Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Altruism can evolve when relatedness is low: evidence from bacteria committing suicide upon phage infection

High relatedness among interacting individuals has generally been considered a precondition for the evolution of altruism. However, kin-selection theory also predicts the evolution of altruism when relatedness is low, as long as the cost of the altruistic act is minor compared with its benefit. Here, we demonstrate evidence for a low-cost altruistic act in bacteria. We investigated Escherichia coli responding to the attack of an obligately lytic phage by committing suicide in order to prevent parasite transmission to nearby relatives. We found that bacterial suicide provides large benefits to survivors at marginal costs to committers. The cost of suicide was low, because infected cells are moribund, rapidly dying upon phage infection, such that no more opportunity for reproduction remains. As a consequence of its marginal cost, host suicide was selectively favoured even when relatedness between committers and survivors approached zero. Altogether, our findings demonstrate that low-cost suicide can evolve with ease, represents an effective host-defence strategy, and seems to be widespread among microbes. Moreover, low-cost suicide might also occur in higher organisms as exemplified by infected social insect workers leaving the colony to die in isolation.     

Local Competition Between Foraging Relatives: Growth and Survival of Bruchid Beetle Larvae

Kin selection theory states that when resources are limited and all else is equal, individuals will direct competition away from kin. However, when competition between relatives is completely local, as is the case in granivorous insects whose larval stages spend their lives within a single seed, this can reduce or even negate the kin-selected benefits. Instead, an increase in competition may have the same detrimental effects on individuals that forage with kin as those that forage with non-kin. In a factorial experiment we assessed the effects of relatedness and competition over food on the survival and on fitness-related traits of the bruchid beetle Callosobruchus maculatus. Relatedness of competitors did not affect the survival of larvae. Larval survival substantially decreased with increasing larval density, and we found evidence that beetles maturing at a larger size were more adversely affected by competition, resulting in lower survival rates. Furthermore, females showed a reduction in their growth rate with increasing larval density, emerging smaller after the same development time. Males increased their growth rate, emerging earlier but at a similar size when food was more limited. Our results add to the growing number of studies that fail to show a relationship between relatedness and a reduction in competition between relatives in closed systems, and emphasize the importance of the scale at which competition between relatives occurs.     

Frequency-dependent conspecific attraction to food patches

In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated.     

Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

Body size but not colony size increases with altitude in the holarctic ant,Leptothorax acervorum

1. Bergmann's rule states that organisms inhabiting colder environments show an increase in body size or mass in comparison to their conspecifics living in warmer climates. Although originally proposed for homoeothermic vertebrates, this rule was later extended to ectotherms. In social insects, only a few studies have tested this rule and the results were ambiguous. Here, ‘body size’ can be considered at two different levels (the size of the individual workers or the size of the colony). 2. In this study, data from 53 nests collected along altitudinal gradients in the Alps were used to test the hypotheses that the worker body size and colony size of the ant Leptothorax acervorum increase with increasing altitude and therefore follow Bergmann's rule. 3. The results show that the body size of workers but not the colony size increases with altitude. Whether this pattern is driven by starvation resistance or other mechanisms remains to be investigated.