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2.4.7: Review - Biology

2.4.7: Review - Biology


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Summary

After completing this chapter you should be able to...

  • Differentiate between abiotic and biotic ecosystem components.
  • Describe the three main categories of ecosystems.
  • Explain the law of conservation of mass.
  • Discuss the biogeochemical cycles of carbon, nitrogen, phosphorus, and sulfur.
  • Explain how human activities have impacted these cycles and the potential consequences for Earth.
  • Explain how soil characteristics influence plant growth.
  • Identify and describe each component of soil.
  • Distinguish among sand, silt, and clay and explain how particle size influences soil texture.
  • Describe each horizon in a typical soil profile.
  • Explain how soils are formed, describing each of the five major factors that affect soil formation and composition.
  • Describe the major types and causes of soil degredation.

Ecosystems consist of living (biotic) and nonliving (abiotic) components. They can be classified as freshwater, marine, or terrestrial. Resistance and resilience are measures of ecosystem health.

Matter is anything that occupies space and has mass. Pure forms of matter are called elements and the smallest units of an element are atoms. Atoms form molecules through ionic, covalent, or hydrogen bonding. Molecules that contain carbon and hydrogen covalent bonds are called organic. There are four main type of large organic molecules (biological macromolecules) in organisms: carbohydrates, lipids, proteins, and nucleic acids.

The chemical elements that organisms need continuously cycle through ecosystems. Cycles of matter are called biogeochemical cycles, or nutrient cycles, because they include both biotic and abiotic components and processes. Examples of biogeochemical cycles include the carbon, nitrogen, phosphorus, and sulfur cycles, and each of these can be altered through human activities.

Soil consists of organic and inorganic material as well as water and air. The organic material of soil is made of humus, which improves soil structure and provides nutrients. Soil inorganic material consists of rock slowly broken down into smaller particles that vary in size, such as sand, silt, and loam. Soils form slowly as a result of biological, physical, and chemical processes. Soil is not homogenous because its formation results in the production of layers called a soil profile. Most soils have four distinct horizons, or layers: O, A, B, and C. Their composition is influenced by the climate, presence of living organisms, topography, parent material, and time. The processes of erosion, compaction, and desertification degrade soils. While these processes occur naturally to an extent, they are exacerbated by certain agricultural practices, deforestation, and other human activities.

Modified by Melissa Ha from the following sources:

  • The Soil and Biogeochemical Cycles from General Biology by OpenStax (licensed under CC-BY)
  • Nutrient Cycles from Human Biology by Suzanne Wakim and Mandeep Grewal (CC-BY-NC)

B.Sc Biology

Course Duration: Bachelor of Science [B.Sc] (Biology) is 3 Years.

Updated on - Apr 19th, 2021 | 03:09 PM by Rahul Saxena

B.Sc Biology is a 3 - year undergraduate course that deals with studying biological aspects of living organisms, aspirants attend classes and labs, profiting the valuable knowledge of organisms and their behavior in and around the environment. With a graduated degree in B.Sc in Biology, aspirants can get job opportunities to work as a biology technician, molecular biologist, ecologist, botanist, agricultural consultant, online tutoring, a career in genetics, and wildlife conservationist. Many multinational industries like Bayer CropScience Ltd, Pidilite industries ltd, tata biotech ltd, etc., are on the verge of higher these aspirants.


2.4.7: Review - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


Author Summary

Cells with damaged DNA are at risk of becoming cancerous tumors. Although “cellular senescence” can suppress tumor formation from damaged cells by blocking the cell division that underlies cancer growth, it has also been implicated in promoting cancer and other age-related diseases. To understand how this might happen, we measured proteins that senescent human cells secrete into their local environment and found many factors associated with inflammation and cancer development. Different types of cells secrete a common set of proteins when they senesce. This senescence-associated secretory phenotype (SASP) occurs not only in cultured cells, but also in vivo in response to DNA-damaging chemotherapy. Normal cells that acquire a highly active mutant version of the RAS protein, which is known to contribute to tumor growth, undergo cellular senescence, and develop a very intense SASP, with higher levels of proteins secreted. Likewise, the SASP is more intense when cells lose the functions of the tumor suppressor p53. Senescent cells promote the growth and aggressiveness of nearby precancerous or cancer cells, and cells with a more intense SASP do so more efficiently. Our findings support the idea that cellular senescence can be both beneficial, in preventing damaged cells from dividing, and deleterious, by having effects on neighboring cells this balance of effects is predicted by an evolutionary theory of aging.

