In the readings and videos for this module week, it was noted that there were numerous operations, tests, or equipment functions that went wrong in the last 32 hours before the explosion. Let’s go back in time and consider that you are the safety professional for all Transocean rigs in the Gulf of Mexico. It has been two months since the disaster and you have just been informed that these things are what caused the blowout that lead to the explosion on the Deepwater Horizon. Use your knowledge to construct a Preliminary Hazard List (PHL) for the items that you feel can lead to these issues reoccurring. The video entitled, “BP Deepwater Horizon Accident Investigation Report” will help to increase your knowledge on how the drilling operation works. Tell your boss (for the purposes of all the case studies in this class your boss is your Instructor) in a one to two page document what the hazards associated with this drilling operation are. This report should include an actual PHL and a short narrative talking about the items in the PHL.
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What does the Electronic Privacy Control Act of 1986 address?
Assignment 08
T06 Cyber Security
Directions: Be sure to make an electronic copy of your answer before submitting it for grading. Unless otherwise stated, answer in complete sentences, and be sure to use correct English spelling and grammar. Sources must be cited in APA format. Your response should be four (4) pages in length; refer to the “Assignment Format” page for specific format requirements.
What does the Electronic Privacy Control Act of 1986 address?
How many titles are there in the Act?
Explain what each of the titles covers.
List any exceptions and amendments to the original Act of 1986.
What is the rationale behind the creation of this Act?
Grading Rubric
Please refer to the rubric on the following page for the grading criteria for this assignment.
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TED talks are organized and highly-vetted public speeches that give normal people—people like you and me—a chance to spread their message to the world. Visit TED.com and watch several TED speakers. Note that you can sort by topic, speaker, and a host of other criteria. You can watch any TED talk you wish. If you’re looking a more concentrated list, you might also wish to visit this link, which highlights the 25 most popular TED talks of all time.
After watching a TED talk (or a few!), write a short analysis of that talk. Please ensure that you cite the talk you watched in appropriate APA formatting. You should answer the following questions in your analysis:
· What is the topic and who is the speaker?
· How does the speaker get the audience’s attention in the introduction?
· What are the main points of the talk? Are they easily identifiable?
· What techniques of delivery does the speaker use effectively (e.g., emotion, vivid language, nonverbals, humor, etc.)? Please be sure to provide examples.
· How does the speaker conclude the speech? Does it echo the themes in the introduction and “tie the knot” or does it go in a different direction?
· Is the speech effective? Did it leave an impact on you? Tell us why or why not.
What did you learn about speech delivery after listening to this speech?
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What If Statement Hazard Category Cause Consequence Recommendations
Formulate “what if” questions about possible failure scenarios
identify the hazard based on the energy type, such as electrical, mechanical, chemical, etc.
identify the potential causes for each hazard
the failure effect on people, property, business, production, environment, etc.
Suggested actions to be taken to improve the performance and reduce the hazards, such as conduct a job safety analysis (jSa), provide required PPe, training, etc.
Operator-Related Hazards
Formulate “what if” questions about possible failure scenarios
identify the hazard based on the energy type, such as electrical, mechanical, chemical, etc.
identify the potential causes for each hazard
the failure effect on people, property, business, production, environment, etc.
Suggested actions to be taken to improve the performance and reduce the hazards, such as conduct a job safety analysis (jSa), provide required PPe, training, etc.
Reducing Agent Atom Transfer Radical Polymerization Madson R. E. Santos,
Increasing the Antimicrobial Activity of Amphiphilic Cationic Copolymers by the Facile Synthesis of High Molecular Weight Stars by Supplemental Activator and Reducing Agent Atom Transfer Radical Polymerization Madson R. E. Santos,† Patrícia V. Mendonca̧,† Mariana C. Almeida,‡ Rita Branco,‡ Armeńio C. Serra,†
Paula V. Morais,‡ and Jorge F. J. Coelho*,†
†Department of Chemical Engineering, CEMMPRE, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, Coimbra 3030-790, Portugal ‡Department of Life Sciences, CEMMPRE, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, Coimbra 3001-401, Portugal
*S Supporting Information
ABSTRACT: Infections caused by bacteria represent a great motif of concern in the health area. Therefore, there is a huge demand for more efficient antimicrobial agents. Antimicrobial polymers have attracted special attention as promising materials to prevent infectious diseases. In this study, a new polymeric system exhibiting antimicrobial activity against a range of Gram- positive and Gram-negative bacterial strains at micromolar concentrations (e.g., 0.8 μM) was developed. Controlled linear and star-shaped copolymers, comprising hydrophobic poly(butyl acrylate) (PBA) and cationic poly(3-acrylamidopropyl)trimethyl- ammonium chloride) (PAMPTMA) segments, were obtained by supplemental activator and reducing agent atom transfer radical polymerization (SARA ATRP) at 30 °C. The antibacterial activity of the polymers was studied by varying systematically the molecular weight (MW), hydrophilic/hydrophobic balance, and architecture. The MW was found to exert the greatest influence on the antimicrobial activity of the polymers, with minimum inhibitory concentration values decreasing with increasing MW. Live/dead membrane integrity assays and scanning electron microscopy analysis confirmed the bactericidal character of the synthesized PAMPTMA-(b)co-PBA polymers.
■ INTRODUCTION Infections and diseases caused by multi-resistant microbes have become a concern in diverse human health-associated areas, often requiring extensive periods of treatment and high medical costs or even culminating in the death of many patients.1 This context has prompted researchers to seek new materials capable of effectively killing pathogens, as an alternative to the common use of antibiotics, which are associated with the development of antimicrobial resistance. Antimicrobial polymers have been pointed out as a highly promising approach to combat drug-resistant microorganisms. Polymers present several advantages over the low molecular weight (MW) biocides. For example, the intensive use of conventional antibiotics in the past decades has dramatically
increased the frequency of resistance among human pathogenic microorganisms.2 On the other hand, antimicrobial polymers can disturb the whole bacterial environment by multiple interactions, such as hydrophilic−hydrophobic interactions, disruption of the integrity of the lipid barrier, or disturbance of the transport of compounds across the membrane, leading to bacteria death.3,4 Moreover, polymers could present reduced cytotoxicity and chemical stability, and their properties can be easily tuned, which allows the optimization of the antimicrobial performance of the polymeric material.
