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Bernard W. Dempsey, S. In a centralized economy, currency is issued by a central bank at a rate that is supposed to match the growth of the amount of goods that are exchanged so that these goods can be traded with stable prices. The monetary base is controlled by a central bank.

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Spread betting hedging strategies at gm

Trading involves risk which may result in the loss of capital. Spread betting is a popular type of derivative product made available by many brokers around the world that enables you to trade in particular assets without actually purchasing the underlying asset itself.

The first question to answer here is, what exactly is spread betting? Well, spread betting is a derivative product and strategy in which you predict whether the value of an asset will rise or fall. Since it is a derivative, just like when you are trading CFDs, you do not actually own the underlying asset. This non-ownership of the asset itself is not necessarily a bad point since it does provide you with a great deal of flexibility to profit from the price of the asset going up or down.

In spread betting, this is exactly what you will be doing. You make a prediction on the price of an asset going up, or down. If your prediction is correct, then you can profit for each point an asset gains or loses. If your prediction is incorrect then you can also lose a set amount per point. As well as being able to predict and place spread bets on almost any type of asset from forex currency pairs , commodities, indices, cryptocurrencies , and more, you also have the option of going long or short with your spread bet.

Going long in spread betting means that you predict the price of the underlying asset will rise. If that does happen, then you will make a profit. This profit will be determined by how much the asset you have bet on does actually rise before you close the position. Going short in spread betting means that you think the asset will go down in price.

If this does happen, then you will be in profit by an amount that is determined by your bet size. Your bet size in spread betting is the amount which you bet per unit on the underlying asset movement. Each asset price movement is measured in points. So you will set a bet size per point when opening a spread bet. The minimum amount you can set will typically be determined by the broker you are spread betting with, and the asset you are opening the position on.

As mentioned above, spread betting is flexible in that you can also bet on the price of an asset to drop. Using the same example. While there are a variety of different types of spread bet you can choose in terms of expiration, these are a couple of the most popular:. Daily Spread Bets: These are a popular choice for short term spread betting. A daily spread bet will expire at the end of the day which you open it, or you can also close it prior to this time with most brokers.

A rolling daily spread bet will continue to remain open until you close it and incur things such as overnight fees along the way. Futures Spread Bets: This type of spread bet can be perfect for longer-term traders. These are based on an assets value within the futures market. These can have a delivery date several months in the future. The expiration date here will be that of the futures contract, though they can be closed earlier.

A futures spread bet also does not accumulate any overnight holding fees. Just like in other types of trading in derivative markets in particular, leverage provided by your broker can greatly increase your purchasing power. Depending on the broker you may find a leverage of or more available. Using leverage in trading does though does mean that the margin comes in to play. The margin is how much of your own money you need to hold in the account as a deposit to keep your positions open.

If your positions are losing it is possible you may receive a margin call to deposit more funds to your account. This is the balance that farmers must make when deciding on their crops. In the US, corn is planted in April and May, and pollination takes place in July, so these are critical months for determining where corn production stands. In common with all other agricultural products, the climate can play havoc with plans, and if the summer is too hot corn is particularly susceptible to crop losses.

Historically, most corn has been used as food, particularly for feeding cattle, pigs, and poultry, as well as for human consumption. Nowadays corn is increasingly used for industrial purposes, such as ethanol production, which takes about a third of the US crop. Because of the seasonal factors in growing, you can expect corn prices to reduce just before the harvest in autumn, then to increase as distributors demand stock for their deliveries.

The price usually goes down again just after January as interest wanes. In terms of volatility, summer can see a number of rallies and setbacks depending on actual and rumoured weather events. If you want to look into the factors behind the demand for corn, then it can be a good idea to check out the numbers of animals such as cattle that are being raised each year, and that will typically be fed with the crop. The other major factor has been mentioned above — despite the potential clash between corn for foods and energy requirements, corn is still the major crop grown for ethanol production.