Citation: Coppé J-P, Patil CK, Rodier F, Sun Y, Muñoz DP, Goldstein J, et al. (2008) Senescence-Associated Secretory Phenotypes Reveal Cell-Nonautonomous Functions of Oncogenic RAS and the p53 Tumor Suppressor. PLoS Biol 6(12): e301. https://doi.org/10.1371/journal.pbio.0060301

Academic Editor: Julian Downward, Cancer Research UK, United Kingdom

Received: June 27, 2008 Accepted: October 22, 2008 Published: December 2, 2008

Copyright: © 2008 Coppé et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by grants from the National Institutes of Health (research grants AG09909 & AG017242 to JC CA126540 to PSN and JC training grant AG000266 for JG and CKP) the Pacific Northwest Prostate Cancer SPORE CA97186) and Larry L. Hillblom Foundation (fellowship to CKP).

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: CM, conditioned medium DDR, DNA damage response ELISA, enzyme-linked immunosorbent assay EMT, epithelial–mesenchymal transition GSE, genetic suppressor element IL, interleukin MIT, mitoxantrone PRE, presenescent PrEC, normal human prostate epithelial cell REP, replicative exhaustion SASP, senescence-associated secretory phenotype SEN, senescent shRNA, short hairpin RNA XRA, X-irradiation


Effects of boron-containing compounds on cardiovascular disease risk factors – A review ☆

Boron is considered to be a biological trace element but there is substantial and growing support for it to be classified as an essential nutrient for animals and humans, depending on its speciation. Boron-containing compounds have been reported to play an important role in biological systems. Although the exact biochemical functions of boron-containing compounds have not yet been fully elucidated, previous studies suggest an active involvement of these molecules in the mediation of inflammation and oxidative stress. Chronic inflammation and oxidative stress are known to amplify the effects of the main cardiovascular risk factors: smoking, diet, obesity, arterial hypertension, dyslipidemia, type 2 diabetes (as modifiable risk factors), and hyperhomocysteinemia and age (as independent risk factors). However, the role of boron-containing compounds in cardiovascular systems and disease prevention has yet to be established.

This paper is a review of boron-containing compounds’ existence in nature and their possible functions in living organisms, with a special focus on certain cardiovascular risk factors that may be diminished by intake of these compounds, leading to a reduction of cardiovascular morbidity and/or mortality.


Understanding differences between solutions, emulsions, colloids and dispersions

Analysis of samples in laboratories more than often requires pre-treatment steps for extraction, isolation, concentration or dilution to measurable concentration ranges. This is generally achieved&hellip

Responses

This clear article is base to further understanding chromatograms. It would be great if were it posible to put more examples, with clearest images to follow the practical issues in Reading thes graphics.

Also there are an initial time where those components that pass without retention provide a small peak. So when Reading the full chromatogram, this could be confuse to understand its role in calculations.

We will add more examples in future posts covering this topic, thanks for your suggestion!

how can i calculate area of these graph peak manually,what is the unit of these area which is calculated by software

Traditionally the practice was to cut the chromatographic peak from the chromatogram and weigh. The weight of sample peak was compared with that of of the standard peak.Otherwise approximations can be arrived at by using triangulation method or using graph paper and by counting the squares.Such methods gave approximate values only. Presently software provides area of each peak in terms of counts which are unitless figures.

Dear Sir
I recently joined your newletter and found it good reading. I am currently trying out various methods for HPLC, as we are trying to do some work with blood levels of levofloxacin. For this i have questions,
I found a methods for seperation- it utilises acetonitrile – water (80:20) at pH 3.5, my samples however- are in methanol. can i inject the samples as they are or do i need to incorporate the mobile phase? The reason being – we used methanol to precipitate proteins out of serum.
Regards
Satish

Hi Satish,
First of all thank you for your comments, we are glad you are enjoying newsletter. You can inject samples in methanol, there should be no problem. Do let us know how it works out!

[…] the identity of the eluting compound. Retention time has been explained in the earlier article How to read a chromatogram?. It is a vital analysis parameter and drifts resulting due to unintentional or uncontrolled changes […]

Happy New Year Sir, I am one of your favourite blog readers. Sir, I can’t decode how to read and interprete the chromatogram as discussed above. Can you please elaborate more on the calculations.