Received: April 27, 2018 Revised: June 29, 2018 Published: July 3, 2018
pubs.acs.org/Biomac
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http://dx.doi.org/10.1021/acs.biomac.8b00685
Since most bacteria present negatively charged cell walls, cationic polymers are usually the most promising candidates as antimicrobial agents, as they can interact with bacteria through electrostatic forces.5 In particular, synthetic polymers contain- ing quaternary ammonium (QAS) groups or quaternary phosphonium (QPS) groups have been widely studied and applied as antimicrobial agents in diverse areas (e.g., biomedical field).6−8 Beside the positive charges, the antimicrobial polymers should have a proper hydrophilic/ hydrophobic balance, which is essential to allow their permeation through the lipid bilayers of the bacteria cell wall. In addition, it is expected that the molecular MW and the structure/architecture of the polymers are also important parameters that influence the polymer’s antimicrobial efficiency.3 Several studies reported in the literature are dedicated to the investigation of the role of each polymer feature on their antimicrobial activity.9−11 However, it is difficult to establish “universal” correlations, since the antimicrobial activity depends on both the polymer nature/ structure and the bacterial strain tested. Thus, judicious design and synthesis of well-defined macromolecules is necessary to provide a clear understanding of the polymer structure/ performance relationship. To this end, advanced polymer- ization techniques, namely, reversible deactivation radical polymerization (RDRP) methods, could be very useful tools to prepare tailor-made antimicrobial polymers with stringent control over properties such as MW, composition, structure, and architecture, which may have a great influence on the biological activity of the polymers.12,13
A number of recent studies have pointed out the importance of a polymer’s architecture in its antimicrobial efficacy, with special relevance given to the nanostructured polymers as star- shaped,14,15 rod-like,16 and polymer-immobilized nanopar- ticles17 that have shown enhanced and indiscriminate activity
toward bacterial cells.18,19 However, despite the outstanding antimicrobial activity of polymers with such structures, the strategies reported for their preparation sometimes require laborious steps concerning the synthesis of the monomers, reaction conditions, and polymer purification. Here, a one-step strategy is reported to obtain antimicrobial
linear and star-shaped amphiphilic copolymers, as a new class of antimicrobial agents, via supplemental activator and reducing agent atom transfer radical polymerization (SARA ATRP) at near room temperature (30 °C). These polymers were designed to have (3-acrylamidopropyl)trimethyl- ammonium chloride (AMPTMA) as the cationic and hydro- philic segment and n-butyl acrylate (n-BA) as the hydrophobic domain (Figure 1). PAMPTMA was chosen as the cationic segment, due to the presence of a pendant quaternary ammonium group, which provides a permanent cationic character to the polymer regardless the pH of the medium. Quaternary ammonium salts are known to be cationic membrane-active compounds possessing antibacterial activity due to electrostatic interactions and consequent microbe membrane disruption.20 In addition, n-BA was chosen as the hydrophobic monomer, since it could improve the mobility of the antimicrobial polymer through the lipid barrier of the bacteria cell wall.21 An extensive library of PAMPTMA-based polymeric structures, including linear, 4-arm stars, and 6-arm stars, consisting of homopolymers, block copolymers, and random copolymers, with different targeted compositions, were synthesized and evaluated against representative Gram- negative and Gram-positive bacterial strains (Staphylococcus aureus, Bacillus cereus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa).
Figure 1. General structures of the amphiphilic linear and star-shaped (PAMPTMA-co-PBA or PAMPTMA-b-PBA) copolymers prepared by SARA ATRP.
copper wire (Sigma-Aldrich), copper(II) bromide (CuBr2, Aldrich, 99%), deuterium oxide (D2O, Sigma-Aldrich, 99.9 atom% D), deuterated (D6) ethanol (d6-EtOD, Euriso-top, 99+%), N,N- dimethylformamide (DMF, Sigma-Aldrich, 99.8+%), and tetrahydro- furan (THF, HPLC grade) were used as received. Ethanol (JMS, 96%) was passed through a rotatory evaporator before use. (3-Acrylamidopropyl)trimethylammonium) chloride (AMPTMA,
TCI Chemicals, 75% solution in H2O) was washed with acetone in order to remove the water from the solution. The pure monomer was used in the polymerizations. n-Butyl acrylate (n-BA, Sigma-Aldrich, 99+%) was passed through a
sand/alumina column before use in order to remove the radical inhibitor. Purified water (Milli-Q, Millipore, resistivity > 18 MΩ·cm) was
obtained by reverse osmosis. Tris(2-(dimethylamino)ethyl)amine (Me6TREN),
22 penta- erythritol tetrakis(2-bromoisobutyrate) (4f-BiB),23 and dipenta- erythritol hexakis(2-bromoisobutyrate) (6f-BiB)23 were synthesized according the procedure described in the literature. Procedures. Typical Homopolymerization of AMPTMA by SARA
ATRP. AMPTMA (2.5 mL, 4.8 mmol) and a solution of EBiB (9.6 mg, 48.3 μmol), CuBr2 (5.4 mg, 24.2 μmol) and Me6TREN (13.0 μL, 48.3 μmol) in ethanol (2.5 mL) were added to a 10 mL Schlenk flask equipped with a stirrer bar. Cu(0) wire (l = 5 cm; d = 1 mm) was then added to the reactor, which was sealed with a glass stopper, frozen with liquid nitrogen, deoxygenated with three freeze− vacuum−thaw cycles, and purged with nitrogen. The reaction was allowed to proceed with stirring (360 rpm) at 30 °C during 24 h. The monomer conversion was determined by 1H NMR in D2O, and the MW parameters were determined by size exclusion chromatography (SEC) analysis. The polymer was dialyzed against water (cutoff 3500), and the pure polymer was recovered after freeze-drying. Typical Homopolymerization of n-BA by SARA ATRP. n-BA (2.5
mL, 17.4 mmol) and a solution of EBiB (36.9 mg, 174.0 μmol), CuBr2 (19.4 mg, 86.7 μmol), and Me6TREN (46.4 μL, 174.0 μmol) in ethanol (2.5 mL) were added to a 10 mL Schlenk flask equipped with a stirrer bar. Cu(0) wire (l = 5 cm; d = 1 mm) was then added to the reactor, which was sealed with a glass stopper, frozen with liquid nitrogen, deoxygenated with three freeze−thaw cycles, and purged with nitrogen. The reaction was allowed to proceed with stirring (360 rpm) at 30 °C during 24 h. The monomer conversion was determined by 1H NMR in CDCl3, and the MW parameters were determined by SEC analysis. The polymer was dissolved in THF and passed through a sand/alumina column in order to remove the catalyst. The pure polymer was obtained by precipitation in cold methanol. Typical “One-Pot” Block Copolymerization of AMPTMA and n-
BA. AMPTMA (3.0 mL, 14.1 mmol) and a solution of CuBr2 (15 mg, 70.7 μmol) and Me6TREN (37.8 μL, 142.0 μmol) in ethanol (3.0 mL) were added to a 25 mL Schlenk flask equipped with a magnetic stirrer bar. Cu(0) wire (l = 5 cm; d = 1 mm) was then added to the reactor, which was sealed with a glass stopper, frozen with liquid nitrogen, deoxygenated with three freeze−vacuum−thaw cycles, and purged with nitrogen. The reaction was allowed to proceed with stirring (360 rpm) at 30 °C. When the monomer reached more than 70%, a degassed solution of n-BA (2.0 mL, 14.1 mmol) in ethanol (8.15 mL) was added to the Schlenk flask under nitrogen. The reaction was allowed to proceed until the maximum conversion of n- BA was achieved. The AMPTMA-b-PBA block copolymer was purified through dialysis (cutoff 3500) against water, followed by ethanol and then water, and recovered after freeze-drying. The conversion of both AMPTMA and n-BA and the theoretical MW of the amphiphilic block copolymer were determined by 1H NMR spectroscopy. Typical Synthesis of Random PAMPTMA-co-PBA Copolymer.
