Why do imagery and perception look and feel so different?

Despite the past few decades of research providing convincing evidence of the similarities in function and neural mechanisms between imagery and perception, for most of us, the experience of the two are undeniably different, why? Here, we review and discuss the differences between imagery and perception and the possible underlying causes of these differences, from function to neural mechanisms. Specifically, we discuss the directional flow of information (top-down versus bottom-up), the differences in targeted cortical layers in primary visual cortex and possible different neural mechanisms of modulation versus excitation. For the first time in history, neuroscience is beginning to shed light on this long-held mystery of why imagery and perception look and feel so different.This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation''.     

Why do some African children develop severe malaria?

Malaria is still a major cause of death and severe illness among children in many parts of tropical Africa, but only a small proportion of children, perhaps 1-2%, who become ill with malaria develop severe disease. Why only, some children experience a severe or fatal attack is not understood clearly. In this article, Brian Greenwood, Kevin Marsh and Robert Snow review some of those characteristics of the parasite and the host that may influence the outcome of a malaria infection. Identification of the relative importance of the many factors likely to be involved is needed in order to develop rational strategies for the prevention of deaths from malaria among children in malaria-endemic areas.     

Why do bacteria use so many enzymes to scavenge hydrogen peroxide?

Hydrogen peroxide (H(2)O(2)) is continuously formed by the autoxidation of redox enzymes in aerobic cells, and it also enters from the environment, where it can be generated both by chemical processes and by the deliberate actions of competing organisms. Because H(2)O(2) is acutely toxic, bacteria elaborate scavenging enzymes to keep its intracellular concentration at nanomolar levels. Mutants that lack such enzymes grow poorly, suffer from high rates of mutagenesis, or even die. In order to understand how bacteria cope with oxidative stress, it is important to identify the key enzymes involved in H(2)O(2) degradation. Catalases and NADH peroxidase (Ahp) are primary scavengers in many bacteria, and their activities and physiological impacts have been unambiguously demonstrated through phenotypic analysis and through direct measurements of H(2)O(2) clearance in vivo. Yet a wide variety of additional enzymes have been proposed to serve similar roles: thiol peroxidase, bacterioferritin comigratory protein, glutathione peroxidase, cytochrome c peroxidase, and rubrerythrins. Each of these enzymes can degrade H(2)O(2) in vitro, but their contributions in vivo remain unclear. In this review we examine the genetic, genomic, regulatory, and biochemical evidence that each of these is a bonafide scavenger of H(2)O(2) in the cell. We also consider possible reasons that bacteria might require multiple enzymes to catalyze this process, including differences in substrate specificity, compartmentalization, cofactor requirements, kinetic optima, and enzyme stability. It is hoped that the resolution of these issues will lead to an understanding of stress resistance that is more accurate and perceptive.     

Why do nitric oxide synthases use tetrahydrobiopterin?

We are combining stopped-flow, stop-quench, and rapid-freezing kinetic methods to help clarify the unique redox roles of tetrahydrobiopterin (H(4)B) in NO synthesis, which occurs via the consecutive oxidation of L-arginine (Arg) and N-hydroxy-L-arginine (NOHA). In the Arg reaction, H(4)B radical formation is coupled to reduction of a heme Fe(II)O(2) intermediate. The tempo of this electron transfer is important for coupling Fe(II)O(2) formation to Arg hydroxylation. Because H(4)B provides this electron faster than can the NOS reductase domain, H(4)B appears to be a kinetically preferred source of the second electron for oxygen activation during Arg hydroxylation. A conserved Trp (W457 in mouse inducible NOS) has been shown to influence product formation by controlling the kinetics of H(4)B electron transfer to the Fe(II)O(2) intermediate. This shows that the NOS protein tunes H(4)B redox function. In the NOHA reaction the role of H(4)B is more obscure. However, existing evidence suggests that H(4)B may perform consecutive electron donor and acceptor functions to reduce the Fe(II)O(2) intermediate and then ensure that NO is produced from NOHA.     

Why do we still use stepwise modelling in ecology and behaviour?

