Get help from the best in academic writing.

University of Phoenix Introduction to Emotional Intelligence Questions

University of Phoenix Introduction to Emotional Intelligence Questions.

Link to the podcast available here: https://www.offtheclockpsych.com/podcast/emotional-intelligence
Be sure that your responses are adequately thorough and detailed. Some of the answers may just be fill-in the blank while others clearly require more detail and full sentences.  It might be helpful to take notes while listening to the podcast in order to appropriately answer the questions below. Keep in mind that the questions are asked in order of the podcast’s progression, making it easier to identify the answer.
Most of the answers to the questions below come directly from the podcast. There are a few questions which are considered “Personal Reflection” questions and are thus indicated as such in parentheses to avoid any confusion.
Questions
1.According to Dr. Goleman, what are the four parts of emotional intelligence
2.According to Dr. Goleman, what is the difference between “having these abilities and not having them”?
3.Describe the difference between the significance of IQ (Intelligence Quotient) and EI (Emotional Intelligence) with relation to professional success (especially in leadership roles).
4.According to the peer-reviewed article mentioned by Dr. Goleman in the podcast, beyond what IQ score is there no longer a “correlations with leadership success or performance”?
5.In Dr. Goleman’s review of competence models from a range of organizations, how many of the distinguishing competencies were in the Emotional Intelligence domain versus the IQ (i.e., “purely cognitive”) domain?
6.What percentage of distinguishing competencies for top leadership roles were found to be in the emotional intelligence domain?
7.According to the historian, Yuval Noah Harari, what sorts of skills should we be teaching children in school?
8.What do you think is the value of teaching children these skills (i.e. Emotional Intelligence or Social & Emotional Learning)? What do you think would be different in your life if you were taught and developed these skills in childhood? Please elaborate. (Personal Reflection)
9.What is the “brain’s radar for threat”?
10.According to Dr. Goleman, what is the “main function of the brain”?
11.According to Dr. Goleman, how has the nature of human problems shifted over our evolutionary history with regards to what triggers the amygdala?
12.What happens in our brains “when the amygdala declares and emotional emergency”?
13.What happens to our attention when this occurs?
14.What happens as a result of our attention-getting fixated on a perceived threat?
15.This process of fixation on a threat leads to what psychologists refer to as _____. (Hint: Starts with an “r”) (Fill in the blank)
16.What sorts of things have you ruminated (“uncontrollably worried”) about in the past? Did you find it difficult to focus on or think about other things? What were the adverse consequences of your rumination? (Personal Reflection)
17.According to Dr. Goleman, what is the “function of worrying”? And how is it different from rumination?
18.Evaluate the times when you worry and indicate whether you think it is helpful or unhelpful. Do you think the situation would still turn out fine if you didn’t worry? Please explain. (Personal Reflection)
19.According to Dr. Goleman, what is the “other function” of the amygdala?
20.Describe what Dr. Goleman refers to as an “amygdala hijack.” (If it isn’t fully clear from the podcast, then feel free to Google it.)
21.In the context of the “amygdala hijack”, describe an incident where you can identify an emotional reaction that was learned in childhood but was inappropriately exaggerated given the incident at hand. What was the consequence? Did you feel remorseful or regretful about your reactions? (Personal Reflection)
22.Dr. Goleman relays a story about “the wise owl and the guard dog” that he refers to as “neuroscience for 5-year-olds.” What are the anatomical equivalents for the “guard dog” and the “wise owl”?
23.What does Dr. Goleman offer as “one definition of maturity”?
24.Dr. Goleman states that, upon reviewing the soundest scientific studies on meditation, the effects that show up first are improved concentration skills, “which is obvious, because at base, every kind of meditation or mindfulness is training ____.” (Fill in the blank)
25.What is the other common finding in meditation studies that Dr. Goleman mentions?