Several PAMPTMA-based random copolymers were synthesized by SARA ATRP, using the following procedure: AMPTMA (2.0 mL, 16.7 mmol), n-BA (1.5 mL, 10.4 mmol), and a solution of CuBr2 (23.5 mg, 0.10 mmol) and Me6TREN (55.0 μL, 0.21 mmol) in
ethanol (2.0 mL) were added to a Schlenk flask equipped with a magnetic stirrer bar. Cu(0) wire (l = 5 cm; d = 1 mm) was then added to the Schlenk flask, which was sealed with a glass stopper, frozen with liquid nitrogen, deoxygenated with three freeze−vacuum−thaw cycles, and purged with nitrogen. The reaction was allowed to proceed with stirring (360 rpm) at 30 °C during 15 h. The conversion of both AMPTMA and n-BA and the theoretical MW of the random copolymer were determined by 1H NMR spectroscopy. The resulting mixture was dialyzed (cutoff 3500) against distilled water, followed by ethanol and then water, and the purified copolymers were obtained after freeze-drying.
The star-shaped PAMPTMA-co-PBA copolymers were synthesized using the procedure described above but using either 4-EBiB or 6- EBiB as initiator for the preparation of 4-arm or 6-arm stars, respectively.
The theoretical number-average molecular weight of the copoly- mers was determined following the equation: Mn
Techniques. Polymer Characterization. 1H nuclear magnetic resonance (NMR) spectroscopy were performed using a Bruker Avance III 400 MHz spectrometer, with a 5 mm TIX triple-resonance detection probe, in D2O, d6-EtOD, or CDCl3 with TMS as internal standard. Conversion of the monomers was determined by integration of monomer and polymer peaks using MestRenova software version 6.0.2-5475.
PAMPTMA homopolymers were analyzed by a SEC system with an online degasser, a refractive index (RI) detector, and a set of columns: Shodex OHpak SB-G guard column, OHpak SB-804HQ column, and OHpak SB-802.5HQ column. The polymers were eluted at a flow rate of 0.5 mL min−1 with 0.1 M Na2SO4 (aq)/1 wt% acetic acid/0.02% NaN3 at 40 °C. Before the injection (50 μL), the samples were filtered through a nylon membrane with 0.20 μm pores. The system was calibrated with five narrow poly(ethylene glycol) standards, and the molecular weight (Mn
SEC) and polydispersity (Đ = Mw/Mn) were determined by conventional calibration using the Clarity software version 2.8.2.648.
PBA homopolymers were analyzed by high-performance size exclusion chromatography (HPSEC) using a Viscotek TDAmax instrument with a differential viscometer (DV), right-angle laser-light scattering (RALLS, Viscotek), low-angle laser-light scattering (LALLS, Viscotek), and RI detectors. The column set consisted of a PL 10 mm guard column (50 × 7.5 mm2) followed by one Viscotek T200 column (6 μm), one Viscotek T3000 column (6 μm), and one Viscotek LT4000L column (7 μm). For HPLC, a dual-piston pump was set with a flow rate of 1 mL min−1. The eluent (THF) was previously filtered through a 0.2 μm filter. The system was also equipped with an online degasser. The analyses were done at 30 °C using an Elder CH-150 heater. Before the injection (100 μL), the samples were filtered through a PTFE membrane with 0.2 μm pore. The system was calibrated with narrow polystyrene standards. The dn/dc used for PBA was 0.064.24 Molecular weights (Mn
SEC) and Đ (Mw/Mn) of the synthesized polymers were determined by multi- detector calibration using OmniSEC software version 4.6.1.354.
Dynamic light scattering (DLS) measurements were performed on a Zetasizer Nano-ZS instrument (Malvern Instrument Ltd., UK). The measurement was made at 25 °C, λ = 632.8 nm, using backward scattering angle of 173°. All samples were dissolved in LB bacterial growth medium at their minimum inhibitory concentration (MIC).
Strains and Growth Conditions. Bacterial indicator strains (Escherichia coli ATCC25922, Staphylococcus aureus ATCC25923, Bacillus subtilis ATCC23857, Bacillus cereus 9843, and Pseudomonas aeruginosa HST244P) were grown at 37 °C. MIC determination was performed by agar diffusion test according to the Clinical & Laboratory Standards Institute (CLSI)25 in Muller−Hinton (MH) solid medium or by microdilution procedure in microplates in Luria− Bertani (LB) medium.26
Minimum Inhibitory Concentration (MIC) Determination. All polymer stock solutions (100 or 500 μM) were prepared in ultrapure sterile water. The MIC of the polymers was evaluated using the standard broth microdilution method. In this case, 2-fold dilutions of
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the polymers were prepared in LB growth medium using 96-well microliter plate. Each well containing 150 μL of polymer solution was inoculated with 50 μL of bacterial suspensions prepared in LB medium (106 CFU mL−1) and further incubated at 37 °C for 24 h. Controls without polymer were performed for each bacterial strain. Each assay was repeated in triplicate to ensure reproducibility of the experiments. The MIC was the lowest concentration of polymer that completely inhibited bacterial growth. MIC values in weight-based concentration are provided in Supporting Information (Table ST1). Scanning Electron Microscopy (SEM). An E. coli bacterial
suspension of approximately 108 cells mL−1 in saline solution was incubated in the presence or absence of polymers for 5 h at 37 °C. Co-incubation of E. coli and polymers was tested at the MIC for each polymer. Cells were harvest by centrifugation and washed in phosphate-buffered saline (PBS). Washed cells were adhered to steel slides and fixed twice with 2.5% glutaraldehyde for 10 min at room temperature. The slides were dehydrated using a series of graded ethanol solutions (70, 80, 90, 95, and 100%) for 15 min at room temperature before drying. The dehydrated samples were gold- coated for 20 s and imaged under high vacuum using a Zeiss Merlin FEG-SEM microscope. Live/Dead Membrane Integrity Assay. Similarly, to the SEM
sample preparation, an E. coli bacterial suspension of 108 cells mL−1 in PBS was incubated in the presence or absence of polymer for 5 h at 37 °C. Co-incubation of E. coli and polymers was tested at the MIC for each polymer. Cells were harvest by centrifugation and washed in 1× PBS. Washed cells were stained with 3 μL of SYTO 9 and propidium iodine (PI) mixed solution (L7007 Live/Dead BacLight Bacterial Viability Kit, Invitrogen) and incubated at room temperature for 15 min in dark conditions. Next, 5 μL of dyed bacterial suspension was placed on a glass slide and covered with a glass coverslip. Cells were visualized under an Axioskop 2 Plus fluorescence microscope using FITC and Rhodamine filters. SYTO 9 (green dye) stains both live and dead cells, while PI (red dye) stains only dead cells. At least 100 cells in triplicates were counted in each experiment. Statistical Analysis. Statistical analysis was performed using the
software Prism 6. Two-way analysis of variance (ANOVA) was performed for live/dead assays. At least 100 cells in triplicate for n = 2 independent experiments were quantified. A p value of 0.05 or less was considered significant.