Happy and prosperous new year to you as well.Please feel free to contact me on my mail id [email protected] .I shall help you with calculations on getting details of your analysis and the data which is generated by your system.
Best Regards

I have seen some people calculate peak area using the triangle formula rather than from the area mentioned in the chromatogram. Is it a right way of calculating peak area?

Hi,
Chromatographic peaks are seldom perfect triangles so area calculation using the triangle area formula will not be representative of area under the peak.In the chromatogram shown in the article you will come across numerical units in area column which are proportional to area under the peak. You can use these units as representing the peak area for further calculations.

Dear Dr.Bhanot,
I find your article (How to read chromatogram) very useful, please can you elaborate more on it?

Hi Auwalu,
I am glad to note that you found the article useful.Please do forward your specific query on which you wish me to elaborate upon.I will like to clarify your doubts.
Thanks

Thanks for all your articles. Even the basic ones are useful as they lay the foundation of complex concepts to understand.

Thanks Shazia for your appreciation. Hope you enjoyed our HPLC programme as well.

thanks for such a simple and perfect explanation
i am b.tech biotech passout
i didnt stuidied seriously during classes , i hope i can get to know about HPLC enough to get a job
thanks :)

Hi,my shimadzu hplc machine just suddenly stopped printing out results.please what could have caused it? And what can i do?

Hi samson,
It appeares to be an electrical problem. The best option would be to inform the maintenance support of the supplier.

Hello! I am currently doing an assignment on melamine detection within milk products. I am just learning how to read chromatograms as I need to investigate the HPLC-UV method (and ELISA and GC/MS). As I am trying to interpret some researched chromatograms however, there are several peaks but only some are marked *melamine*. How do you determine whether a spike is indicative of the analyte you are looking for or not? Thank you

Hi Lauren, you will need to inject the pure standard to confirm the retention time and also spike it in the sample to confirm there is on shifting of the rt in the samples.

Am very impressed with the unmeasurable knowledge I have learned from this tutorial. I am a master student and in my thesis i would like to identifiy some protein protein interactums. would this skill of HPLC help me to elucidate these interactions? thanks once again.

Thanks Terry for your encourangement.HPLC holds great promise for applications in life sciences and biochemistry. You will certainly find useful references as you progress with your research

Hi Dr. Deepak Really an informative article. Thanks a lot.

I need more help on this to understand. I have some HPLC reports and by using the formula to find each component %age, the percentage is coming different than what HPLC is actually giving.

We are using a carbohydrate column and we are seperating fructose, glucose, sucrose and maltose from a Honey sample. can you tell me that if we want to detect HMF, what can we do?

Hi, the HPLC might be giving the % by area percentage and you might be calculating with standard areas thus the difference. As for HMF a separate method will be needed on a UV detector.

What does difference in Retention time mean? In our undergraduate thesis, our sample Lycopene has RT of 3.937 and the standard has RT of 4.100. Does it mean that out sample is not lycopene?

Hi Katrina, Slight variations in the RT are acceptable and the tolerance level is usually established during a method validation study. If you are working on HPLC it seems to be fine, you need to make sure you are injecting the sample and standard in the same diluent.

Hello Sir,
Its a great support for analyst through newsletter.
In addition to this, RT variation criteria of different samples are not defined. It should be established during validation and limit must be justified. However RT variation criteria of same sample can be established (RSD up to NMT 0.2%)

Much thanks,
I have just obtained my chromatograms from analysis of bio- gas and bio-oils, and i do not know what to do with it.
I am so relieved after reading this article.
Thanks for post!

I am glad to read through your write up Dr Deepak Bhanot. Indeed, they are very educative and I have learnt alot.

Thanks a lot for your encouragement.

Good morning Sir,
The write up has being of tremendous useful, I appreciate your good work. Kindly assist me with an example or References on how to write the interpretation of the chromatogram on how it affect the seasons.
I will appreciate your favourable response.
Yours sincerely,
Olusola.

Good to note that you found the information useful.I am not able to understand your query. Can you elaborate a little more on your requirement.Further clarify what you imply by seasons.

Hi
Thanks a lot for your articles. Its very helpful in understanding HPLC machine.
Thanks again

Hello sir,
Thank for your article

I need to find the resolution between second and third peak. How to get the peak width based on the graph? It is because the information state at the graph only have retention time, area, %area, height and %height. Thank you for reading my question sir.