University of Phoenix Introduction to Emotional Intelligence Questions

The Biological Effects Of Ionizing Radiation Biology Essay

The Biological Effects Of Ionizing Radiation Biology Essay. The biological effects of ionizing radiation are determined by both the radiation dose and the radiation quality ionization density. To understand the radiation protection concerns associated with different types of ionizing radiation, knowledge of both the extent of exposure and consequent macroscopic dose absorbed – gray value, as well as the microscopic dose distribution of the radiation modality is required. The definitions of these variables are discussed below but in general to advance the knowledge of the biological effects of different radiation types one needs to know the dose absorbed, the radiation quality and effectiveness of a particular radiation type to induce biological damage. In this study the biological effect of high energy neutrons is compared to that of a reference radiation type 60Co γ-rays for a cohort of donors, mostly radiation workers. Comparisons are made at different dose levels in blood cells from each donor to ascertain the relative biological effectiveness of the test radiation modality against that of a recognized reference radiation (Hall, 2005). Such studies are essential to determine the radiation quality for high energy neutron sources applicable to practises in radiation protection. In some nuclear medicine applications radionuclides are used to treat malignant disease. For this the use of short lived alpha particle emitters or other radiation modalities that deliver high ionization densities in cells, are particularly attractive. This as the cellular response in relation to inherent radiosensitivity of the effected cells is thought to be more consistent compared to the use of radionuclides that emit radiation with a lower ionization density e.g. β-particles. The relative biological effectiveness of the high energy neutrons used in this study is followed as a function of the inherent radiosensitivity of different individuals. This allows the identification of cell populations that are relatively sensitive or relatively resistant to radiation. As such research material is available to investigate cellular response too Auger electrons. The latter is known to induce biological damage akin to that of alpha particles. A short description of the physical and biological variables applicable to this study is summarised below. Ionizing Radiation The term ionizing radiation refers to both charged particles (e.g., electrons or protons) and uncharged particles (e.g., photons or neutrons) that can impart enough energy to atoms and molecules to cause ionizations in that medium, or to initiate nuclear or elementary-particle transformations that in turn result in ionization or the production of ionizing radiation. Ionization produced by particles is the process by which one or more electrons are liberated in collisions of the particles with atoms or molecules (The International Commission on Radiation Units and Measurements [ICRU] Report 85, 2011). Interaction of Ionizing Radiation with Matter Ionizing radiation is not restricted to ionization events alone. Several physical and chemical effects in matter such as: heat generation, atomic displacements, excitation of atoms and molecules, destruction of chemical bonds and nuclear reactions may occur. The effects of ionizing radiation on matter depend on the type and energy of radiation, the target, and the irradiation conditions. Radiation can be categorized in terms of how it induces ionizations: Directly ionizing radiation, consist of charged particles such as electrons, protons and alpha particles. Indirectly ionizing radiation consists of neutral particles and/or electromagnetic radiation such as neutrons and photons (γ-rays and X-rays). Ionising radiation interacts with matter by: Interaction with the electron cloud of the atom, or by Interaction with the nucleus of the atom. Types of ionizing radiation linked to this study γ-rays Ionizing photons (γ- and X-rays) are indirectly ionising radiation. These wave like particles have zero rest mass and carry no electrical charge. Low energy (E>2m0c2) may be absorbed by atomic nuclei and initiate nuclear reactions (Cember, 1969). The charged electrons emitted from the atoms, produce the excitation and ionisation events in the absorbing medium. Neutrons Neutrons, similar to ionizing photons are indirectly ionizing radiations; however, these particles do have a rest mass. There is negligible interaction between neutrons and the electron cloud of atoms since neutrons do not have a net electrical charge (Henry, 1969). The principle interactions occur through direct collisions with atomic nuclei during elastic scattering events. In this process, ionisation is produced by charged particles such as recoil nuclei and nuclear reaction products. The production of secondary ionising photons will result in the release of energetic electrons. In turn these charged particles can deposit energy at a considerable distance from the interaction sites (Pizzarello, 1982). Auger electrons Auger electron emission is an atomic-, not a nuclear process. In this process an electron is ejected from an orbital shell of the atom. A preceding event, e.g. electron capture (EC) or internal conversion (IC) leaves the atom with a vacant state in its electron configuration. An electron from a higher energy shell will drop into the vacant state and the energy difference will be emitted as a characteristic x-ray (Cember, 1969). The energy of the x-ray (Ex-ray) being the difference in energy (E) between the two electron shells L and K. Ex-ray = EL -EK Alternatively, the energy may be transferred to an electron of an outer shell, causing it to be ejected from the atom (Fig. 1). The emitted electron is known as an Auger electron and similarly to the x-ray has an energy: EAuger = EΔ -EB where: EΔ = the energy