■ RESULTS AND DISCUSSION Polymer Synthesis and Characterization. It is known
from the literature3 that a proper balance of hydrophilic cationic segments and hydrophobic regions is a key factor for a polymer to achieve its maximum antimicrobial activity. Therefore, the control over the polymer composition is essential for the preparation of antimicrobial polymers with enhanced performance, which can be achieved by using RDRP techniques. In this work, SARA ATRP was explored for the preparation of cationic amphiphilic antimicrobial copolymers, based on PAMPTMA as the cationic segment and PBA as the hydrophobic domain. Prior work reported by our research group showed that star-shaped PMA-b-PAMPTMA block copolymers could be successfully prepared using an eco- friendly SARA ATRP system.27 Here, the same catalytic system was used for the polymerization of both AMPMTA and n-BA. Due to the very different nature of the monomers (AMPTMA is a highly hydrophilic monomer whereas n-BA is very hydrophobic), the first step was to select an appropriate solvent for the copolymerization. In this case, ethanol enabled homogeneous polymerization, dissolving all monomers and polymers. The successful polymerization of both AMPTMA and n-BA
by ATRP techniques has already been reported.28,29 However, to best of our knowledge, this is the first time that methods have been developed for the controlled synthesis of PAMPTMA-b-PBA block copolymers by “one-pot” SARA ATRP (Figure 1). First experiments were dedicated to the homopolymeriza-
tion of either AMPTMA (linear and star-shaped) or n-BA (linear) by SARA ATRP in ethanol in order to define the reaction conditions for a controlled and compatible synthesis of both polymers (Supporting Information, Figures SF1−SF3). Then, the reaction conditions were optimized for the preparation of the PMAPTMA-co-PBA (block) copolymers.
Figure 2. 400 MHz 1H NMR spectrum of purified PAMPTMA50-co-PBA15 copolymer (Mn th = 12.4 × 103) in d6-EtOD obtained by SARA ATRP.
http://pubs.acs.org/doi/suppl/10.1021/acs.biomac.8b00685/suppl_file/bm8b00685_si_001.pdf
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http://dx.doi.org/10.1021/acs.biomac.8b00685
Figure 2 shows a typical 1H NMR spectrum of a PMAPTMA- co-PBA copolymer, successfully prepared by SARA ATRP in ethanol at 30 °C. The recorded spectrum shows the characteristic signals of the cationic segment appearing at 3.38 ppm, corresponding to the quaternary ammonium group, and an isolated signal of the PBA segment at 4.55 ppm, corresponding to the -OCH2- protons of the ester group.
30,31
Using the developed SARA ATRP method, a library of different amphiphilic (co)polymers was prepared by targeting several parameters: MW, mole fraction of the hydrophobic segment (PBA), and architecture of the polymers (Table 1 and Supporting Information, Table ST2). PAMPMTA and PBA homopolymers were also synthesized and used as control samples. It is worth mentioning that the systematic study of the antimicrobial efficiency requires the preparation of polymers
with diverse characteristics (e.g., composition, architecture, etc.), which can only be achieved by means of advanced polymerization techniques, such as the SARA ATRP, ARGET, photo-ATRP, eATRP, and sono-ATRP.32−37 The samples were labeled according to the architecture and composition of the (co)polymers to facilitate the interpretation of the results: L, linear random (co)polymer; Lb, linear block copolymer; 4S, 4-arm star; 6S, 6-arm star; A, PAMPMTA segment followed by the correspondent DP value; B, PBA segment followed by the correspondent DP value. As an example, the sample LA27B33 (Table 1, entry 12) is a linear random copolymer with 27 units of PAMPMTA and 33 units of PBA. For the determination of the MW parameters of the
copolymers, SEC analysis using a refractive index detector and ethanol as the eluent was carried out. However, unfortunately,
Table 1. Characteristics of the Antimicrobial Polymers Prepared by SARA ATRP
1 6SA114B0 6-arm star homopolymer 114 0 142.0 0 2 6SA69B41 6-arm star random copolymer 69 41 117.4 37 3 6SA79B24 6-arm star random copolymer 79 24 117.3 23 4 6SA95B0 6-arm star homopolymer 95 0 117.0 0 5 6SA22B0 6-arm star homopolymer 22 0 28.1 0 6 6SA9B0 6-arm star homopolymer 9 0 12.7 0 7 4SA93B0 4-arm star homopolymer 93 0 77.6 0 8 4SA84B20 4-arm star random copolymer 84 20 80.5 20 9 LA38B2 linear random copolymer 38 2 8.3 5 10 LA34B9 linear random copolymer 34 9 8.4 20 11 LA23B23 linear random copolymer 23 23 8.0 50 12 LA27B33 linear random copolymer 27 33 10.0 56 13 LA50B4 linear random copolymer 50 4 11.0 7 14 LA45B15 linear random copolymer 45 15 11.5 24 15 LA39B47 linear random copolymer 39 47 14.2 55 16 LA115B0 linear homopolymer 115 0 24.0 0 17 LA98B29 linear random copolymer 98 29 24.2 23 18 LA59B44 linear random copolymer 59 44 18.2 42 19 LbA79B47 linear block copolymer 79 47 22.5 37 20 LbA78B63 linear block copolymer 78 63 24.4 44 21 LbA68B40 linear block copolymer 68 40 19.4 37 22 LbA264B33 linear block copolymer 264 33 59.0 10 23 LA335B0 linear homopolymer 335 0 69.4 0 24 LA0B49 linear homopolymer 0 49 6.4 100
aDegree of polymerization value per star arm.
