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A NOTE ON CASUAL CHANGE IN WORK RELATIONSHIPS

S. T. ANONYMOUS

It has long been established that functional and emotional changes occur as a result of sexual activity (DRG , Ca. 200 A.D.). Behavioral alteration has been observed in dogs (Dog Whisperer, 2005), cats (Old Cat Lady a.k.a. TJ, 2006), and people. Freud (ca. 1895) and Ruth (ca. 1980) established base-line data on communication, unusual interrelationships, and a correlation between thinking of sex and blurting out words mid-sentence, thus creating awkward situations in public. Chemical tracers and ultraviolet light have been used to track these changes within offices, but until now no mathematical predictor has been established.

DISCUSSION

A Model of Change

A statistical model for the Index of Change in work related behavior due to sexual involvement is presented here. Awkwardness at work is calculated as follows:


Caw = [ Σ Qi-r * (Tw – 1) ] * 100
Σ Qi-p * Σ Ns




where

Caw = Percentage of change in awkwardness at work; Index of Change
Qi-r = Quality of experience remembered 24 hours following interval
Tw = Time spent together at work in hours
Qi-p = Perceived quality of experience at time of interval
Ns = Number of intervals




and where variance is determined as:

V(Caw) = ± [ Tw + Σ Qi-p ] * 100
(Ns2-1) * Σ Qi-r




These formulae were derived theoretically (Appendix A) and have yet to be confirmed by empirical data. I feel strongly, however, that change in awkwardness at work can be correctly calculated to within two standard deviations of the mean. Quality of sex rating is between 1 and 10. A rating of 1 or 10 should not be considered outliers even if they differ greatly from the mean as participants often experience “good” and or “bad” days. When Ns = 1, index of change exceeds 100%. Furthermore, the model assumes that Qi-p > Qi-r, due to the stochastic nature of guilt and hormonally induced euphoria, and Tw > 1. When the number of intervals increases beyond Ns = 1 to 3, percentage change in awkwardness decreases. Variability also decreases as Ns increases.

These phenomena can largely be explained by the complacency coefficient (k; Brown vs. Brown, 2005). Manifested by situational newness, it wears quickly and is thus inversely related with Ns. It is predicted as:

Qi-r = Qmax e-kt

Although this equation is only a predictor of the coefficient, theoretical data (Appendix A) reveals a strong exponential relationship (Fig 1.) between Ns and Caw that closely mimics the above equation. Although Caw exceeds 100% with low values of Ns, it is indicative of major change that can potentially occur at the onset of such a relationship.

Rationale

Maturity, mastication, and infrequent masturbation have been linked as possible sources of instigation for sexual activity leading to work place awkwardness. However, chemical alteration through consumption of copious amounts of alcohol is also attributed to immediate gratification (Qmax) and short term happiness (k < 0.5).

The Index of Change model considers short- to mid-term variability but does not account for rapid situational perturbation. Changes suggested by DHIM (Ca. 250 A.D.) in the Kama Sutra, interludes behind closed doors at work, threesomes with a friend, and drunken hot tub nudity skew this model toward 100% change (see also DOP , 1999).

Alternate model

Contrary to accepted beliefs about pre-marital sexual gratification, and derived from the index of change by incorporating the complacency coefficient, the equation:

Cα = Σ Qmax * Tw
Σ Ns




is true when Qmax ≈ 10; where Cα is index of change due to religious belief. Satisfaction (Qmax) is always maximum value as Qi-r = Qi-p.



















FIGURE 1. – Sexual interval-Index of Change relationship as expressed by exponential decay regression analysis. These values were calculated from data in Appendix 1-B.

This is constant for those of belief; satisfaction is either a one (act of conception; weighted as a ten for social purposes even if it was an accident) or ten (God’s will). With quality of experience at Qmax, distinction can be made between the complacency coefficients of people in two-sided religious relationships (kα) and one-sided (kβ) relationships. The difference is noted because kβ often results in exponential growth, rather than decay, of desire for the non-Jesical party. Tests using hypothetical data to further develop the above models into a Grand Unified Model of Sexual Experience resulted in display of “UNDEFINED” on all calculation systems. This was determined to be Devine intervention; further experimentation was terminated for fear of reprisal, hellfire, and damnation.