of inner-shell vacancy – energy of outer-shell vacancy EB = binding energy of emitted (Auger) electron Auger emission is favoured for, low-Z materials where electron binding energies are small. Auger electrons have low kinetic energies; hence travel only a very short range in the absorbing medium (Cember, 1969). File:Auger Process.svg Fig. 1: Schematic representation of the Auger electron emission process, where an orbital electron is ejected following an ionization event. Dosimetric Quantities Several dosimetric quantities have been defined to quantify energy deposition in a medium when ionizing radiation passes through it. Radiation fields are well described by physical quantities such as particle fluence or air kerma free in air are used. However these quantities do not relate to the effects of exposure on biological systems (International Commission on Radiological Protection [ICRP] Publication 103, 2007). The absorbed dose, D, is the basic physical quantity used in radiobiology, radiology and radiation protection that quantifies energy deposition by any type of radiation in any absorbing material. The International System of Units (SI) of absorbed dose is joule per kilogram (J.kg-1) and is termed the gray (Gy). Absorbed dose, D, is defined as the quotient of mean energy, dε, imparted by ionising radiation in a volume element and the mass, dm, of the matter in that volume (Cember, 1969). The absorbed dose quantifies the energy imparted per unit mass absorbing medium, but does not relate this value to radiation damage induced in cells or tissue. The radiation weighted dose (HT) is used as a measure of the biological effect for a specific radiation quality on cells or tissue. It is calculated from equation where DT,R is the mean absorbed dose in a tissue T due to radiation of type R and wR is the corresponding dimensionless radiation weighting factor. The unit of radiation weighted dose is J.kg-1 and is termed the sievert (Sv). Radiation weighting factors are recommended by the International Commission on Radiological Protection (International Commission on Radiological Protection [ICRP] Publication 103, 2007) and are derived from studies on the effect of the micro-deposition of radiation energy in tissue and on its carcinogenic potential. Linear Energy Transfer (LET) Ionizing radiation deposits energy in the form of ionizations along the track of the ionizing particle. The spatial distribution of these ionization events is related to the radiation type. The term linear energy transfer (LET) relates to the rate at which secondary charged particles deposit energy in the absorbing medium per unit distance (keV/µm). LET is a realistic measure of radiation quality (Duncan, 1977). The LET (L) of charged particles in a medium is defined as the quotient of dE/dl where dE is the average energy locally imparted to the medium by a charged particle of specified energy in traversing a distance dl (Pizzarello, 1982). For high energy photons (x- and γ-rays), fast electrons are ejected when energetic photons interact with the absorbing medium. The primary ionization events along the track of the ionizing particle are well separated. This type of sparsely ionizing radiation is termed low-LET radiation. The LET of a 60Cobalt teletherapy source (1.3325 and 1.1732 MeV) is in the range of 0.24 keV/µm (Vral et al., 1994). Neutrons cause the emission of recoil protons, alpha particles and heavy nuclear fragments during scattering events. These emitted charged particles interact more readily with the absorbing medium and cause densely spaced ionizing events along its track. The p66(Be) neutron beam used in this study has an ionization density of 20 keV/µm and hence regarded as high-LET radiation. Auger electrons travel very short distances in the absorbing medium due to their low kinetic energies. All the energy of these particles is liberated in small volumes over short track lengths. Ionization densities are therefore very high, up to 40 keV/µm this is comparable to high-LET alpha particles (Godu et al., 1994). Relative Biological Effectiveness (RBE) The degree of damage caused by ionizing radiation depends firstly on the absorbed dose and secondly on the ionization density or quality of radiation. Variances in the biological effects of different radiation qualities can be described in terms of the relative biological effectiveness (RBE). RBE defines the magnitude of biological response for a certain radiation quality compared to a distinct reference radiation. It is expressed in terms of the ratio (Quoc, 2009): Megavoltage X-rays or 60Co γ-rays are commonly employed as the reference radiation since these are standard therapeutic sources of radiation. Thus for an identical dose neutrons the biological effect observed would be greater, compared to 60Co γ-rays. The fundamental difference between these radiation modalities is in the spatial orientation or micro deposition of energy. Furthermore, RBE varies as a function of the dose applied – increase in RBE is noted for a decrease in dose. By evaluating dose response curves (Fig 2), it is evident that the shoulder of the neutron curve is much shallower (smaller β-value) compared to the reference radiation curve. Therefore changes in RBE are prominent over low dose ranges (Hall, 2005). Fig 2: Dose response curves based on the linear quadratic model demonstrate differences in RBE as a function of dose. Through evaluation of the biological effect curves it is apparent that the RBE for a specific radiation quality may vary. This is characterized by the type of tissue or cells being investigated, dose and dose rates applied oxygenation status of the tissue, energy of radiation and the phase of the cell cycle and inherent radiosensitivity of cells. The RBE increases with a decrease in dose, to reach a maximum RBE denoted RBEM this is calculated from the ratio of the initial slope of the dose response curves for both radiation modalities. RBE LET