The effect of this is not necessarily clear, because if petrol goes up in cost you might assume that ethanol is more worthwhile, but there is a counter effect that people drive less. Looking again at fundamentals, the US Department of Agriculture releases regular reports related to corn supply and corn usage. In January they issue the annual crop summary for the previous year, and this includes remaining stocks at the end of the year.

In March they report how much corn is expected to be planted, and in June this is confirmed or revised by actual planting figures. They also issue weekly progress statements during the growing season that report on the condition of corn. As one of the major commodities, there are no liquidity problems with trading corn, and as long as you keep an eye on the agricultural reports to warn you in advance of price pressures, you have a good chance of profit spread trading corn.

At the time of writing November there is a global glut of corn and wheat in markets which is negative for the commodities in the short term. The main reason for this global glut of grain were high prices which has boosted investments in agriculture and encouraged farmers to increase production as much as possible. There is no shortage of information to be found about trading corn, and as with all agricultural commodities, it is worth keeping in touch with weather forecasts as well as other issues.

The current price quote is This price is in US cents per bushel, with the standard futures contract being bushels. If you think that corn will increase in value, then you may choose to place a buy bet at Assume you made a winning bet, and the price goes up to It is easy to figure out how much you will have won, and you do it like this: —. Sometimes your bet will not be successful, and the price will go in the opposite direction to the one you expected. When this happens, you have to have the discipline to cut your losses quickly and close the bet.

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Historically, most corn has been used as food, particularly for feeding cattle, pigs, and poultry, as well as for human consumption. Nowadays corn is increasingly used for industrial purposes, such as ethanol production, which takes about a third of the US crop. Because of the seasonal factors in growing, you can expect corn prices to reduce just before the harvest in autumn, then to increase as distributors demand stock for their deliveries.

The price usually goes down again just after January as interest wanes. In terms of volatility, summer can see a number of rallies and setbacks depending on actual and rumoured weather events. If you want to look into the factors behind the demand for corn, then it can be a good idea to check out the numbers of animals such as cattle that are being raised each year, and that will typically be fed with the crop. The other major factor has been mentioned above — despite the potential clash between corn for foods and energy requirements, corn is still the major crop grown for ethanol production.

The effect of this is not necessarily clear, because if petrol goes up in cost you might assume that ethanol is more worthwhile, but there is a counter effect that people drive less. Looking again at fundamentals, the US Department of Agriculture releases regular reports related to corn supply and corn usage. In January they issue the annual crop summary for the previous year, and this includes remaining stocks at the end of the year.

In March they report how much corn is expected to be planted, and in June this is confirmed or revised by actual planting figures. They also issue weekly progress statements during the growing season that report on the condition of corn. As one of the major commodities, there are no liquidity problems with trading corn, and as long as you keep an eye on the agricultural reports to warn you in advance of price pressures, you have a good chance of profit spread trading corn.

At the time of writing November there is a global glut of corn and wheat in markets which is negative for the commodities in the short term. The main reason for this global glut of grain were high prices which has boosted investments in agriculture and encouraged farmers to increase production as much as possible. There is no shortage of information to be found about trading corn, and as with all agricultural commodities, it is worth keeping in touch with weather forecasts as well as other issues.

The current price quote is This price is in US cents per bushel, with the standard futures contract being bushels. If you think that corn will increase in value, then you may choose to place a buy bet at Assume you made a winning bet, and the price goes up to It is easy to figure out how much you will have won, and you do it like this: —.

Sometimes your bet will not be successful, and the price will go in the opposite direction to the one you expected. When this happens, you have to have the discipline to cut your losses quickly and close the bet. Say the price dropped to As another example, suppose corn that was listed at This time the bet would go on at the lower price of Bet hedging is generally understood as a strategy whereby an organism decreases variation in fitness at the expense of a small decrease in mean fitness.

To test whether variation in macrophage phagolysosome pH constitutes a diversified bet-hedging strategy, we first modeled host survival rate as a function of final phagolysosomal pH in the context of a pathogen randomly selected from Supplemental Table 1. To each phagolysosomal pH, we associated a fitness value that models survival likelihood against pathogens, using a list of pathogens Supplemental Table 1 with their viable pH ranges collected from the literature, and we computed the distribution of fitness p as phagolysosomal pH varies Figure 5, A and B.