Table 2. Characteristics of Linear PAMPTMA-co-PBA Copolymers with Different MW Values and Hydrophobic Characters and Respective MIC Values Obtained in Liquid Medium
MIC (μM)
polymer code DPAMPTMA/DPn‑BA %PBA (molar) Mn th × 10−3 S. aureus B. subtilis B. cereus E. coli P. aeruginosa
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it was found that the cationic segment (PAMPTMA) interacted with the column (Shodex Asahipak 5 μm GF-510 HQ, LC column 300 × 7.5 mm) either in the form of homopolymer or as a segment of the copolymers, which avoided the acquisition of chromatograms. Even with the addition of stabilizers (e.g., amines, LiBr, etc.) to the eluent, there was no separation of the polymers. Only the PBA homopolymer eluted properly in the conditions described. Therefore, for this study, the molecular weight used for the characterization of the polymers was the Mn
th (Table 1), taking into account that the polymerization of both monomers is well-controlled and gives polymers with Mn
SEC close to the theoretical ones. Influence of Molecular Weight and Hydrophobicity
on Polymers’ Antimicrobial Activity. The MW of an antimicrobial polymer has been previously found to play an important role in bacterial death induction.38 Recent studies have demonstrated bacterial inactivation and/or death by low MW polymers,9,39,40 based on the fact that such structures can easily diffuse through the lipid membrane. However, since the antibacterial activity largely depends on polycationic charges, high MW can provide a higher number of cationic units thus, improving the electrostatic forces and, simultaneously, the bactericidal effects.38,41 On the other hand, solubility issues, aggregation, and interaction with the cell wall barrier could become crucial with MW increase. Nevertheless, these factors are dependent on both the polymeric system employed and the type of bacteria tested. Here, three series of PAMPTMA-co- PBA linear random copolymers with targeted MW values around 8000, 12 000, and 24 000 were synthesized (Table 1, entries 9−18, except entries 15 and 16). In each series, the content of PBA was varied from 0% to 50% in order to evaluate the influence of the hydrophobicity of the polymers on their antimicrobial activity. The efficiencies were investigated by determining the MIC of the polymers (Table 2) against a range of Gram-positive (S. aureus, B. subtilis, and B. cereus) and Gram-negative (E. coli and P. aeruginosa) bacteria in liquid medium. For the copolymers with a low percentage of PBA (LA38B2
vs LA50B4in Table 2), the increase of the MW led to an enhancement of the antimicrobial activity for all Gram-positive
and Gram-negative bacteria tested. The same behavior was observed for the PAMPMTA homopolymers used as control samples (LA115B0 and LA335B0 in Table 2), as well as for the copolymers with 20% PBA in a less pronounced variation of the MIC values (LA34B9 vs LA45B15 vs LA98B29 in Table 2). However, for the highest contents of PBA studied (up to 50%), the antimicrobial activity of the copolymers was dependent on the bacterial species tested (LA23B23 vs LA27B33 vs LA39B47 in Table 2). In general terms, the amphiphilic copolymers investigated were more active toward B. subtilis bacteria, as jugged by the lower MIC values. While the cationic segments of the polymers provide
electrostatic interaction with the negatively charged bacterial cell wall, the presence of hydrophobic domains within the copolymer structure is also important, since it can allow for a better permeation of the polymer through the bacteria lipid membrane.42 Several studies have shown that the antimicrobial activity of amphiphilic structures is higher than that of hydrophilic (cationic) analogues,18,43 and it increases with the hydrophobicity character of the amphiphilic struc- tures.9,14,44 However, in this work the variation of the percentage of the hydrophobic PBA segment in the copolymers did not influence their antimicrobial activity. Only a small improvement was observed for the samples with MW around 8000 (LA38B2 vs LA34B9 vs LA23B23 in Table 2), for B. cereus and E. coli, suggesting that only low MW polymers that could permeate the bacteria lipid membrane can benefit from having hydrophobic domains. Possibly, the majority of the polymers investigated in this work have high MW (>10 000), preventing them from penetrating into the bacterial membrane. In fact, the PAMPTMA homopolymers (LA115B0 and LA335B0 in Table 2) used as control samples (no hydrophobic domains) showed the broadest spectrum of antimicrobial activity, along with some of the lowest MIC values, compared to the amphiphilic PAMPTMA-co-PBA copolymers. Besides the high MW of the samples, it was hypothesized that the behavior observed in this work might be attributed to the fact that the increase of the hydrophobicity could lead to different self-assembled polymeric structures in aqueous media.43 To investigate this, DLS analyses of three different representatives amphiphilic copolymers (highest
Table 3. Characteristic of Linear and Star-Shaped PAMPTMA-co-PBA Copolymers and MIC Values Obtained in Liquid Medium
MIC (μM)
polymer code DPAMPTMA/DPn‑BA a %PBA (molar) Mn
th × 10−3 S. aureus B. subtilis B. cereus E. coli P. aeruginosa
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content of PBA, LbA68B40, and LA39B47; lowest content of PBA, LbA264B33), at their respective MIC values in the growth medium used for the antimicrobial activity assays, were performed. The results (Supporting Information, Figure SF4) showed size distributions in the range of 1 nm up to 10 nm, which suggest that these polymers do not form aggregates but exist as single chains (unimers) at their MIC. Therefore, in this case the effect of polymer aggregation on the antimicrobial activity can be ruled out. The PBA homopolymer (LA0B49 in Table 1) did not show any antimicrobial activity against the bacteria strains investigated. This result highlights the need for a cationic segment in the structure of a polymer in order to exhibit good antimicrobial activity. In summary, this set of results indicates that the
antimicrobial activity of the studied copolymers does not show dependence with hydrophobicity, but it is positively influenced by the increase of the MW of the polymers. Influence of Architecture on Polymers’ Antimicrobial
Activity. The SARA ATRP method is a very useful tool to prepare polymers with distinct architectures, which can result in materials with different antimicrobial performances. Star- shaped polymers have been pointed out as promising antimicrobial agents, with a considerable higher activity than their linear analogues.18 Here, new PAMPMTA homopolymer stars and PAMPTMA-co-PBA star copolymers were tailored to have different number of arms, MW values, and hydrophobic contents to investigate the influence of the architecture on their antimicrobial performance (Table 1, entries 1−8). The MIC values of the polymers were obtained in liquid medium, and the results are shown in Table 3. In general, the MIC values either increased or remained
constant with PBA content increase, as previously observed for the linear copolymers. Compared to the star-shaped cationic PAMPTMA homopolymers, the amphiphilic star-shaped copolymer analogues were much less active against bacteria. This effect was highly visible for S. aureus, with MIC values at least 8-fold higher than those of the homopolymers (Table 3, 6SA114B0 vs 6SA69B41 and 6SA79B24; 4SA93B0 vs 4SA84B20). When compared to its star copolymer analogues (4SA84B20
and 6SA79B24 in Table 3), the linear copolymer (LA98B29 in Table 3) presented higher MIC values, except for P. aeruginosa. This decrease in antimicrobial efficacy might be associated with the initial adsorption/binding interaction between polymer and bacteria, which is stronger for star-shaped compared to linear polymers.19 Moreover, it is expected that star-shaped polymers present high charge density which provides increased interactions between these polymeric
structures and the anionic compounds of the bacterial cell wall.45 This behavior has been previously observed by other authors for peptide stars18 or nanoparticles formed by self- assembly of linear peptides.46 However, for this conclusion it is worth mentioning that the star-shaped polymers have higher molecular weight (MWstar ≈ MWlinear × number of arms) than their linear analogues, since the linear polymers studied are equivalent in MW to one arm of the star. This rationalization has also been used by other authors and could be argued due the importance of the amounts of positive charges in polymer activity.18 The increase of antimicrobial activity from linear to star, in these cases, could be then associated with the increase of the MW of the polymers, as previously observed. The real influence of the architecture could be then
evaluated by comparing linear and star-shaped polymers with the similar MW values. The results presented in Table 3 show that the MIC values of linear and star-shaped PAMPTMA homopolymers (6SA22B0 vs LA115B0 and 4SA93B0 vs LA335B0) are in the same range for the bacteria investigated, suggesting that in fact the architecture itself might not play a key role in the antimicrobial activity. In this case, it seems that the MW of the polymers has a greater influence on their performance, than the particular shape. The results obtained with star shaped polymers seem to be only related to the increase in the MW of the polymer (considering the same DP for the star arm). On this matter, the preparation of stars by SARA ATRP is still an advantageous strategy since it is a straightforward route for the synthesis of very high MW polymers, under controlled conditions (Đ < 1.5). The most effective polymers prepared in this work (e.g., 6SA114B0 and 4SA93B0) were active for all the bacteria strains investigated and presented lower MIC values, in particular for Gram- positive, than amphiphilic peptide nanoparticles reported in the literature,46 making them excellent candidates for antimicrobial applications. Besides the good performance of the polymers presented in this work, the synthesis via SARA ATRP is much more affordable than the chemistry involving the preparation of polypeptides.