relationship For a given absorbed dose, differences in the biological response for several cell lines, exposed to different radiation qualities have been demonstrated (Slabbert et al., 1996). Cells exposed to a specified dose low LET radiation do not exhibit the same biological endpoint than those exposed to same dose high LET radiation. This since with low LET radiation a substantial amount of damage may be repaired because the energy density imparted to each ionization site is relatively low. The predominant mode of interaction for this radiation type is indirect through chemical attack from radiolysis of water. As the LET increases, for a specific dose, fewer sites are damaged but the sites that are located along the track of the ionizing particle are severely damaged because more energy is imparted. Thus the probability of direct interaction between the particle track and the target molecule increases with an increase in LET. The RBE of radiation can be correlated with the estimates of LET values. However, as the LET increases, exceeding 10keV/µm it is no longer possible to assign a single value for the RBE. Beyond this LET, the shape of the cell survival curve changes markedly in the shoulder region compared to low-LET. Since RBE is a measure of the biological effect produced, comparison of the low-LET and high-LET curves will reveal that RBE increases with decreasing dose (Hall, 2005). The average separation in ionizing events at LET of about 100 keV/μm is equal to the width of deoxyribonucleic acid (DNA) double strand molecule (Fig. 3). Further increase in LET results in decreased RBE since ionization events occur at smaller intervals than DNA molecule strand separation (Fig. 3) and this energy imparted does not contribute to DNA damage. Fig 3: Average spatial distribution of ionizing events for different LET values in relation to the DNA double helix structure (Hall 2005). Cellular Radiosensitivity Tissue radiosensitivity models In 1906 the radiobiologists Bergonie and Tribondeau established a rule for tissue radiosensitivity. They studied the relative radiosensitivities of cells and from this could predict which type of cells would be more radiosensitive (Hall, 2005). Bergonie and Tribondeau realized that cells were most sensitive to radiation when they are: Rapidly dividing (high mitotic activity). Cells with a long dividing future. Cells of an unspecialised type. The “law” of Bergonie and Tribondeau was later adapted by Ancel and Vitemberger; they concluded that radiation damage is dependent on two factors: the biological stress on the cell. the conditions to which the cell is exposed pre and post irradiation. Cell division causes biological stress thus cells with a short doubling time express radiation damage at an earlier stage than slowly dividing cells. Undifferentiated rapidly dividing cells therefore are most radiosensitive (Hall, 2005). A comprehensive system of classification was proposed by Rubin and Casarett, cell populations were grouped into 4 categories based on the reproduction kinetics: Vegetative intermitotic cells were defined as rapidly dividing undifferentiated cells. These cells usually have a short life cycle. For example: erythroblasts and intestinal crypt cells and are very radiosensitive. Differentiating intermitotic cells are characterized as actively dividing cells with some level of differentiation. Examples include: meylocytes and midlevel cells in maturing cell lines these cells are radiosensitive. Reverting postmitotic cells are regarded as to not divide regularly and generally long lived. Liver cells is an example of this cell type, these cell types exhibit a degree radioresistance. Fixed postmitotic cells do not divide. Cells beloning to this classification are regarded to be highly differentiated and highly specialized in both morphology and function. These cells are replaced by differentiating cells in the cell maturation lines and are regarded as the most radioresistant cell types. Nerve and muscle cells are prime examples (Hall, 2005). Michalowski proposed a type of classification which divides tissues into hierarchical (H-type) and flexible (F-type) populations. Within this classification cells are grouped in 3 distinct categories: Stem cells, that continuously divide and reproduce to give rise to both new stem cells and cells that eventually give rise to mature functional cells. Maturing cells arising from stem cells and through progressive division eventually differentiate into an end-stage mature functional cell. Mature adult functional cells that do not divide Examples of H-type populations include the bone marrow, intestinal epithelium and epidermis; these cells are capable of unlimited proliferation. In F-type populations the adult cells can under certain circumstance be induced to undergo division and reproduce another adult cell. Examples include; liver parenchymal cells and thyroid cells. The two types represent the extremes in cell populations. It should be noted that most tissue populations exist between the extremes, these exhibit characteristics of both types where mature cells are able to divide a limited number of times. The sensitivity to radiation can be attributed to the length of the life cycle and the reproductive potential of the critical cell line within that tissue (Hall, 2005). Cell cycle dependent radiosensitivity As cells progress through the cell cycle various physical and biochemical changes occur (Fig. 4). These changes influence the response of cells to ionizing radiation. Variations in radiosensitivity for several cell types at different stages of the cell cycle has been documented (Hall, 2005). Following the law of Bergonie and Tribondeau that cells with high mitotic activity are most radiosensitive, it was found that cells in the mitotic phase (M-phase) of the cell cycle are most sensitive. Late stage gap 2 (G2) phase cells are also very sensitive with gap 1 (G1) phase being more radioresistant