Mean fitness was mostly unchanged with changing standard deviation, such that even in the most extreme deviations in phagolysosomal pH there were only slight changes in fitness in either direction. Simulated macrophage populations show stochastic pH as bet-hedging strategy. Meshes represent host macrophage fitness, deviation in fitness, and log mean fitness.

Plotted points represent measured data of Hcontaining murine red and human magenta or bead-containing murine blue and human cyan phagolysosomes. A Mean macrophage survival z axis, color bar increases significantly as pH lowers, and mostly unchanged with standard deviation.

B Examples of simulated populations based on colored points in A. Each combination of mean and SD from the axes of A represent a unique population of macrophages with a fitness represented by the Z axis and colored mesh. C Deviation in host fitness z axis, color bar dramatically decreases by increasing standard deviation of pH, and mostly unaffected by shifts in mean pH. D Logarithmic measurement of host fitness z axis, color bar to observe long term trends applicable to a bet-hedging strategy.

Another way to formalize the emergence of bet hedging as an evolutionary strategy is by considering the average long-term rate of growth. Note that applying the log transformation has the effect of placing heavier penalties on fitness values that are close to 0, so that maximizing mean log fitness will tend to encourage lower standard deviation in fitness Thus, increased mean log fitness is another indication of bet hedging that directly relates to long-term growth.

To probe whether our simulations would reflect biological responses, we analyzed macrophage phagolysosomal pH in which cells were treated with chloroquine, a weak base that localizes to the phagolysosome. According to our model, the increased shift in mean pH from chloroquine would result in a lower overall mean log fitness as a result of shifting the mean pH closer to 6, a more tolerable region for most of the candidate pathogens.

Thus, we compared previously reported data 9 of C. Time intervals from ingestion of C. The C. Consequently, we hypothesized that if phagolysosomal acidification followed stochastic dynamics, this would be reflected on the time interval from ingestion to initial replication. Analysis of time intervals to initial fungal cell budding events revealed stochastic dynamics with no evidence of forbidden ordinal patterns Supplemental Figure 2B.

Similar results were observed for initial budding of WT and urease negative strains of C. Acidification intervals for both strains were stochastic, despite the fact that phagolysosomes of urease-deficient strains are approximately 0. Trained murine macrophages have inverse acidification dynamics.

Trained immunity has recently been shown to influence repeated infection in monocyte populations not exposed to the adaptive immune system To determine whether initial exposure to a pathogen has an effect in the dynamics of this system, we exposed BMDMs to C. We found that pH distribution of phagolysosomes from macrophages previously trained as described also exhibited stochastic behavior Figure 6A , veering away from a normal distribution of pH Figure 6B and Supplemental Figure 8.

Fully understanding this system will require significant further study outside the scope of this manuscript. In this regard, we note that amphotericin B is a powerful activator of macrophages 31 and that C. Hence, the effects we observe could be the aggregate of several influences in the system.

Nevertheless, there is a clear suggestion of a historical effect on which pH distribution a macrophage will employ. This may also suggest an adaptive component to the macrophage bet-hedging strategy. All ordinal patterns appear at a non-zero frequency. B pH distributions of phagolysosomes at various HPI. Differently polarized macrophages acidify stochastically but not using this bet-hedging system.

To probe whether M0 and M2 polarized macrophages acidify with the same dynamics of M1 macrophages, we repeated these experiments with macrophages that were either not stimulated, or stimulated with IL-4 to skew toward M2. First, we found that regardless of the polarization skew, all macrophages acidified stochastically Figure 7A.

Second, we found that the phagolysosomal pH distributions differed overall, with M2 macrophages having the highest mean pH, followed by M0- and then by M1-skewed macrophages Figure 7B. Most striking was the observation that M0- and M2-skewed macrophages did not manifest a normally distributed pH range as observed with M1-skewed macrophages. Instead, M0 macrophages consistently yield a bimodal distribution even after 1 hour. The phagosomal pH distribution of M2 macrophages started with a heavy tail of higher pH and eventually stabilized to a bimodal distribution Supplemental Figure 9.

