Investigation of Portfolio Choice that Tracks a Continuously Moving Target

We investigate the problem of tracking a moving target, specifically a given continuously compounded, deterministic growth rate by using linear quadratic control theory. We first present some preliminaries and then derive and solve the corresponding Riccati equations for our problem. We then implement our findings in MATLAB by using one stock from the FTSE100 and a risk-less asset and analyze the results. Furthermore, we focus on situations when the performance of our tracking is not the expected and highlight when this can happen and what the risks involved are. We also present situations when we would need to borrow or short sell, like in the case of aggressively tracking a target. We then briefly present the extension to multi asset portfolios. Finally, we discuss the case of tracking a market index

Colorectal cancer screening awareness among physicians in Greece

BACKGROUND: Data comparison between SEER and EUROCARE database provided evidence that colorectal cancer survival in USA is higher than in European countries. Since adjustment for stage at diagnosis markedly reduces the survival differences, a screening bias was hypothesized. Considering the important role of primary care in screening activities, the purpose of the study was to investigate the colorectal cancer screening awareness among Hellenic physicians. METHODS: 211 primary care physicians were surveyed by mean of a self-reported prescription-habits questionnaire. Both physicians' colorectal cancer screening behaviors and colorectal cancer screening recommendations during usual check-up visits were analyzed. RESULTS: Only 50% of physicians were found to recommend screening for colorectal cancer during usual check-up visits, and only 25% prescribed cost-effective procedures. The percentage of physicians recommending stool occult blood test and sigmoidoscopy was 24% and 4% respectively. Only 48% and 23% of physicians recognized a cancer screening value for stool occult blood test and sigmoidoscopy. Colorectal screening recommendations were statistically lower among physicians aged 30 or less (p = 0.012). No differences were found when gender, level and type of specialization were analyzed, even though specialists in general practice showed a trend for better prescription (p = 0.054). CONCLUSION: Contemporary recommendations for colorectal cancer screening are not followed by implementation in primary care setting. Education on presymptomatic control and screening practice monitoring are required if primary care is to make a major impact on colorectal cancer mortality

Can Non-lytic CD8+T Cells Drive HIV-1 Escape?

The CD8+ T cell effector mechanisms that mediate control of HIV-1 and SIV infections remain poorly understood. Recent work suggests that the mechanism may be primarily non-lytic. This is in apparent conflict with the observation that SIV and HIV-1 variants that escape CD8+ T cell surveillance are frequently selected. Whilst it is clear that a variant that has escaped a lytic response can have a fitness advantage compared to the wild-type, it is less obvious that this holds in the face of non-lytic control where both wild-type and variant infected cells would be affected by soluble factors. In particular, the high motility of T cells in lymphoid tissue would be expected to rapidly destroy local effects making selection of escape variants by non-lytic responses unlikely. The observation of frequent HIV-1 and SIV escape poses a number of questions. Most importantly, is the consistent observation of viral escape proof that HIV-1- and SIV-specific CD8+ T cells lyse infected cells or can this also be the result of non-lytic control? Additionally, the rate at which a variant strain escapes a lytic CD8+ T cell response is related to the strength of the response. Is the same relationship true for a non-lytic response? Finally, the potential anti-viral control mediated by non-lytic mechanisms compared to lytic mechanisms is unknown. These questions cannot be addressed with current experimental techniques nor with the standard mathematical models. Instead we have developed a 3D cellular automaton model of HIV-1 which captures spatial and temporal dynamics. The model reproduces in vivo HIV-1 dynamics at the cellular and population level. Using this model we demonstrate that non-lytic effector mechanisms can select for escape variants but that outgrowth of the variant is slower and less frequent than from a lytic response so that non-lytic responses can potentially offer more durable control

Lung cancer invading the superior vena cava – surgical treatment. A short and up-to-date review

Lung cancer is one of the leading causes of cancer-related deaths worldwide. Superior vena cava syndrome (SVCS) is a rare but potentially life-threatening complication of lung cancer, occurring in approximately 5–10% of cases. There are difficulties in the process of surgical treatment of SVC infiltrated by lung tumors but the contribution of technological evolution and innovation is promising. At the same time, the amelioration of survival rates of patients subjected to surgical treatment is equally promising. The reported outcomes of surgical treatment for SVC invasion due to lung tumors vary depending on the extent of the tumor and the patient’s overall health status. However, studies clearly suggest that surgical treatment can improve survival and quality of life in selected patients. The literature review showed that the surgical approach to lung cancer invading the SVC constitutes the most indispensable treatment which helps to achieve the long-term survival of patients

Infected cells in the neighbourhood of CD8+ T cells secreting non-lytic factors.