Influence of the Structure of Copolymers on the Antimicrobial Activity. As previously mentioned, several studies investigated the role of key factors that control the bactericidal activity of polymers.19,39,47 Structural parameters such as amphiphilicity,48 MW, and type and density of cationic charges19 have been shown to influence the biological activity of different polymers. However, there are relatively few reports investigating amphiphilic block copolymers.47,49 Here, the MIC values of amphiphilic PAMPTMA-co(b)-PBA random and block copolymers with MW and molar ratio similar to
Table 4. Characteristics of PAMPTMA-co-PBA and PAMPTMA-b-PBA Copolymers and MIC Values Obtained in Liquid Medium
MIC (μM)
polymer code DPAMPTMA/DPn‑BA a %PBA (molar) Mn
th × 10−3 S. aureus B. subtilis B. cereus E. coli P. aeruginosa
http://pubs.acs.org/doi/suppl/10.1021/acs.biomac.8b00685/suppl_file/bm8b00685_si_001.pdf
http://dx.doi.org/10.1021/acs.biomac.8b00685
those of n-BA were evaluated in order to understand if the distribution of the hydrophobic domains within the copoly- mers affects their antimicrobial activity. The antimicrobial activity of block copolymers was higher
against Gram positive bacteria when compared to random copolymers with similar MW and composition (LA59B44 and LbA68B40 in Table 4). Nonetheless, for E. coli, the random copolymer has a better performance, showing a lower MIC value than the analogue block copolymer. These results are in sharp contrast with previous data reported in the literature, showing that the bactericidal activity of random and block amphiphilic poly(vinyl ether)s copolymers was similar against E. coli.47 Based on these observations, it seems that the structure of the copolymers could influence their antimicrobial activity depending on the bacterial strain considered and the nature of the monomers. It is then important to investigate the performance of antimicrobial polymers in conditions mimick- ing those of the targeted application of the materials (e.g., target bacteria strains, in vivo or ex vivo application, etc.). Similarly to what was observed for the random copolymers,
the decrease of the hydrophobic content in the block copolymers and the increase in the MW led to an increase of the antimicrobial activity (LbA264B33 sample in Table 4 exhibited the lowest MIC values). Overall, block copolymers showed the broadest spectrum of activity and lower MIC values. Preliminary Studies on Cell−Polymer Interactions. In
order to evaluate whether the antimicrobial effect of polymers
was due to loss of bacterial membrane integrity, two distinct approaches to assess membrane damage were used: SEM and fluorescence microscopy, as described in the Experimental Section. For this study, representative PAMPTMA homopol- ymers and PAMPTMA-co-PBA copolymers of each architec- ture (linear and star-shaped) were selected on the basis of their antimicrobial performance. In both techniques, E. coli cells were incubated for 5 h with 4-arm star (4SA84B20 and 4SA93B0, Table 1), 6-arm star (6SA114B0 and 6SA69B41, Table 1), or linear (LA115B0 and LA98B29, Table 1) polymers at their MIC. As shown in Figure 3, untreated E. coli cells exhibited rod-
shaped morphology and smooth surface. Treatment of the cells with the star-shaped polymers (4SA84B20, 4SA93B0, 6SA114B0, and 6SA69B41, Table 1) caused increased granules at the cell surface (cell debris) and lysed cell morphologies, regardless the number of arms of the stars. Treatment with linear polymers (LA115B0 and LA98B29, Table 1) also caused increased cell debris granules at the cell surface, but to a lesser extent than the star-shaped polymers. To further investigate whether the antimicrobial activity of these polymers is in fact due to direct damage of the bacterial membrane, a second approach using live (Syto 09)/dead (propidium iodide) staining was used to assess membrane damage after polymer treatment. In agreement with the SEM results, all tested polymers displayed a significant increase in cell death by membrane damage compared with untreated cells (p < 0.0001) (Figure 3). In particular, the 6-arm star polymers with the
Figure 3. SEM and fluorescence microscopy images of E. coli cells before and after treatment with 6-arm and 4-arm star-shaped and linear PAMPMTA homopolymers and PAMPTMA-co-PBA copolymers. Conditions: 5 h incubation at concentration of MIC. Live/dead staining shows live (SYTO 09-labeled) cells in green and dead (propidium iodide-labeled) cells in red. Percentage of death by membrane damage for untreated and 6SA114B0-, 6SA69B41-, 4SA84B20-, 4SA93B0-, LA115B0-, and LA98B29-treated E. coli cells. At least 100 cells were counted in triplicates for at least two independent experiments. ***p = 0.0001; ****p < 0.0001.
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highest MW values (6SA114B0 and 6SA69B41, Table 1), exhibited the highest death by membrane damage (85.1% and 90.5%, respectively) compared with 2.5% death in E. coli untreated cells. 4SA84B20 and 4SA93B0 (4-arm star polymers, Table 1) also showed increased cell death by membrane damage (72.0% and 62.1%, respectively). Linear polymers (LA115B0 and LA98B29, Table 1) showed the lowest cell death among all polymer architecture types, but still as high as 58.9% and 52.7% for the homopolymer and random copolymer, respectively. When comparing bacterial membrane damage by different amphiphilic polymers, cell damage was higher for copolymers, except the linear ones. This result suggests that polymers with amphiphilic character and high MW increase membrane damage, leading to cell death.