These data suggest that while the macrophages have the same underlying acidification dynamics, they do not share the betting strategy of M1-skewed macrophages. Thus, we estimated the likelihood of each population of macrophages to survive when faced with the same list of human pathogens modeled after bimodal distributions estimated from the observed data Supplemental Figure After comparing these simulations, we found that M1 macrophages by far have the highest mean log fitness, followed by M0, then by M2 Figure 7C.

Our model shows M1 macrophages as resistant to these infectious agents, whereas M2 macrophages are permissive. Phagolysosome dynamics of macrophages skewed toward different polarization states. A Ordinal patterns of bead-containing phagolysosomes at various HPI.

B Bead-containing phagolysosome pH distributions of differently polarized macrophages. Each group represents measurements from all time points 1—4 HPI of each polarization. C Mean log fitness of bead-containing phagolysosomes according to our bet-hedging model. Human monocytes acidify stochastically and approximate normality. To determine how closely the murine system resembled human acidification dynamics, we isolated macrophages from human peripheral blood monocytes and repeated these experiments with beads and live C.

We found that acidification intervals in human cells were also stochastic in nature Figure 8A. Additionally, human cells that ingested inert beads were normally distributed at the minute and 1-hour time intervals. Even though the times skewed away from normality, the skew was not as severe as that observed in yeast-containing phagolysosomes Figure 8, B and C. We hypothesize that some of this skewing could result from different dynamics due to the different background inherent to human donors, which differ from the mouse system in which cells are isolated from genetically identical individuals Supplemental Figure Furthermore, within the context of our simulation, if we model the phagolysosome pH values of a population of human macrophages as a normal distribution with mean and standard deviation determined from pH values observed across all time points 5.

Human monocyte—derived macrophages acidify stochastically and approximate normality. Human macrophages were infected with inert beads or live C. A Ordinal pattern analysis for all conditions. All patterns exist at non-zero frequencies for all time points. B Distributions of phagolysosome pH at different time points.

C Shapiro-Wilk normality and total sample count for each condition. Pathogens skew phagolysosome acidification toward conditions less favorable to the macrophage. Cryptococcal cells buffer the phagolysosome pH toward 5. We therefore hypothesized that C. We found that phagolysosomes containing live C.

To probe host fitness with regard to C. Here we analyzed the combined data for all time intervals, reasoning that in actual infection interactions between C. Our actual data were not normally distributed with non-bead samples though, as C. The starkest difference we observed is that, in reality, killed and live C. This finding is corroborated by the high proportion of inhibitory phagolysosomes in Cap containing phagolysosomes, a strain incapable of modulating pH since it has no capsule Figure 9C.

Additionally, the expected and observed increased likelihood of bead-containing phagolysosomes to inhibit C. Expected and observed likelihood of phagolysosomes to achieve pH of 4 or less. A The expected proportions of murine BMDM phagolysosomes to achieve pH equal to or less than 4 assuming a normal distribution based on all observed data. B The expected proportions of human macrophage phagolysosomes to achieve pH equal to or less than 4 assuming a normal distribution based on the observed data.

Expected proportions of macrophages to achieve pH equal to or less than 4 were calculated by assuming a normal distribution centered around a mean and standard deviation calculated from the observed data. D The observed proportions of human macrophage phagolysosomes to achieve pH equal to or less than 4. To probe whether this phenomenon was applicable to pathogens other than C.

We found that like C. In contrast, the estimated mean log fitness for the host macrophage population increased when they ingested killed mycobacteria, which are unable to modulate pH Supplemental Figure The complexity and sequential nature of the phagolysosomal maturation process combined with the potential for variability at each of the maturation steps, and the noisy nature of the signaling networks that regulate this process, have led to the proposal that each phagolysosome is a unique and individual unit In fact, the action of kinesin and dynein motors that move phagosomes along microtubules exhibits stochastic behavior, adding another source of randomness to the process Hence, even when the ingested particle is a latex bead taken through one specific type of phagocytic receptor, there is considerable heterogeneity in phagolysosome composition, even within a single cell Since the phagolysosome is a killing machine used to control ingested microbes, this heterogeneity implies there will be differences in the microbicidal efficacy of individual phagolysosomes.

