testing Tracker.)

Does a low percent positive mean that a population has herd immunity?

No. A low percent positive simply means that the level of coronavirus transmission, relative to the amount of testing, is low at this point in time.

As of July 2020, it is unlikely any country (or U.S. state) is close to achieving herd immunity. Places that have low percent positive levels have gotten there by reducing levels of coronavirus transmission through policies restricting social contact, aggressive testing and isolation, and the actions of everyday people to maintain distance. But even in these places, the vast majority of the population is still vulnerable to getting COVID-19.

In the future, if populations begin to develop herd immunity (for example, through widespread vaccination), the level of coronavirus transmission will fall—which will also cause the percent positive to fall. But just because a place has a low percent positive now does not mean that it has achieved herd immunity. In fact, some of the places with the lowest percent positive (for example, in New Zealand, where an outbreak has not yet occurred) are likely to have the least amount of population immunity. These places will still be able to achieve herd immunity through mass vaccination when a vaccine becomes available.

How can we reduce the percent positive when it is too high?

Simply put, there are two ways to lower the percent positive: Reduce the amount of coronavirus transmission or increase the number of people who get tested. Fortunately, these two things often go hand-in-hand. If a place is doing more testing—and responding appropriately to positive tests, by making sure that people who might be contagious are isolated, for example—the amount of transmission should go down over time. But even without testing, measures such as stricter regulations regarding wearing masks, physical distancing, and avoiding large gatherings are all effective ways to reduce transmission.

Why does more testing help?

When there is not enough testing in an area, people who are infected with coronavirus don’t get counted, and they don’t know to isolate themselves. As a result, these people can spread the coronavirus and cause disease in their communities.

People who test positive for the coronavirus (and those exposed to them) should isolate themselves for two weeks, and contact tracing should be done to prevent the infection from spreading. Without enough testing, the coronavirus spreads “silently”. By the time severe cases begin to surge in hospitals, outbreaks are larger and much harder to control. These outbreaks can be detected earlier—and their severity lessened—by testing more people.

But for testing to work, people need to get test results quickly. When people have to wait many days to get their results back, they may be less likely to keep themselves isolated. By the time a positive test result comes back, therefore, someone who has been waiting many days may have infected more people.

While tracking the number of positive tests is useful, what matters more is the total number of people who are infected—and we can only know this number by testing more people. As more people are tested, the percent positive will go down.

What should I do if I’m in a place with a high percent positive?

Since this means that the level of coronavirus transmission in your area is likely still high, you should be very careful about wearing masks, washing your hands, maintaining physical distance, and avoiding situations that may put you at risk for getting infected or infecting others (which you could do if you’re infected but don’t know it). You should also consider getting tested if you have any symptoms, or if you have not been distancing and are likely to be in contact with people who are at risk of getting very sick if they develop COVID-19.

If I’m in a place with a low percent positive, does this mean I’m immune?

No. All this means is that coronavirus transmission, relative to testing, is low in your setting at the moment. This usually means that the risk of getting COVID-19 in your area is lower at this time, but it’s important to remember that coronavirus transmission can increase again at any time. Also, a lower percent positive does not mean there is herd immunity. We all need to keep our guard up if we want to keep transmission levels low—which is what needs to happen if we want to get back to our normal activities again.

David Dowdy is an associate professor and Gypsyamber D’Souza is a professor in Epidemiology at the Bloomberg School.

Watch the video: Στην έκθεση RΠροϊόντα μεταφέρετε την ετικέτα με το κείμενο Προϊόντα σε 0εκ από επάνω και 0εκ.. (December 2021).