■ CONCLUSIONS An extensive library of effective well-defined PAMPTMA- based antimicrobial (co)polymers, with different architectures, MWs, and compositions, was prepared by a simple and scalable SARA ATRP method at near room temperature. Despite the difficulty to establish general correlations between polymer features and antimicrobial activity, the results showed that (i) generally amphiphilic structures were more effective toward B. subtilis bacteria; (ii) increasing hydrophobicity (PBA segment) had no influence on the antimicrobial activity; (iii) the presence of the cationic segment is mandatory for the antimicrobial properties of the (co)polymers; (iv) star-shaped polymers with the similar MW as linear analogues presented similar antimicrobial activity; and (v) the antimicrobial activity was enhanced by the increase of the MW. The synthesis of star-shaped polymers represents a convenient route to obtain high MW polymers with remarkable antimicrobial activity. Preliminary SEM and fluorescence microscopy studies on the interaction between polymers and bacteria revealed that both cationic homopolymers and amphiphilic copolymers were able to kill E. coli bacteria by membrane damage.
■ ASSOCIATED CONTENT *S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.bio- mac.8b00685.
Results of the homopolymerization of both AMPMTA and n-BA by SARA ATRP in ethanol, MIC values in weight-based concentration and DLS volume distribu- tions of representatives PAMPTMA-co(b)-PBA (PDF)
■ AUTHOR INFORMATION Corresponding Author *E-mail: jcoelho@eq uc.pt. ORCID Armeńio C. Serra: 0000-0001-8664-2757 Jorge F. J. Coelho: 0000-0001-9351-1704 Notes The authors declare no competing financial interest.
■ ACKNOWLEDGMENTS Patrićia V. Mendonca̧ acknowledges a postdoctoral grant (SFRH/BPD/117589/2016) supported by the Portuguese Foundation for Science and Technology (FCT). Madson R. E. Santos acknowledges CNPq (Conselho Nacional de Desen-
volvimento Cientifíco e Tecnoloǵico) for a doctoral fellowship (202484/2015-7). Rita Branco acknowledges a postdoctoral grant (SFRH/BPD/110807/2015) supported by the Portu- guese Foundation for Science and Technology (FCT). Mariana C. Almeida acknowledges a postdoctoral grant within the Project ReNATURE – Valorization of the Natural Endogenous Resources of the Centro Region (Centro 2020, Centro-01-0145-FEDER-000007). The authors thank Post- nova for valuable technical discussions about the molecular weight characterization of polymers. The authors thank the Portuguese Foundation for Science and Technology (FCT) for funding the Safesurf project (PTDC/CTMPOL/6138/2014). The 1H NMR data were collected at the UC-NMR facility, which is supported in part by FEDER − European Regional Development Fund through the COMPETE Programme (Operational Programme for Competitiveness) and by Na- tional Funds through FCT − Fundaca̧õ para a Cien̂cia e a Tecnologia (Portuguese Foundation for Science and Technol- ogy) through grants REEQ/481/QUI/2006, RECI/QEQ- QFI/0168/2012, and CENTRO-07-CT62-FEDER-002012, and Rede Nacional de Ressona ̂ncia Magnet́ica Nuclear (RNRMN).
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http://pubs.acs.org/doi/abs/10.1021/acs.biomac.8b00685
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mailto:jcoelho@eq uc.pt
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At some point almost every inmate will be eligible to be released from prison. As the authors point out in Chapter 15 there are a number of release mechanisms that are available. Research these release mechanisms and discuss which ones may be more effective and how they can best serve the inmate and society in general.
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This module will focus on variable and samples to determine a methodology to use, in relation to what you are seeking to know, a narrowing of the potential influences needs to occur. These influences are known as variables and play a crucial role in your research intentions. For the next few minutes we will explore some of these. Once the variables are considered, the next step in obtaining data is to identify a source. Often this will be one or more samples taken to better understand and resolve the research question. Being able to assess the validity of findings taken from samples, and the extent of reliability if repeated, are objectives to focus on here.
Clarify the Problem or Purpose
Single variable research
Remaining open to outcomes
Multiple variable research
Crafting a hypothesis or research question
Slide 3 Transcript If you are studying a single phenomenon you will be addressing a single variable primarily. Otherwise, you can study two or more variables, but there will be tradeoffs. Depending on which type of research approach you choose, you may develop relationships or compare ideas which may not be that clear at the beginning of your research. So, it is important not to imply you already know the outcome. With a hypothesis, you believe there will be confirmation of a difference you suspect. The research question will be working from a tentative definition to launch the effort.
Relevance of Hypothesis or Research Statement
Hypothesis for quantitative research designs
Both apply to data collection, analysis, and interpretation
Research statement for qualitative research designs
Both express the purpose, scope, and direction of study
States assumptions
Provides delimitations
Slide 5 Transcript We can expect to see a hypothesis used with experimental research, while research questions (also called statements) are more common in qualitative designs. Both provide guidance for the type of data to collect and suggest how analysis and interpretation may be accomplished. As mentioned previously, a research statement announces the purpose or reason for the inquiry, scope of how much of the problem is being taken on, and direction or trajectory of the outcomes expected. Another factor is in delimiting the research which is to say what is not going to be studied and why. This can be thought of as establishing parameters for your inquiry, and necessarily will limit how far you can generalize or broaden the applicability. Part of setting the limitations is in stating assumptions, which must be carefully considered since they invite critical assessment and the need to justify including them while excluding others.
This is where variables and sampling play a critical role, since the extent of influences that bear upon your focus for study, and the characteristics of samples to be used as evidence, can become entangled and may dilute the strength of your findings.
Various Variables
Quantitative Approach Qualitative Approach Seeks to measure outcomes Discovers variables for study at a
later opportunity
Other Types of Variables Intervening Organismic Dummy Confounding Extraneous Dichotomous Control Criterion Latent Manifest Predictor
Independent variables controlled by researcher
Mediating variables explain effects
Dependent variables measure the effect
Slide 7 Transcript In quantitative research, we typically refer to independent and dependent variables. This can appear simplistic. We often hear that the independent variable is what is introduced or manipulated by the researcher to see what effect it has on something, while the dependent variable is a measure of the extent of that effect. Also playing key roles are any mediating variables that help explain why the independent variable produced this effect – so, the independent variable influences the mediating variable which in turn influences the dependent variable. We can begin to see the complications where multiple independent, mediating, and dependent variables are interacting. There are many other names for variables, including intervening, confounding, or control variables. For instance, variables might include on-time airline performance, fatal accident rates, age of an airline fleet, customer satisfaction, weather delays, or maintenance costs per mile. In qualitative designs, variables emerge during the inquiry and are of less emphasis because numerical measures are not used. While quantitative measures seek to confirm something, qualitative concepts emerge and subsequently can generate hypotheses and associated variables to measure.