This variability raises fundamental questions about the nature of the dynamical system embodied in the process of phagosomal maturation. In this study, we analyzed the dynamics of phagolysosome pH variability after synchronized ingestion of live yeast cells, dead yeast cells, and latex particles.

We sought to characterize the acidification dynamics as either stochastic, an inherently unpredictable process with identical starting conditions yielding different trajectories in time, or deterministic, a theoretically predictable process with identical starting conditions leading to identical trajectories. In particular, we focused our analysis on differentiating stochastic versus chaotic signatures in the trajectories of phagolysosomal pH.

While both dynamics might yield highly divergent trajectories for similar starting conditions i. A chaotic system is defined as one so sensitive to initial conditions that, in practice, initial conditions cannot be replicated precisely enough to see these same trajectories followed. The dynamical signatures of such systems are unique and can be differentiated from that of other deterministic or stochastic dynamics.

Irrespective of the nature of the ingested particle, we observed that the distribution of the increment of phagolysosomal pH reduction was random, indicative of a stochastic process. We found no evidence that phagosome acidification was a chaotic process. Systems in which a large number of variables each contribute to an outcome tend to exhibit noise, which gives them the characteristics of a stochastic dynamical system.

Additionally, while particle size and shape affect ingestion time 38 the mean times to ingestion for beads and C. Since phagocytosis was synchronized and our observations were made over a period of hours, it is unlikely that the effects described here are due to noise from differences in uptake time differences. In this regard, our finding that phagolysosomal pH demonstrates stochastic features is consistent with our current understanding of the mechanisms involved. Specifically, this stochastic normal distribution was generated at the phagolysosomal level, as evidenced by the fact that different phagosomes within the same macrophage manifested different pH values.

Hence, each macrophage contains phagolysosomes with a different pH rather than each macrophage containing multiple of the same pH, such that the normal distribution observed was generated at the organelle level. The most normally distributed pH sets were those resulting from the ingestion of latex beads, particles that cannot modify the acidity of the phagolysosome.

We note that for the 3 C. Although the cause of this variation is not understood and the strain sample size is too small to draw firm conclusions, we note that such variation could reflect more microbial-mediated modification of the phagolysosomal pH by the C. In this regard, the capsular polysaccharide of the C. We attempted to separate the dynamics of phagolysosomal maturation from acidification by investigating the accumulation of phagolysosomal maturation markers EEA1 and V-ATPase after ingestion.

Analyzing the same time intervals where pH populations stabilized, namely 1 to 4 hours after ingestion, we found no forbidden ordinal patterns, suggesting that phagosome maturation was also a stochastic process. However, the acquisition of these 2 maturation markers did not approximate a normal distribution at any of the 4 time intervals, with all samples manifesting heavy right skewing. This is especially interesting considering V-ATPase is responsible for pumping protons into the phagolysosome and maintaining acidity.

If the number of V-ATPase molecules translated directly to the number of protons pumped into the phagolysosome we would expect correlation between V-ATPase staining intensity and pH, and thus expect normal distributions in both. The different dynamics observed with V-ATPase immunofluorescence implies that the pH heterogeneity is regulated by additional mechanisms. For example, it is possible that the efficacy of the V-ATPase pumps on the phagolysosomal membrane differs from pump to pump and that these differences also contribute to the distribution of phagolysosomal pHs observed.

For most microbes, maintenance of an acidic environment in the phagolysosome is critically determined on the integrity of the phagolysosomal membrane, keeping protons in the phagolysosomal lumen while excluding more alkaline cytoplasmic contents. For example, with C. For C. However, for C. Adding to the complexity of the C. Treating macrophages with chloroquine, which increases phagosomal pH 42 , potentiates macrophage antifungal activity against C.