and synthesis (S phase) cells the most resistant (Domon, 1980). Fig. 4: Cell cycle of proliferating cells representing the different phases leading up to cell division. The G0 resting phase for cells that do not actively proliferate has been included since T-lymphocytes naturally occur in this phase (Hall, 2005). Nonproliferating cells, generally cells that are fully differentiated, may enter the rest phase G0 from G1 and remain inactive for long periods of time. Peripheral T-lymphocytes seldom replicate naturally and remain in G0 indefinately. Lymphocyte Radiosensitivity The hematopoietic system is very sensitive to radiation. Differential blood analyses are routinely employed as a measure of radiation exposure. This measurement is based on the sensitivity of stem cells and the changes observed in the constituents of peripheral blood due to variations in transit time from stem cell to functioning cell (Hall, 2005). It has been shown that lymphocytes, although they are resting cells (G0 phase) which do not actively proliferate nor do have a long dividing future hence do not meet the criteria of a radiosensitive cell type as described above are of the most radio sensitive cells. The reasons for their acute sensitivity cannot be explained (Hall, 2005). Furthermore two distinct subpopulations T-lymphocytes with respect to radiosensitivity were found in peripheral blood. The small T-lymphocyte which is extremely radiosensitive and disappears almost completely from the peripheral blood at doses of 500 mGy (Kataoka, 1974, Knox, 1982 and Hall, 2005). Cytogenetic expression of ionizing radiation induced damage The primary target in radiotherapy is the double helix deoxyribonucleic acid (DNA) molecule (Rothkam et al. 2009). This macro molecule contains the genetic code critical to the development and functioning of most living organisms. The DNA molecule consists of two strands held together by hydrogen bonds between the bases. Each strand is made up of four types of nucleotides. A nucleotide consists of a five-carbon sugar (deoxyribose), a phosphate group and a nitrogen containing base. The nitrogen containing bases are adenine, guanine, thymine or cytosine. Base pairing between two nucleotide strands is universally constant with adenine pairing with thymine and guanine with cytosine (Fig. 5). This attribute permits effective single strand break repair since the opposite strand is used as a template during the repair process. The base sequence within a nucleotide strand differs; the arrangement of bases defines the genetic code. The double helix DNA molecule is wound up on histones and bound together by proteins to form nucleosomes. This structure is folded and coiled repeatedly to become a chromosome. Fig. 5: The double helix structure of a DNA molecule consists of two neucleotide strands held together by hydrogen bonds between the bases. Figure modified from http://evolution.berkeley.edu/evolibrary/article/history_22 by P Beukes. Ionizing radiation can either interact directly or indirectly with the DNA strand. When an ionization event occurs in close proximity to the DNA molecule direct ionization can denature the strand. Ionization events that occur within the medium surrounding the DNA produce free radicals such as hydrogen peroxide through radiolysis of water. Damage induced by ionizing radiation to the DNA include base damage (BD), single strand breaks (SSB), abasic sites (AS), DNA-protein cross-links (DPC), and double strand breaks (DSB) (Fig. 6). Fig. 6: Examples of several radiation induced DNA lesions. Figure modified from Best B (9) by P Beukes. Low-LET radiation primarily causes numerous single strand breaks, through direct and indirect interaction (Hall, 2005). Single strand breaks are of lesser biological importance since these are readily repaired by using the opposite strand as a template. High-LET radiation damage is dominated by direct interactions with the DNA molecule. Densely ionizing radiation has a greater probability to induce irreparable or lethal double strand breaks since energy deposition occurs in discrete tracks (Hall, 2005). The number of tracks will be fewer but more densely packed compared to low-LET radiation of equivalent doses. Several techniques to quantify chromosomal damage and chromatid breaks have been established. These range from isolating DNA and passing it through a porous substrate or gel (Hall, 2005) by applying an external potential difference too advanced techniques of visually observing and numerating chromosomal aberrations of interphase cells. Cytogenetic chromosome aberration assays of peripheral blood T-lymphocytes to assess radiation damage include but are not limited to: premature chromosome condensation (PCC) assay, metaphase spread dicentric and ring chromosome aberration assay (DCA), metaphase spread fluorescence in situ hybridisation (FISH) translocation assay and cytokinesis blocked micronuclei (CBMN) assay (Fig. 7). Fig. 7: Different cytogenetic assays on peripheral T-lymphocytes for use in biological dosimetry. Figure modified from Cytogenetic Dosimetry IAEA, 2011. PCC occurs when an interphase cell is fused with a mitotic cell. The fusion causes the interphase cell to produce condensed chromosomes prematurely. Chromosomal aberrations can thus be analysed immediately following irradiation without the need for mitogen stimulation or cell culturing. Numeration of dicentrics in metaphase spreads has been used with great success to assess radiation damage in cells since the 1960’s (Vral et al, 2010). The incidence of these aberrations follows a linear quadratic function with respect to the dose. Unstable aberrations like dicentrics or centric rings are lethal to the cell hence not passed on to daughter cells (Hall, 2005). In contrast translocations are stable aberrations; these are not lethal to the cell and passed on to daughter cells. Examination of translocations thus provides a long term history of exposure. Although