<p>The mean fraction of wild type-infected cells neighbouring secreting CD8+ T cells was evaluated over time. We tracked the proportion of wild type- and variant-infected cells in the immediate neighbourhood (radius = 1, i.e. 26 neighbours) of 5,425 secreting CD8+ T cells. In these runs, CD8+ T cells secreted non-lytic factors for 30 mins after triggering and were tracked for 34 minutes. The proportion of wild type-infected cells was high (i.e. the majority of affected cells were wild-type infected, conferring an advantage upon variant-infected cells), but not 100% (i.e. some variant-infected were also affected by the factor) and the proportion of wild type-infected cells decreased significantly over time (i.e. the spatial heterogeneity that conferred an advantage upon escape variants was short lived). The correlation between the proportion of wild type-infected cells and time during the 30 mins post CD8+ T cell-triggering was significant, Spearman correlation coefficient = −0.93, p<10⁻⁶.</p

The cellular automaton model accurately reproduces T cell and viral dynamics at the cellular and population level.

<p>(a) A snapshot of the 3D cellular automaton model. The different coloured nodes represent the different cell populations. (b) The speed of four individual simulated CD8+ T cells in the CA model as a function of time. The mean speed is 9 µm/min and the mean motility coefficient is 75 µm²/min. (c) Dynamics of uninfected and infected CD4+ T cells over a course of 150 days. The bars represent the 95% central range values.</p

Existence of clusters of wild type and variant-infected cells.

<p>The percentage of wild type-infected cells neighbouring a wild type-infected cell is significantly higher than the percentage of variant-infected cells. The same is true for variant-infected cells. We track simulated wild type and variant-infected CD4+ T cells (≈500 cells) for multiple timepoints. Abbreviations: WT/WT = Wild type infected cells in the immediate area (r = 1, i.e. 26 nodes considered) surrounding a wild type-infected cell, WT/VAR = Wild type infected cells in the immediate area surrounding a variant-infected cell, WT/ALL = Wild type-infected cells on the whole grid. VAR/VAR, VAR/WT and VAR/ALL are defined similarly.</p

The percentage of simulations that result in variant fixation.

<p>Abbreviations: Pr = Probability of recognition by CD8+ T cells, TO = Target Only, P1 = Polarised secretion (r = 1), D1 = Diffusive secretion (r = 1) and D2 = Diffusive secretion (r = 2).</p

The immune control exerted by lytic and non-lytic CD8+ T cell responses.

<p>(<b>A</b>) The frequency of productively infected cells at setpoint for varying probability of CD8+ T cell recognition and for different secretion patterns. As the probability of recognition increases, the frequency of infected cells decreases for the lytic response but remains approximately constant for the non-lytic response for all secretion patterns. When Pr = 0.001 (first box) the frequency of infected cells is similar for the lytic and non-lytic responses (<b>B</b>) Number of uninfected CD4+ T cells protected from infection by a non-lytic CD8+ T cell response. The number of cells protected increases significantly as the probability of recognition and the area of diffusion increases (cumulative number by 50 dpi). (<b>C</b>) Set-point of productively infected cells when varying the duration of the protective effect of the soluble factor. Number of infected cells at setpoint does not decrease when the duration of effect is increased even though the number of cells protected robustly increases (<b>D</b>) Set-point of productively infected cells for different sizes of the epitope-specific CD8+ T cell clones. Here we show the results for the non-lytic control that blocks viral infection, similar results were found for non-lytic control that blocks viral production (Figures S6, S7 in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003656#ppat.1003656.s001" target="_blank">Text S1</a> and data not shown). <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003656#s2" target="_blank">Results</a> for more extensive parameter combinations are shown in supplementary information (Figures S10 and S11 in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003656#ppat.1003656.s001" target="_blank">Text S1</a>). The percentage of productively infected cells is calculated from 40–50 dpi. Abbreviations: NLi: Non-lytic model - blocking infection of uninfected CD4+ T cells, P1 = Polarised secretion (r = 1), D1 = Diffusive secretion (r = 1) and D2 = Diffusive secretion (r = 2).</p