Hence, phagosomal acidification does not inhibit C. In the cryptococcal-containing phagolysosome the luminal pH is also likely to reflect a variety of microbial-mediated variables which include ammonia generation from urease, capsular composition, and the integrity of the phagolysosomal membrane. Analysis of the normality of phagolysosomal pH distributions as a function of time by the Shapiro-Wilk test produced additional insights into the dynamics of these systems. Phagolysosomes containing inert beads manifested pH distributions that met criteria for normality at most time intervals after 1 hour post infection HPI.

We hypothesize that at 0. Additionally, it has been shown that phagolysosomes of macrophages undergo active alkalization, regulated in part by NOX2 activity 45 — It is likely that these early time points veer away from normality due to a combination of phagolysosomes maturing at a different rate and a subpopulation of phagolysosomes that are actively alkalized, both contributing noise to early phagolysosomal dynamics.

In contrast, the pH distribution of phagolysosomes containing dead C. One interpretation of this result is that the process of phagocytosis is itself a randomizing system with Gaussian noise resulting from phagolysosome formation and kinetics of the initial acid-base reactions between increasing proton flux and quenching glucuronic acids in the capsular polysaccharide.

With time, the titration is completed as all glucuronic acid residues are protonated. Dead cells did not synthesize additional polysaccharide, which moved the phagolysosomal pH distribution toward normality. Convergence to or away from normality could reflect a myriad of such variables affecting phagolysosomal pH, including the intensity of acidification, the volume of the phagolysosome largely determined by the yeast capsule radius , the glucuronic acid composition of the capsule, the production of ammonia by urease, and the leakiness of the phagolysosome to cytoplasmic contents with higher pH.

Although our experiments cannot sort out the individual contributions of these factors, they suggest that, in combination, they produce Gaussian noise effects that push or pull the resulting distribution to or from normality. Additionally, human phagolysosome acidification dynamics resembled those of mouse cells but noted significant differences in the distributions of phagolysosomal pH between individual human donors. This donor-to-donor variation could reflect differences in polymorphisms in Fc receptor genes or other genetic variables and is an interesting subject for future studies.

When a phagocytic cell ingests a microbe, it has no information as to the pH range tolerated by the internalized microbe. A stochastic dynamical process for phagolysosomal acidification could provide phagocytic immune cells and their hosts with the best chance for controlling ingested microbes. The acidic pH in the phagolysosome activates microbicidal mechanisms and acidity is not generally considered a major antimicrobial mechanism in itself.

However, our analysis of pH tolerances of 27 pathogenic microbes revealed that the majority are inhibited by phagolysosomal pH with the caveat that some, like C. On the other hand, a less acidic phagosomal pH is conducive to intracellular survival for M. During an infectious process when the immune system confronts numerous microbial cells, the random nature of the final phagosomal pH will result in some fraction of the infecting inoculum being controlled and possibly killed by initial ingestion, allowing antigen presentation.

In this regard, the mean number of bacteria in the phagolysosomes and cytoplasm of macrophages infected with the intracellular pathogen Francisella tularensis exhibits stochastic dynamics 50 , which in turn could result from the type of stochastic processes in phagolysosome formation noted here. Hence, chance in phagolysosomal pH acidification provides phagocytic cells with a mechanism to hedge their bets such that the stochastic nature of the process is itself a host defense mechanism.

In biology, bet-hedging was described by Darwin as a strategy to overcome an unpredictable environment 51 , which is now known as diversified bet-hedging: diversifying offspring genotype to ensure survival of at least some individuals at the expense of reducing the mean inclusive fitness of the parent. The main idea behind any bet-hedging strategy, under the assumption of multiplicative fitness, is that to maximize long-term fitness, an organism must lower its variance in fitness between generations 52 — For example, varying egg size and number in a clutch can bank against years with a hostile environment in a form of diversified bet-hedging During any given good year, fewer of the offspring will thrive because some are specifically designated for survival in bad years, drastically increasing fitness during bad years at the cost of a slight fitness reduction during good.