When to Use Which Variables
Important for design and statistical analysis
Qualitative Not usually involved with qualitative designs Non-numerical
Slide 9 Transcript Identifying which variables will be considered is important in helping to choose which research design to use, and, what statistical analysis is appropriate. Quantitative variables are numeric and represent a measurable quantity. For example, measures of height, temperature, or speed can be expressed in centimeters, Celsius, or kilometers per hour. Measures can be ordinal, ranked, continuous, discrete, variable, or in intervals. Depending on how you intend to measure the dependent variable will determine how you can express relationships among other variables. In quantitative designs, variables will vary by amount, whereas in qualitative designs properties will be non-numeric and identified with labels or categories. So, quantitative variables are measures on a continuous scale (or in identifiable discrete categories), while what might be considered qualitative variables are generally not on a continuous scale and are mostly in discrete categories.
Establishing Validity
The Issue is Credibility Many Types of Validity Face Construct Content Criterion Ecological (Multiple types associated with statistical results)
Strong Evidence That Assertion is Genuine
Slide 11 Transcript Validity refers to how credible a research finding might be – that is, are the findings genuine and believable. We have heard the definition of validity as “the extent to which something measures what it is supposed to measure”, but this have never been a satisfactory explanation for me. Validity derives from a Latin term for strong. So, if something is valid, there is strong evidence that is it is genuine. Several types of validity have been identified and include face validity (or it seems apparent), construct validity (is measuring the variable of interest), content validity (is measuring what is contained in the construct), criterion-related validity (the measure can predict an expected outcome), and ecological validity (measures what actually exists in real conditions).
Demonstrating Reliability
If Valid, Then Reliable Two Basic Types of Reliability
Internal Split-half Cronbach’s alpha
Repetition Means Reliable
External Test-retest Inter-rater Cohen’s kappa
Slide 13 Transcript If data are valid, they also must be reliable. Reliability indicates how likely the same result would be found if the study were repeated. There are two basic types of reliability – internal and external. Internal reliability indicates how consistent the measure is within itself. A common method for this is the split half procedure. For example, you might split an examination or set of measures into two equal parts (selecting items at random, for instance), then comparing if the means are significantly different. External reliability measures how different results are compared with each other over several trials or episodes. A couple of examples would be test-retest where the same examination administered to different participants remains stable over time with similar score distributions. Another measure for external reliability would be the inter-rater assessment which would evaluate the degree to which different raters give consistent estimates for what is being rated. Statistical measures for reliability might include using several measures for internal consistency with the same variable, a measure known as Cronbach’s alpha. For inter-rater reliability you might use a measure known as Cohen’s kappa.
Taking participants from the entire population True random sampling (or, without replacement)
Taking a portion of the population to study But, is that portion representative
Taking participants from the entire population True random sampling (or, without replacement)
Other Names for Sampling
Slide 15 Transcript Seldom can a researcher capture everything or everyone who would be included in interpreting results from a study. So, they take a portion of the total population and, hopefully, infer that what applies for the sample is also true among the population. Clearly, issues of representativeness arise and whether the sample accurately resembles what would be found respectively in the population under consideration. When humans are involved, were refer to them as participants, not as subjects. Two categories are often used to describe sampling – probability and nonprobability. These terms refer to whether you expect to be able to predict something accurately about the population. Probability sampling involves selecting directly from an entire population, while nonprobability sampling selects participants from the population who are accessible, thereby excluding some. This was a problem in the days of designing drug testing samples for the workplace. A truly random sample, for instance, meant that everyone in the target group was accessible for selection. However, when some members may have been unavailable because of sickness, travel, or other reasons, only those remaining were available for testing. Various names for sampling include convenience sampling (ease of access to participants, e.g., whoever walks up at the time), quota sampling (an equal number per category are chosen), stratified random sampling (which differs because there are subgroups identified), systematic sampling (e.g., every third person), cluster sampling (readily identifiable or accessible persons in a subgroup), and so on. A word about random sampling – truly random sampling means each participant has an equal chance of being selected each time a selection is made. To do this, for example, you might have a bowl with the names of everyone on it. One is selected, and then returned to the bowl. This means they might be selected again. The more common technique used, though, is random sampling without replacement, meaning their name does not get returned to the bowl. This is what is used in the workplace drug testing scenario, for instance.
Considerations for Predicting Outcomes
Participant Reactivity
Changes (limitations) affect variables
Expectancy Effects
Experimenter Bias
Aspects relating to qualitative approaches
Confirmation bias
Sensitivity and Range Effects
We find what we look for,
and we look for what we know.
“
“
Slide 17 Transcript
There are some considerations we want to look at when we are thinking of a methodology to predict some type of outcome. Participant reactivity, which is like the Hawthorne Effect from your Introductory Psychology course, refers to influences on participants when they know they are being measured. Experimenter bias relates to when the behavior of a researcher influences results, perhaps through tone of voice, body language, and so on. Often, the researcher is not aware of these influences. It is worth noting that, in qualitative research designs, this often has been a criticism of how the results are distorted. However, it actually is at the very heart of the difference in that variety can produce some very different results and lead to unforeseen discoveries. Expectancy effects are like the self-fulfilling prophesy that David Hamburg described. I am reminded of something a neurologist who was supervising my residency said, “We find what we look for, and we look for what we know.” This is a bias studied extensively and can shape the way results are produced, collected, and interpreted. So, when setting up an inquiry, if we already think we know the results, we are biased for look for confirmation. Sensitivity and range effects involve situations where the actual measure can change or be different based on how the independent variable is applied. The range issue is one where the researcher may limit which scores or values are considered in the extreme, and therefore are not included in the calculations.
Types of Sampling Error
Sampling Error
Favoritism
RememberStandard Error of the Mean
Suggests samples are not representative
Parameter = Population
Bias
How much mean values differ NonresponseStatistic = Sample
Slide 19 Transcript
When we do samples, we introduce error. Sampling error refers to the extent to which the mean values from samples are not really comparable because they do not accurately represent identical portions of the population. Standard error of the mean refers to how far a sample mean might be deviating from the actual population mean. The error can be reduced by increasing the size of the sample. Remember, a parameter is a characteristic of a population, where a statistic is a characteristic of a sample. Inferential statistics allows a researcher to make a calculated estimate about a population parameter based on a statistic from a sample randomly drawn. Error can be introduced through bias, which means the researcher used a sampling procedure that favored certain participants for instance. For example, an on-line survey might exclude those not computer savvy or with less sophisticated experience using the software. Another type of sampling bias is nonresponse bias, where some targeted participants choose not to respond, for a variety of reasons, but which skews the results and is not representative of the population studied. These are flaws built in to the design. That’s it for variables and samples so far. Thanks for listening and have a great week.
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Asci 670
2.2 lecture.
View the presentation and listen to the explanations offered. When completed, reflect on the presentation and write a brief statement that describes what you found to be an important aspect of the information and how that might help you with your research process.
I will attach the power point presentation attached to this assignment…
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