The best option available to you is to use the software capabilities available on your system. You have to first go through the software capabilities and then take help to get resolution between the selected peaks

Hello Dr,
Your brief and clearly composed HPLC chromatogram reading blog really impacts high on young researchers, well done.
Sir, 1) where a peak area is wide, can one take the midpoint for the retention time or the point of emergence of the peak. 2) During Isolation of components of a mixture, we use to collect eluate from each peak immediately the peak appears and stop immediately it collapses, but I observed that the distance between detector and exit port has to take some seconds, how can we adjust this so as to avoid collection of impurity. thanks

Try a slight increase in mobile phase flow rate keeping other operating conditions same. Hopefully it should solve both your problems. Your peak should get narrowed down and impurity content in collected portion should also decrease.

i would like to subscribe to your newsletter. kindly provide me with the proper links both for PC and smart phone. I am in the beginning stages of learning the analytical experiments.

You can subscribe by providing your details on clicking the subscribe now button on our web site http://www.lab-training.com

Is this the % recovery?
I have to calculate % recovery of butanol in my experiment, I know the retention time and area under curve and initial concentration of butanol too.

The chromatogram will give the concentration in your sample.As you know the original concentration you can calculate %recovery.

Thank you for providing these useful notes.
I would like to ask you a question about the area of the peak. Do they represent amount or concentration of the compound we are trying to analyse?

I understood that in order to do quantification of the compound, we should run the standards at different conc. for calibration purposes.

The area under the peak represents the amount of a component in the sample. You can calculate the concentration from the volume injected. Hope this clarifies yur query.

How do you calculate the concentration from the volume injected? does the area % mean that for instance 40% of the 15microliter sample is the component? Or is the area under the peak the amount of the component in mol and do you calculate the concentratie by deviding it by the volume injected? Or is the area under the curve the concentration?
Please help

Dear Famke,
The area under the peak represents the amount of a compound present as a percentage of the total area of the peaks in the chromatogram. The areas are printed in tabulated format in numerical digits without any units.The area is calculated from these numerical figures with reference to the area count of the standard which is injected in between the sample runs.Please let me know if you are interested in the formula for concentration calculations which I can send on your e-mail id.
Thanks

Hi, I am a Food Engineering student and I am working in a presentation about HPLC. Congratulations for your website, the explanation is very clear!!

Comment on the issue of integration when there is a shift in Retention time from expected. I understand this sometimes occurs when there is column deterioration.

Shift in retentiontime can hppen due to several factors such as change i operating conditions, namely flow rate of mobile pahse, temperature of column,changes in composition of mobile phase,etc

What does difference in Retention time mean?
our sample has RT of 5.96 and the standard has RT of 6.56. Does it mean that out sample is not ?

in HPLC analysis of my sample, I got a peak at 5.94 and my std gave a peak at 6.56 mins, having done the experiment under identical conditions can both the peaks be identified as the same compound?

The 2 peaks are from different compounds.

Hello sir
I am very thank you for your helping me understand in which what way chromotopography works in a HCLP and I very muchly like to be read yuor articles as they are very help

Hello sir
I am very thank you for your helping me understand in which what way chromotopography works in a HCLP and I very muchly like to be read yuor articles as they are very help

is very important. introduces it to the best

This contains is so much helpful. Thank You so much. And please try to give more example for completely understand.

How can we calculate area% and height% in a chromatogram?

Most software give you the option to do this. If not you will have to add all of them up and calculate manually ore in excel.

Very informative. I think I am smarter after reading that but believe it’ll take some time to process fully in order to apply to my situation of interest. I own a medical testing company and perform a lot of drug testing as a third party administrator. I have a question in regards to false positives dealing with GCMS, specifically with methamphetamine and cocaine, in particular with hair drug analysis. There seem to be 3 or 4 main reasons for false positives: inadequate sample washing process, environmental contamination, molecularly similar components from hair products or food and drug metabolites. With methamphetamine I believe I read there are around 12 known molecularly similiar ?neoisomers?? nanosomethings. other things that appear to have the same height/curve spectrum on a GCMS. DO you have any idea what I’m talking about or what other components that have been tested by GCMS/LCMS that appear to be identical to other components with similar spectrums or graphical appearance when tested??