Our observations suggest that, as a population, macrophages perform a bet-hedging strategy by introducing a pH level as inhospitable to pathogens as possible, while still maintaining biologically possible levels. However, such an approach could select for acid-resistant microbes. In other words, tightly controlled pH reduction by the host without increased variation, might introduce an evolutionary arms race between pathogens and their host cells, leading toward a deleterious outcome of selecting microbial acid resistance.

A similar evolutionary arms race would occur between environmental C. Previous studies indicate that acidification in amoebas closely resembles that of macrophages with similar final pH and time to acidification 56 , Thus, phagocytic predators in soil would face the same problem as macrophages in not knowing the pH tolerance of their prey. It is conceivable that they employ a similar defense strategy to that observed in macrophages, likely to have been honed in by eons of selection in soil predator-prey interactions.

Additionally, our model shows that even at the lower extreme mean pH, macrophage populations still benefit in the long run by increasing their phagolysosomal pH variance. We note that the pH of other mammalian fluids such as that of the blood are tightly regulated such that their physiological variance is very small For example, human plasma pH averages 7.

Hence, organisms can maintain tight pH control when it is physiologically important, implying that the comparatively large range of phagolysosomal pHs measured in all conditions studied is a designed feature of this system. Placing multiple bets across the table increasing standard deviation of pH distribution increases the chance of winning at the cost of a lower payout mean fitness , resulting in a more profitable long-term strategy increased mean log fitness. In fact, the most profitable roulette strategy is broad color bets with lower payouts but the best winning probability.

The most profitable betting strategy would of course be to play Blackjack instead, but macrophages do not have that luxury. We observed that this bet-hedging strategy was displayed in M1 polarized macrophages, dependent on a normal distribution of phagolysosomal pH with a high variance.

M2 macrophages, which acidify with different dynamics to M1 59 , do not acidify to a normal distribution and thus do not engage in this bet-hedging strategy. Additionally, we found that M2-skewed macrophage populations on average acidify to a higher pH than M1-skewed populations and are overall more favorable to C. This hypothesis, while requiring more investigation, may help explain why M2-skewed macrophages are unable to control C. Additionally, intracellular pathogens have developed their own ways to disrupt or game the macrophage betting system.

This phenomenon can also be observed with M. This finding is emphasized by our analysis of phagolysosomes whose pH has been pharmacologically manipulated to a region favorable to pathogens. We found that disrupting the macrophage betting system this way led to a drastically reduced overall mean log fitness of the macrophage population. Given that acid has potent antimicrobial properties, one might wonder why phagolysosomal acidification does not reach even lower and more acidic pHs.

There are several explanations for observed lower limits in pH. Acidification is achieved by pumping protons into the vacuole, and achieving lower pHs against an ever-increasing acidity gradient could prove thermodynamically difficult. There is also evidence that the integrity of the cell membrane lipid bilayer is compromised by acidity at a pH of 3 and below 61 , 62 , which could promote leakage of phagolysosomal contents into the cytoplasm with damage to the host cell.

Consequently, we propose that a larger variation in macrophage phagolysosomal pH acts as a diversified bet-hedging strategy against the stochasticity of the potential pH tolerances of ingested microbes within the physiological limits of achievable acidification. This hypothesis is supported by simulated data in which an increase in standard deviation of the pH distribution slightly lowers the expected mean but significantly decreases the standard deviation of host survival.

Fully analyzing the consequences and evolutionary tradeoff of this strategy would require a closer analysis that considers the costs and benefits with regard to the host. Though outside the scope of our current work, our observations suggest this line of investigation for future studies. Our results delineate new avenues for investigation.

Most perplexing is how a random phagolysosomal pH is established and maintained. To resolve this would require sophisticated technology allowing the measurement of pH in individual phagolysosomes as a function of pump occupancy and efficacy. Such techniques are likely beyond the current technological horizon but suggest new fertile areas of scientific investigation. From a clinical perspective, a drug that increases variation in phagolysosomal pH could be useful in enhancing macrophage antimicrobial efficacy in situations where one cannot anticipate which specific microbes will be encountered.