1. The biases and shortcomings of stepwise multiple regression are well established within the statistical literature. However, an examination of papers published in 2004 by three leading ecological and behavioural journals suggested that the use of this technique remains widespread: of 65 papers in which a multiple regression approach was used, 57% of studies used a stepwise procedure. 2. The principal drawbacks of stepwise multiple regression include bias in parameter estimation, inconsistencies among model selection algorithms, an inherent (but often overlooked) problem of multiple hypothesis testing, and an inappropriate focus or reliance on a single best model. We discuss each of these issues with examples. 3. We use a worked example of data on yellowhammer distribution collected over 4 years to highlight the pitfalls of stepwise regression. We show that stepwise regression allows models containing significant predictors to be obtained from each year's data. In spite of the significance of the selected models, they vary substantially between years and suggest patterns that are at odds with those determined by analysing the full, 4-year data set. 4. An information theoretic (IT) analysis of the yellowhammer data set illustrates why the varying outcomes of stepwise analyses arise. In particular, the IT approach identifies large numbers of competing models that could describe the data equally well, showing that no one model should be relied upon for inference.     

Why do proteins use selenocysteine instead of cysteine?

Selenocysteine is present in a variety of proteins and catalyzes the oxidation of thiols to disulfides and the reduction of disulfides to thiols. Here, we compare the kinetic and thermodynamic properties of cysteine with its selenium-containing analogon, selenocysteine. Reactions of simple selenols at pH 7 are up to four orders of magnitude faster than their sulfur analogs, depending on reaction type. In redox-related proteins, the use of selenium instead of sulfur can be used to tune electrode, or redox, potentials. Selenocysteine could also have a protective effect in proteins because its one-electron oxidized product, the selanyl radical, is not oxidizing enough to modify or destroy proteins, whereas the cysteine-thiyl radical can do this very rapidly.     

Why do men seek status? Fitness payoffs to dominance and prestige

In many human societies, high male social status associates with higher fertility, but the means by which status increases lifetime fitness have not been systematically investigated. We analyse the pathways by which male status begets reproductive success in a small-scale, Amerindian society. Men who are more likely to win a dyadic physical confrontation, i.e. dominant men, have higher intra-marital fertility for their age, and men with more community-wide influence, i.e. prestigious men, exhibit both higher intra-marital fertility and lower offspring mortality. Both forms of status elicit support from allies and deference from competitors, but high status men are not provisioned more than their peers. Prestigious but not dominant men marry wives who first give birth at earlier ages, which multivariate analysis suggests is the strongest pathway between status and fitness in this population. Furthermore, men are motivated to pursue status because of fitness gains both within and outside of marital unions: dominant and prestigious men have more in-pair surviving offspring as well as more extra-marital affairs.     

The effects of MAPK p38α on AZT resistance against reactivating HIV-1 replication in ACH2 cells

Molecular and Cellular Biochemistry - Changes in wall shear stress of blood vessels are assumed to be an important component of many physiological and pathophysiological processes. However, due to...     

Why do men marry and why do they stray?

Humans are quite unusual compared to other great apes in that reproduction typically takes place within long-term, iteroparous pairings--social arrangements that have been culturally reified as the institution of marriage. With respect to male behaviour, explanations of marriage fall into two major schools of thought. One holds that marriage facilitates a sexual division of labour and paternal investment, both important to the rearing of offspring that are born helpless and remain dependent for remarkably long periods (provisioning model). And the other suggests that the main benefits which men receive from entering into marriage derive from monopolizing access to women's fertility (mating effort model). In this paper, we explore extramarital sexual relationships and the conditions under which they occur as a means of testing predictions derived from these two models. Using data on men's extramarital sexual relationships among Tsimane forager-horticulturists in lowland Bolivia, we tested whether infidelity was more common when men had less of an opportunity to invest in their children or when they risked losing less fertility. We found that Tsimane men appear to be biasing the timing of their affairs to when they are younger and have fewer children, supporting the provisioning model.     

Why do bees turn back and look?