the abovementioned techniques are very accurate and well described, the complexity and time consuming nature of the assays has stimulated the development of automated methods of measuring chromosomal damage. Micronuclei (MN) formation in peripheral blood T-lymphocytes lends itself to automation, since the outcome of radiation insult is visually not too complex with limited variables. DNA damage incurred from ionizing radiation or chemical clastogens induce the formation of acentric chromosome fragments and to a small extent malsegregation of whole chromosomes. Acentric chromosome fragments and whole chromosomes that are unable to engage with the mitotic spindle lag behind at anaphase (Cytogenetic Dosimetry IAEA, 2011). Micronuclei originate from these acentric chromosome fragments or whole chromosomes which are excluded from the main nuclei during the metaphase/anaphase transition of mitosis. The lagging chromosome fragment or whole chromosome forms a small separate nucleus visible in the cytoplasm of the cell. Image recognition software can thus be employed to quantify radiation damage by applying classifiers that describe cell size, staining intensity, cell separation, aspect ratio and cell characteristics when numerating MN frequency in BN cells. The classifiers are fully customizable depending on cell size, staining technique or cell type that will be used. Rationale for this study The principal objective of this study is to define RBE variations for high-LET radiation with respect to radiosensitivity. Specifically this is done for very high energy neutrons and Auger electrons. In general the response of different cell types vary much more to treatment with low-LET radiation compared to high-LET radiation (Broerse et al. 1978). Radiosensitivity differences have been demonstrated for different cancer cell lines (Slabbert et al. 1996) as well as various clonogenic mammalian cells (Hall, 2005) exposed to both high and low-LET radiation. In general there is an expectation and in certain cases some experimental evidence to support less variations in radiosensitivities of cells to high-LET radiation. Furthermore the ranking in the relative radiosensitivity of cell types changed for neutron treatments compared to exposure to X-rays (Broerse et al. 1978). To quantify the radiation risk of individuals exposed to cosmic rays or mixed radiation fields of neutrons and γ-rays, several experiments were conducted to ascertain biological damage induced by neutron beams of various energies (Nolte et al., 2007). Clonogenic survival data (Hall, 2005), dicentric chromosome aberrations (Heimers 1994) and micronuclei formation (Slabbert et. al 2010) have been followed. Chromosome aberration frequencies have been quantified and this represent radiation risk to neutron energies ranging from 36 keV up to 14.6 MeV (Schmid et al. 2003). To complement these studies additional measurements have been made for blood cells exposed to 60 MeV and 192 MeV quasi monoenergetic neutron beams (Nolte et al. 2007). Comparisons of RBE values obtained in these studies are shown in figure 1. Significant changes in the maximum relative biological effectiveness (RBEM) of these neutron sources are demonstrated as a function of neutron energy, with a maximum value of 90 at 0.4 MeV. RBEM drop to ±15 for neutron energies higher than 10 MeV and it appears that the RBEM remain constant up to 200 MeV. The RBEM value of 47 -113 reported by Heimers et al. (1999) is not consistent with these observations. Fig. 1: RBEM values for neutrons of different energies after Nolte et al. (2007) The data shown in Fig. 1 was obtained by using the blood of a single donor. This to ensure consistency in the biological response for different neutron energies used in different radiation facilities in different parts of the world. Keeping the donor constant has the advantage that only a single data set for the reference radiation was needed. These measurements were done over several years. In all these studies, dicentric chromosome aberrations were followed. As informative as these investigations may be, it is doubtful if RBE values obtained from blood samples from a single donor are indeed representative for the wider population to state radiation weighting factors. It is unclear if RBE values for high energy neutrons will vary when measured with cells with different inherent radiosensitivities. Warenius et al. (1994) demonstrated that the RBE of a 62.5 MeV neutron beam increases with increase in radioresistance to 6 MV X-rays. Similarly Slabbert et al (1996) using a 29 MeV p(66)/Be neutron with an average energy of 29 MeV, noted a statistically significant increase in the RBE values for cell types with increased radioresistance to 60Co γ-rays. Although these investigators used 11 different cell types, few of these were indeed radioresistant to 60Co γ-rays. Close inspection of the data shows that the relationship between neutron RBE and radioresistance to photons disappear when the cell type with the highest resistance to γ-rays (Gurney melanoma) is removed from the data set Slabbert et al. 1996). In a follow up study the authors failed to demonstrate the relationship for a p(66)/Be neutron beam but such a relationship was demonstrated for a d14/Be neutron beam (Slabbert et al. 2000). It therefore appears that the relationship for RBE and radioresistance is dependent on the selection of cells used in the study as well as the neutron energy. Using lymphocytes Vral et al. (1994) demonstrated a clear reduction RBEM values for 5,5 MeV neutrons with an increase in the α-values of dose effect curves obtained for 60Co γ-rays. This for lymphocytes obtained from six healthy donors. Using only four donors Slabbert et al. (2010) also demonstrated a relationship between RBEM neutrons and radiosensitivity to 60Co y rays. In the latter case the RBEM values are lower – as can be expected since these investigators used a higher energy neutron source. Although