SUMMARY

The models proposed herein consider quantity and quality of sexual encounters and how they affect intra-office social dynamics. Previous bodies of work have elaborated profoundly on the psychological issues at hand, but failed to create a predictable model. Empirical data is still needed for verification.
Relationships involving religion experience a proportionally increasing index of change with time spent together at work. In order to improve their relationship, they must spend less time together during the day reducing enjoyment provided by random encounters, flirtation, and cleavage (where applicable). Variation in the complacency coefficient due to perceived, not necessarily displayed, religious beliefs exist that create a situation wherein only a two-sided relationship results in maximum sexual gratification with minimal time together at work.















APPENDIX A. – Hypothetical data and calculations for the Index of Change.


(A)
Quality of Experience Interval (Ns)
1 2 3 4 5 6
Qi-p 7 7 8 10 8 9
Qi-r 6 5 6 8 7 8
Qmax 10 10 10 10 10 10







Tw = 3.5 hours
Caw = 34 ± 3.8 %
Cα = 35 %





(B)
Quality of Experience Interval (Ns)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Qi-p 7 7 8 10 8 9 6 5 2 8 7 9 7 8 8
Qi-r 6 5 6 8 7 8 4 3 1 6 6 8 5 5 7
Qmax 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10

Tw = 5.3 hours
Caw = 22 ± 0.6 %
Cα = 53 %

DNA replication is essential for the life of a cell, but when a cell’s DNA becomes damaged DNA replication comes to a halt. Escherichia coli gets around this problem using the SOS response which consists of many different repair mechanisms (i.e. mismatch repair, nucleotide excision, and base excision), but sometimes the damage is too great and the cell must take drastic measures. The cell then activates its last resort; SOS mutagenesis. DNA polymerase V’s function is to bypass lesions in DNA by putting in bases at random opposite the DNA lesion. DNA polymerase V is expressed in response to the signal produced by single stranded DNA caused by DNA damage (1). The ssDNA interacts with RecA thereby
activating RecA, next RecA mediates the cleavage of LexA, which is the repressor of the genes that code for the proteins that are expressed during the SOS response. RecA also mediates the cleavage of UmuD to the mutagenically active UmuD’ species. UmuD’ then forms a heterotrimer with UmuC forming UmuD’2UmuC, also known as DNA polymerase V. The DNA polymerase V genes are one of the last sets of genes to be activated because DNA polymerase V is error prone, typically placing adenines opposite the damaged site. While this may be the organism’s last chance at survival, it also causes mutations and so must be tightly regulated (2).
Regulation of DNA polymerase V occurs at many levels within a cell. The umuDC operon is one of the very last of the LexA regulated genes to be expressed following a DNA damage event. Moreover, because of the error-prone nature of DNA pol V, the activity of the UmuD’2UmuC polypeptides must be tightly regulated. Protein regulation via selective degradation by ensures proper regulation. The UmuD’2UmuC heterotrimer is stable but the components are not, so proteases must be used to break down the components of this error-prone system to prevent excessive mutations. The UmuC protein is regulated by the Lon protease (3), yet little is known about the mechanism of Lon protease recognition. The UmuC protein is a member of the superfamily of DNA polymerases (found in all three kingdoms). The DinB protein, also a member of this superfamily, is a homolog of the UmuC protein, yet DinB lacks the carboxy-terminal 67 amino acid found in UmuC. Interestingly, UmuC is unstable in vivo, while DinB is relatively. It is this difference in protein stability between UmuC and DinB that suggests that the carboxy-terminal 67aa chain of UmuC may contain the site that Lon recognizes. In an attempt to find the specific sequence that the Lon protease interacts with, a series of c-terminal deletions where made to the UmuC protein. Once the region is narrowed down, point mutations will be created to determine the exact recognition area. From there the recognition point could also shed light on other recognition sites for Lon and give insight as to how proteins are regulated in different cells.