The timing of learning of colour and shape of the food source, as well as of near-by landmarks, was examined exploiting a behaviour described recently, the Turn Back and Look behaviour (TBL): Bees departing from a novel food source after feeding turn around to view it at a short distance (Figs. 2, 3) before departing for the hive. They repeat this behaviour on several successive visits, termed the TBL phase (Fig. 5). To examine the function of the TBL, I trained individual bees in 4 different modes. In the first 3 they could view a food source or a landmark of a particular colour or shape during (i) arrival as well as departure, (ii) only arrival, and (iii) only departure; in the final mode (iv) the bees viewed one colour (or shape) on arrival, and another on departure. At the end of the TBL phase, the bees were tested by offering them a choice between the visual stimulus to which they were trained (modes i–iii) and a different (novel) one, or between the stimulus viewed on arrival and that viewed on departure (mode iv). The test results show that learning after feeding (while performing the TBL), i.e. backward conditioning, occurs regardless of whether the colour (Fig. 6, Fig. 10a) or shape (Fig. 7) of the food source, or the colour (Fig. 10b), shape (Fig. 11), and position (Fig. 12) of a near-by landmark is considered. Bees trained in mode (iv) preferred the stimulus learned on arrival over that learned on departure in almost all cases. However, a stimulus viewed exclusively on departure (mode iii) was often learned as well as when it was viewed exclusively on arrival (mode ii) (Figs. 10a, 11, 12), or both on arrival and departure (mode i) (Fig. 6). The finding that the timing of learning can be manipulated suggests that it is not based on hard wired predispositions to learn particular visual cues on arrival, and others on departure.     

Why do cladocerans fail to control algal blooms?

Field studies show that even at high nutrient loads phytoplankton may be kept at low levels by filter-feeding zooplankton for a period of weeks (spring clear water phase in lakes) or months (low-stocked fish-ponds). In the absence of planktivorous fish, large-bodied cladocerans effectively control the abundance of algae of a broad size spectrum. Laboratory experiments show that, although difficult to handle and of poor nutritional value, filamentous algae can also be utilized by large-bodiedDaphnia and prevented from population increase, exactly as the principles of the biomanipulation approach would predict. This is not always the case, however. Even when released from predation, large cladocerans often cannot grow and reproduce fast enough to prevent bloom formation. Sometimes, they disappear when the bloom becomes dense, and the biomanipulation approach is not applicable any more. Recent experimental data on four differently-sizedDaphnia species are used in an attempt to (1) explain why cladocerans fail to control filamentous cyanobacteria when filament density is high, and (2) determine the critical filament density at whichDaphnia becomes ineffective. At this critical concentration,Daphnia growth and reproduction is halted, and no positive numerical response to growing phytoplankton standing crop should be expected fromDaphnia population. Bloom formation thus becomes irreversible. The question of what can be done to overcome this bottleneck of the biomanipulation approach may become one of the most challenging questions in plankton ecology in the nearest future.     

Motivation and placebos: do different mechanisms occur in different contexts?

This paper challenges the common assumption that the mechanisms underlying short-term placebo paradigms (where there is no motivation for health improvement) and long-term placebo paradigms (where patients value improvement in their health) are the same. Three types of motivational theory are reviewed: (i) classical placebo motivation theory that the placebo response results from the desire for therapeutic improvement; (ii) goal activation model that expectancy-driven placebo responses are enhanced when the placebo response satisfies an activated goal; and (iii) motivational concordance model that the placebo response is the consequence of concordance between the placebo ritual and significant intrinsic motives. It is suggested that current data are consistent with the following theory: response expectancy, conditioning and goal activation are responsible for short-term placebo effects but long-term therapeutic change is achieved through the effects of goal satisfaction and affect on the inflammatory response system and hypothalamic-pituitary-adrenal axis. Empirical predictions of this new theory are outlined, including ways in which placebo effects can be combined with other psychologically mediated effects on short-term and long-term psychological and physiological state.     

Why and how do we model circadian rhythms?

In our attempts to understand the circadian system, we unavoidably rely on abstractions. Instead of describing the behavior of the circadian system in all its complexity, we try to derive basic features from which we form a global concept on how the system works. Such a basic concept is a model of reality. The author discusses why it is advantageous or even necessary to transform conceptual models into mathematical formulations. As examples to demonstrate those advantages, the author reviews 4 types of mathematical models: negative feedback models thought to operate within pacemaker cells, models on coupling between pacemaker cells to generate pacemaker output, oscillator models describing the behavior of the composite circadian pacemaker, and models describing how the circadian pacemaker influences behavior.     

Why and where do oligochaetes hide their cocoons?