a significant relationship between these parameters has been demonstrated by the investigators, the cohort of 4 donors in the study is very small. In fact 2 out of the 4 donors have different RBEM values but appear to have the same radiosensitivity. A study using larger number of donors with blood cells exposed to high energy neutrons is clearly needed. This in particular too verify the findings above indicating a different wR for donors of different sensitivity. The studies of RBE variations with neutron energy by Schmid et al., (2003), Nolte et al. (2005) and were conducted dicentric formations observed in metaphase spreads. It is known that more than six months were used to analyse the data for different doses for blood cells obtained from a single donor exposed to a single neutron energy. It follows that some method of automation to assist the radiobiological evaluation of cellular radiation damage is needed to quantify wR values as a function of radiosensitivity. Recently a semi-automated image analysis system, Metafer 4, this holds promise to test numerous donors for micronuclei formations Study to include more participants hence Metafer…. The Biological Effects Of Ionizing Radiation Biology Essay

Comparison of Join algorithms in MapReduce Framework

python assignment help Mani Bhushan, Balaraj J, Oinam Martina Devi Abstract: In the current technological world, there is generation of enormous data each and every day by different media and social networks. The MapReduce framework is increasingly being used widely to analyse large volumes of data. One of the techniques that framework is join algorithm. Join algorithms can be divided into two groups: Reduce-side join and Map-side join. The aim of our work is to compare existing join algorithms which are used by the MapReduce framework. We have compared Reducer-side merge join and Map-side replication-join in terms of pre-processing, the number of phases involved, whether it is sensitive to data skew, whether there is need for distributed Cache, memory overflow. The objective is to determine which algorithm holds well in given scenario. I INTRODUCTION Data-intensive applications include large-scale data warehouse systems, cloud computing, data-intensive analysis. Applications for large-scale data analysis use MapReduce (MR) paradigm [6]. MAPREDUCE is a programming model for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs and a reduce function that merges all intermediate values associated with the same intermediate key [5]. Let us look upon the execution of MapReduce execution. MapReduce Execution: The Map/Reduce framework consists of two operations, “map” and “reduce”, which are executed on a cluster of shared-nothing commodity nodes. In a map operation, the input data available through a distributed file system, is distributed among a number of nodes in the cluster in the form of key-value pairs. Each of these mapper nodes transforms a key-value pair into a list of intermediate key-value pairs [1]. The intermediate key-value pairs are propagated to the reducer nodes such that each reduce process receives values related to one key. The values are processed and the result is written to the file system [1]. Figure 1.1: MR execution in detail [7]. In [3], the authors have described crucial implementation details of a number of well-known join strategies in MapReduce, and present a comprehensive experimental comparison of these join techniques on a 100-node Hadoop cluster. The authors have provided the overview of MapReduce overall. They have described how to implement several equijoin algorithms for log processing in MapReduce. They have used the MapReduce framework as it is, without any modification. Therefore, the support for fault tolerance and load balancing in MapReduce is preserved. They have worked on Repartition Join, Broadcast Join, Semi-Join, and Per-Split Semi-Join. The authors have revealed many details that make the implementation more efficient. We have evaluated the join methods on a 100-node system and shown the unique tradeoffs of these join algorithms in the context of MapReduce. We have also explored how our join algorithms can benefit from certain types of practical preprocessing techniques. In [4], the authors have examined the algorithms for performing equi-joins between datasets over Map/Reduce and have provided a comparative analysis. The results indicate that all join algorithms are significantly affected by certain properties of the input datasets (size, selectivity factor, etc.) and that each algorithm performs better under certain circumstances. Our cost model manages to capture these factors and estimates fairly accurately the performance of each algorithm. II COMPARISON OF ALGORITHMS Data-intensive applications required to process multiple data sets. This implies the need to perform several join operation. Its known join operation is one of the most expensive operations in terms both I / O and CPU costs [6]. Now let us see two of the join algorithms analysed in the earlier work: 2.1 Reducer-side merge join: It is the most straightforward way to join two datasets over the Hadoop framework. It can be considered as the Hadoop version of the parallel sort-merge join algorithm. The main idea is to sort the input splits on the join column, forward them to the appropriate reducer and then merge them during the reduce phase. The performance of the algorithm is dominated by two main factors. The first is the communication overhead required to shuffle the datasets through the network from mapper to reducer. The second one is the time required to sort and write the datasets to disk before forwarding them to the reducers. However, the drawback of the the Reduce-side merge join is that the map function does not apply any filter and the output size remains at the same size with the input and also the reducer loads in memory all the tuples of each split. Figure 1.2 Reducer-side merge join [4] 2.2 