I am a business man that fell into the business. Not a scientist. However it seems that the majority of scientists and clinicians are not aware of this frequent occurrence of false positives specifically for methamphetamine. However, I deal with around 20 people a month that test positive and swear they have never even used the drug. They can’t all be calling my company lying since I don’t have anything to do with how they were originally tested. Several lawyers have contacted me and believe that a number of these people are probably telling the truth. This means that countless people are having their kids taken away and/or are being falsely imprisoned due to false allegations.

Can someone with more knowledge of this topic give me some ideas of what could be or is likely happening?

Blake Brewer
Founder and President
Drug Testing Solutions Dallas
Recovery Solution
North Texas and Southern California

Hi
False positive rises from wrong interpretation of MS spectra when relying only on matching the spectra with authentic libraries. However such analysis must be confirmed by convolution as all positive falsies happened when both compounds eluted together and sum of fragmentations pattern match a third compound.
It is very easy to clear out the results and match the ion ratios must be identical to those in the library without any additional ion.
Other solution just extract the parent ion with 4 confirming ions if all appear in your sample this is positive result otherwise negative


References

Costello JC, Stolovitzky G. Seeking the wisdom of crowds through challenge-based competitions in biomedical research. Clin Pharmacol Ther. 2013 93(5):396–8.

Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, et al. A large-scale evaluation of computational protein function prediction. Nat Methods. 2013 10(3):221–7.

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000 25(1):25–9.

Dessimoz C, Skunca N, Thomas PD. CAFA and the open world of protein function predictions. Trends Genet. 2013 29(11):609–10.

Gillis J, Pavlidis P. Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA). BMC Bioinform. 2013 14(Suppl 3):15.

Schnoes AM, Ream DC, Thorman AW, Babbitt PC, Friedberg I. Biases in the experimental annotations of protein function and their effect on our understanding of protein function space. PLoS Comput Biol. 2013 9(5):1003063.

Jiang Y, Clark WT, Friedberg I, Radivojac P. The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective. Bioinformatics. 2014 30(17):609–16.

Robinson PN, Mundlos S. The human phenotype ontology. Clin Genet. 2010 77(6):525–34.

Moreau Y, Tranchevent LC. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet. 2012 13(8):523–36.

Clark WT, Radivojac P. Information-theoretic evaluation of predicted ontological annotations. Bioinformatics. 2013 29(13):53–61.

Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S, et al. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2005 33(Database issue):154–9.

Huntley RP, Sawford T, Mutowo-Meullenet P, Shypitsyna A, Bonilla C, Martin MJ, et al. The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Res. 2015 43(Database issue):1057–63.

Clark WT, Radivojac P. Analysis of protein function and its prediction from amino acid sequence. Proteins. 2011 79(7):2086–96.

Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997 25(17):3389–402.

Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman & Hall 1993.

Cal S, Argüelles JM, Fernández PL, López-Otın C. Identification, characterization, and intracellular processing of ADAM-TS12, a novel human disintegrin with a complex structural organization involving multiple thrombospondin-1 repeats. J Biol Chem. 2001 276(21):17932–40.

Wolfsberg TG, Straight PD, Gerena RL, Huovila A-PJ, Primakoff P, Myles DG, et al. ADAM, a widely distributed and developmentally regulated gene family encoding membrane proteins with a disintegrin and metalloprotease domain. Dev Biol. 1995 169(1):378–83.

Brocker CN, Vasiliou V, Nebert DW. Evolutionary divergence and functions of the ADAM and ADAMTS gene families. Hum Genomics. 2009 4(1):43–55.

Wass MN, Mooney SD, Linial M, Radivojac P, Friedberg I. The automated function prediction SIG looks back at 2013 and prepares for 2014. Bioinformatics. 2014 30(14):2091–2.

Funding

We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC) National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM) the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT) the National Basic Research Program of China grant 2012CB316505 (WT) NSERC grant RGPIN 371348-11 (PP) FP7 infrastructure project TransPLANT Award 283496 (ADJvD) Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS) Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD) the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT) KU Leuven CoE PFV/10/016 SymBioSys (YM) the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF).

Availability of data and materials

Data The benchmark data and the predictions are available on FigShare https://dx.doi.org/10.6084/m9.figshare.2059944.v1. Note that according to CAFA rules, all but the top-ten methods are anonymized. However, methods are uniquely identified by a code number, so use of the data for further analysis is possible.

Software The code used in this study is available at https://github.com/yuxjiang/CAFA2.