In Mondsee, a prealpine lake of Austria, abundance and vertical distribution of oligochaetes were investigated at four different depths (5, 10, 20 and 40 m). Adult oligochaetes and cocoons were found to be almost always absent in the uppermost centimeters of sediment. A hypothesis was developed that predation by fish is one factor, among others, inducing the observed vertical distribution patterns. In the laboratory the predation efficiency of the fish Abramis brama decreased with increasing sediment cover over the cocoons. Tolerance limits of embryonic worms to anaerobic conditions (LC 50 values) were found to differ for different developmental stages, ranging from 28 hrs for eggs to 43 hrs for fully developed embryos. Oxygen uptake rates of oligochaete embryos increased with their stages of development, eggs using 1.51 and fully developed embryos 3.32 nl O₂/ind./h (10°C).     

Why is Substrate Peptide Binding Unsusceptible to Multidrug-Resistant Mutations in HIV-1 Protease? A Structural and Energetic Analysis

Understanding of the molecular mechanism and biological implication underlying the difference in binding of substrate peptides and small-molecule inhibitors to multidrug-resistant mutants of HIV-1 protease would help to develop new anti-HIV agents combating drug resistance. Here, an integration of rigorous quantum mechanics/molecular mechanics (QM/MM) analysis and empirical Poisson–Boltzmann/surface area (PB/SA) model is described to investigate the structural basis and energetic property of wild-type HIV-1 protease and its mutants in recognizing and binding with a wide variety of ligands, including the peptides derived from its cognate cleavage sites and the cleavage site variants as well as a number of FDA-approved protease inhibitors, attempting to explain why is substrate binding unsusceptible to most observed HIV-1 protease mutations. A preliminary test study demonstrates that the combined QM/MM–PB/SA scheme is able to effectively reproduce the relative ligand binding energy changes upon protease single- and double-mutations, albeit the absolute values appear to be different significantly between the calculated and experimental results. With the QM/MM–PB/SA calculations a complete mutation energy map of HIV-1 protease–ligand interactions is created, which unravels distinct affinity pictures of wild-type substrates, substrate variants and, particularly, the protease inhibitors bound to HIV-1 protease mutants, suggesting that, on the one hand, the evaluation pressure under anti-HIV chemotherapies addresses site-directed protease mutations that impair and undermine the intermolecular interactions specific to inhibitors but not substrates; on the other hand, co-evaluation of protease and its substrate peptides provides a more effective mechanism to avoid therapeutic surveillance. Further, nonbonded interaction analysis and computational alanine scanning reveal 12 key residues that is critical for substrate binding, from which the Asn25, Gly27, Ala28, Asp29 and Pro81 are identified that have not yet been found to cause drug resistance and hence would be the promising sites targeted by new protease inhibitors.     

Why do parasitized hosts look different? Resolving the “chicken-egg” dilemma

Phenotypic differences between infected and non-infected hosts are often assumed to be the consequence of parasite infection. However, pre-existing differences in hosts’ phenotypes may promote differential susceptibility to infection. The phenotypic variability observed within the host population may therefore be a cause rather than a consequence of infection. In this study, we aimed at disentangling the causes and the consequences of parasite infection by calculating the value of a phenotypic trait (i.e., the growth rate) of the hosts both before and after infection occurred. That procedure was applied to two natural systems of host–parasite interactions. In the first system, the infection level of an ectoparasite (Tracheliastes polycolpus) decreases the growth rate of its fish host (the rostrum dace, Leuciscus leuciscus). Reciprocally, this same phenotypic trait before infection modulated the future level of host sensitivity to the direct pathogenic effect of the parasite, namely the level of fin degradation. In the second model, causes and consequences linked the growth rate of the fish host (the rainbow smelt, Osmerus mordax) and the level of endoparasite infection (Proteocephalus tetrastomus). Indeed, the host’s growth rate before infection determined the number of parasites later in life, and the parasite biovolume then decreased the host’s growth rate of heavily infected hosts. We demonstrated that reciprocal effects between host phenotypes and parasite infection can occur simultaneously in the wild, and that the observed variation in the host phenotype population was not necessarily a consequence of parasite infection. Disentangling the causality of host–parasite interactions should contribute substantially to evaluating the role of parasites in ecological and evolutionary processes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

What spatial data do we need to develop global mammal conservation strategies?

Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.