Map-side replication-join The Map-Side Replication join tries to address the drawbacks of the previous approach. The concept was initially conceived in the database literature [2]. The implementation is much simpler compared to the previous algorithm. We start by replicating the small table to all nodes by using the distributed cache facility. Then, during the setup2 of the mapper we load the table into a hash table. For each value of the hash table we nest an array list for storing multiple rows with the same join attribute. Hence, for each row of the bigger table we search over only the unique keys of the small table. In the case we have many rows per join attribute it results in substantial performance gain. The hash table provides constant time search for a key value. During the execution of the mapper for each key-value pair of the input split we extract the join attribute and probe the hash table. If the value exists we combine the tuples of the matching keys and submit the new tuple. The algorithm is illustrated in figure 1.3. The main disadvantage of this algorithm is that it is restricted by the memory size of the nodes. If the small table does not fit in memory we cannot use the algorithm at all. Figure 2.2 Map-side replication-join. III CONCLUSION IV REFERENCES [1] Fariha Atta. Implementation and analysis of join algorithms to handle skew for the hadoop mapreduce framework. Master’s thesis, MSc Informatics, School of Informatics, University of Edinburgh, 2010. [2] Shivnath Babu. Towards automatic optimization of mapreduce programs. In Proceedings of the 1st ACM symposium on Cloud computing, SoCC ’10, pages 137–142, New York, NY, USA, 2010. ACM. [3] Spyros Blanas, Jignesh M. Patel, Vuk Ercegovac, Jun Rao, Eugene J. Shekita, and Yuanyuan Tian. A comparison of join algorithms for log processing in mapreduce. In Proceedings of the 2010 international conference on Management of data, SIGMOD ’10, pages 975–986, New York, NY, USA, 2010. ACM. [4] A Chatzistergiou. Designing a parallel query engine over map/reduce. Master’s thesis, MSc Informatics, School of Informatics, University of Edinburgh, 2010. [5] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: a flexible data processing tool. Commun. ACM, 53:72–77, January 2010. [6] A. Pigul. Comparative Study Parallel Join Algorithms for MapReduce environment. Saint Petersburg State University. [7] S. Blanas, J. M. Patel, V. Ercegovac, J. Rao, E. J. Shekita, and Y. Tian. A comparison of join algorithms for log processing in mapreduce. In SIGMOD ’10: Proceedings of the 2010 international conference on Management of data, pages 975–986, New York, NY, USA, 2010. ACM. [8] Shivnath Babu. Towards automatic optimization of MapReduce programs. In SIGMOD ’10: Proceedings of the 2010 international conference on Management of data. Pages 137-142. New York, NY, USA, 2010. ACM.

Respond in essay form to prompt #3 on pg. 461 (In attachment labeled ‘Prompt’). Essay should be at least

Respond in essay form to prompt #3 on pg. 461 (In attachment labeled ‘Prompt’). Essay should be at least five pages in length and MLA formatted. Use Miller’s ‘Dark Night of The Soul’ and/or Wideman’s ‘Our Time’ five times to back up analysis. Attached is the prompt. Also attached is both readings: Miller’s ‘Dark Night of the Soul’ and Wideman’s ‘Our Time’ Lastly, I attached my teachers example for this prompt.

Biology homework help

Biology homework help. This assignment consists of two parts. 25% of the grade will come from a column graph showing the average number of days in a chrysalis for each temperature. The graph does not have to be computer-generated, but should be neat, accurate, and properly labelled.,25% of the grade will come from a column graph showing,This assignment consists of two parts., 1. 25% of the grade will come from a column graph showing ,the average number of days in a chrysalis for each temperature,. The graph does not have to be computer-generated, but should be neat, accurate, and properly labelled. It will need a descriptive title. Also labels for the X and Y axes. Finally, the graph should accurately reflect the data. Don’t overcomplicate the graph. Although the table shows detailed data, the only data you need for the graph are the three temperatures and three averages!, Below is an example of a simple column (or bar) graph. This particular graph shows the number of students preferring four different juices; you will be plotting the average number of days in the chrysalis for each temperature: cool, room, and warm. For your graph, you will have three “bars” or “columns.”, ,Question two,2. 75% of the grade will come from a paper of between 1000 and 1500 words, a minimum of 1000. The first part of the paper will focus on an analysis of the data to draw an informed conclusion about the experiment. This section will include ideas about how and why different temperatures affect the speed of metamorphosis. It should also include information from at least one similar study that validates or invalidates your conclusion and it should be cited at the end of the paper.,The second part of the paper will focus on the effects of a changing climate on, not only insects, but also the communities and ecosystems they are part of.,The paper should conclude with your ideas about the interconnectedness of all species in a community. Also, communities in an ecosystem, etc. This section should use two references. You may use one from the papers listed below. You may also use your textbook as a reference.,Part of this assignment (valued at 5 points) is to visit the Stone Writing Center,http://dmc122011.delmar.edu/swc/ for help with your paper (either in person in the White Library or online) at some stage of writing your paper. I should get confirmation of your visit directly from the SWC, but you may want to pick up a confirmation from them in the form of an email or a SWC Tutorial Verification,Attachments,Click Here To Download,Biology homework help