Authors’ contributions

PR and IF conceived of the CAFA experiment and supervised the project. YJ performed most analyses and significantly contributed to the writing. PR, IF, and CSG significantly contributed to writing the manuscript. IF, PR, CSG, WTC, ARB, DD, and RL contributed to the analyses. SDM managed the data acquisition. TRO developed the web interface, including the portal for submission and the storage of predictions. RPH, MJM, and CO’D directed the biocuration efforts. EC-U, PD, REF, RH, DL, RCL, MM, ANM, PM-M, KP, and AS performed the biocuration. YM and PNR co-organized the human phenotype challenge. ML, AT, PCB, SEB, CO, and BR steered the CAFA experiment and provided critical guidance. The remaining authors participated in the experiment, provided writing and data for their methods, and contributed comments on the manuscript. All authors read and approved the final manuscript.


Abstract

Background

Despite decades of research, the concept of normality in labour in terms of its progression and duration is not universal or standardized. However, in clinical practice, it is important to define the boundaries that distinguish what is normal from what is abnormal to enable women and care providers have a shared understanding of what to expect and when labour interventions are justified.

Objectives

To synthesise available evidence on the duration of latent and active first stage and the second stage of spontaneous labour in women at low risk of complications with ‘normal’ perinatal outcomes.

Search strategy

PubMed, EMBASE, CINAHL, POPLINE, Global Health Library, and reference lists of eligible studies.

Selection criteria

Observational studies and other study designs.

Data collection and analysis

Four authors extracted data on: maternal characteristics labour interventions duration of latent first stage, active first stage, and second stage of labour and the definitions of onset of latent and active first stage, and second stage where reported. Heterogeneity in the included studies precluded meta-analysis and data were presented descriptively.

Main results

Thirty-seven studies reporting the duration of first and/or second stages of labour for 208,000 women met our inclusion criteria. Among nulliparous women, the median duration of active first stage (when the starting reference point was 4 cm) ranged from 3.7–5.9 h (95th percentiles: 14.5–16.7 h). With active phase starting from 5 cm, the median duration was from 3.8–4.3 h (95th percentiles: 11.3–12.7 h). The median duration of second stage ranged from 14 to 66 min (95th percentiles: 65–138 min) and from 6 to 12 min (95th percentiles: 58–76 min) in nulliparous and parous women, respectively. Sensitivity analyses excluding first and second stage interventions did not significantly impact on these findings

Conclusions

The duration of spontaneous labour in women with good perinatal outcomes varies from one woman to another. Some women may experience labour for longer than previously thought, and still achieve a vaginal birth without adverse perinatal outcomes. Our findings question the rigid limits currently applied in clinical practice for the assessment of prolonged first or second stage that warrant obstetric intervention.


2.4.7: Review - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


2.4.7: Review - Biology

Open Journal Systems (OJS) is an open source software application for managing and publishing scholarly journals. Originally developed and released by PKP in 2001 to improve access to research, it is the most widely used open source journal publishing platform in existence, with over 10,000 journals using it worldwide.

OJS Features

OJS is a comprehensive tool for managing your entire submission and editorial workflow and publishing your articles and issues online. It offers the following features:

  • Responsive reader front-end with a selection of free themes or designs
  • Flexible and configurable editorial workflow
  • Online submission and management of all content
  • Subscription module with delayed open access options
  • Integrated with scholarly publishing services such as Crossref, ORCiD, and DOAJ
  • Recommended by Google Scholar for ease of indexing and discoverability
  • Locally installed and controlled
  • Community-led and supported
  • Multilingual and translated into over 30 languages
  • Extensive user guides and training videos

OJS is free and open source software released under the open source GPL v2 license. You are free to download, use, and modify it at no charge. OJS is made freely available to journals worldwide for the purpose of making open access publishing a viable option for more journals, as open access can increase a journal’s readership as well as its contribution to the public good on a global scale (see PKP Publications).

PKP Publishing Services also offers a fee-based service which provides the installation and hosting of OJS, as well as performing daily backups of your data, applying security patches and upgrades, and priority answering your support questions. All revenue generated by the hosting service goes into developing PKP software and supporting the Public Knowledge Project.

“Scholars need the means to launch a new generation of journals committed to open access, and to help existing journals that elect to make the transition to open access…”
Budapest Open Access Initiative, 2002


Watch the video: Cellular Organelles (January 2023).