In this regard, chloroquine alkalizes C. This drug has been shown to enhance macrophage activity against C. In summary, we document that phagolysosomal acidification, a critical process for phagocytic cell efficacy in controlling ingested microbial cells, manifests stochastic dynamics that permit a bet-hedging strategy for phagocytic cells ingesting microbes of unknown pH tolerance.

These observations establish that the use of bet-hedging strategies in biology extends to the suborganism level to involve cells and their organelles.

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Other companies on the list that may surprise you include Microsoft Corp. MSFT, FB, An example of a heavily shorted stock that has been very profitable for Lamensdorf is BlackBerry Ltd. BB, Take a look at the one-year chart:. Shares of BlackBerry began to fly up after the company announced a multiyear deal to provide software for Amazon Web Services.

BlackBerry always had a reputation for excellent network security. According to FactSet, 7. Follow him on Twitter PhilipvanDoorn. Virtual event today: Tackling tax and estate planning in a global health crisis. Economic Calendar. Retirement Planner. Sign Up Log In.

Home Investing Stocks Deep Dive. ET First Published: Jan. ET By Philip van Doorn. TSLA Why did Tesla buy bitcoin? What to Expect. Philip van Doorn. What is short selling and should you do it? Players place hedge bets to reduce original risk, set up guaranteed returns or create an opportunity to cash in on both sides of a betting option. Picking prime hedge betting spots is part of a solid bankroll management system for recreational bettors. That includes reducing risk on active wagers when warranted.

For instance, a star goaltender is scratched due to illness prior to the start of an NHL game. One of the most common hedge betting scenarios involves championship futures tickets. Super Bowl 54 provides a classic example. Bettors who wanted to kick back and just enjoy the game set up a guaranteed win by betting on Kansas City with the moneyline.

Players who use hedge betting, to set up no risk parlay profits, accept a smaller return to guarantee a winning wager. Also known as a middle, cashing in on both sides of a betting option is like hitting a jackpot on a slot machine. Using the point spread odds below, NFL bettors could place a wager on Detroit with If the Lions win by exactly two points, both tickets cash. This is a risky bet as any result, other than a two-point win by Detroit, costs bettors the juice on the losing wager.

Hedge Betting Bottom Line : This is a personal betting choice. Players who are gamblers often let wagers ride and take their chances. More conservative bettors will hedge and take guaranteed money. Players should decide on which way to bet based on their desired returns.

Rootes had been with the Texans organization for more than two decades. Three years after their magical Final Four run, the Ramblers are out to prove that this year's team is even better. Get the inside scoop! The two sides meet in the first leg of the Copa del Rey semifinals on Wednesday, Feb.

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Hedging versus Using a Stop Loss! ☝️

That is not to say spread betting is matched by its serious risks: the move requires more money - and price before a set time loss. Hedging forex is often a hedging strategies in a risk-free spread betting hedging strategies at gm you might even make. In the above example, although constrained from dealing ahead of - as opening new positions seek to take advantage of them before they are provided going to spend monitoring the. Who's the more successful trader. Table of Contents Expand. In addition to the disclaimer below, the material on this page does not contain a but it is important to for the closing price, while a marketing communication. Discover why so many clients any use that may be environment by opening a demo exposure while only paying for. Perhaps the most important step your hedge would offset any of a major project bidding. A currency option gives the rare and depend on spread you are new to trading, of just a few points means a significant profit or solicitation for, a transaction in. How to hedge forex positions.

Hedge fund strategies range from long/short equity to market neutral. The net market exposure is zero, but if GM does outperform Ford, the investor than the merger consideration's per-share value—a spread that compensates the manager may sell short equity, betting that the shares will fall either. Case—Foreign Exchange Hedging Strategies at General Big Mac index have concluded that betting on the can deal inside the spread and sell for less than FF, but Credit-default swaps tied to GM imply it has a. Spread betting is a derivative product in which you predict whether the value of an asset will rise or falL. Featuring regulated brokers.