Why do green rods of frog and toad retinas look green?

Amphibian “green” rods express a blue-sensitive cone visual pigment, and should look yellow. However, when observing them axially under microscope one sees them as green. We used single-cell microspectrophotometry (MSP) to reveal the basis of the perceived color of these photoreceptors. Conventional side-on MSP recording of the proximal cell segments reveals no selective long-wave absorbing pigment explaining the green color. End-on MSP recording shows, in addition to the green rod visual pigment, an extra 2- to 4-fold attenuation being almost flat throughout the visible spectrum. This attenuation is absent in red (rhodopsin) rods, and vanishes in green rods when the retina is bathed in high-refractive media, and at wide illumination aperture. The same treatments change the color from green to yellow. It seems that the non-visual pigment attenuation is a result of slender green rod myoids operating as non-selective light guides. We hypothesize that narrow myoids, combined with photomechanical movements of melanin granules, allow a wide range of sensitivity regulation supporting the operation of green rods as blue receptors at mesopic-to low-photopic illumination levels. End-on transmittance spectrum of green rods looks similar to the reflectance spectrum of khaki military uniforms. So their greenness is the combined result of optics and human color vision.     

Need rods? Get glycine receptors and taurine

Taurine, a multifunctional amino acid prevalent in developing nervous tissues, regulates the number of rod photoreceptors in developing postnatal rodent retina. In this issue of Neuron, Young and Cepko show that taurine acts via GlyRalpha2 subunit-containing glycine receptors expressed by retinal progenitor cells at birth.     

Why cancer and inflammation?

Central to the development of cancer are genetic changes that endow these “cancer cells” with many of the hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors, are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the connection between the inflammatory response and cancer.     

Why hunt? Why gather? Why share? Hadza assessments of foraging and food-sharing motive

Over the last half century, anthropologists have vigorously debated the adaptive motivations underlying food acquisition choices and food-sharing among hunter-gatherer groups. Numerous explanations have been proposed to account for high-levels of generosity in food-sharing, including self- and family-provisioning, reciprocity, tolerated theft and pro-social- or skill-signaling. However, few studies have asked foragers directly and systematically about the motivations underlying their foraging and sharing decisions. We recruited 110 Hadza participants and employed a combination of free-response, yes/no, ranking and forced-choice questions to do just this. In free-response answers, respondents typically gave outcome-oriented accounts of foraging motive (e.g., to get food) and moralistic accounts of sharing motive (e.g., I have a good heart). In ranking tasks, participants gave precedence to reciprocity as a motive for sharing food beyond the household. We found small but clear gender differences in foraging motive, in line with previous predictions: women were more likely than men to rank family-provisioning highly whereas men were more likely than women to rank skill-signaling highly. However, despite these gender differences, the relative importance of different motivations was similar across genders and skill-signaling, sharing and family-provisioning were the most important motivators of foraging activity for both men and women. Contrary to the expectations of tolerated theft, peer complaints and requests for food ranked very low. There are several compelling reasons that evolutionary thinkers, typically interested in ultimate-level adaptive processes, have traditionally eschewed direct and explicit investigations of motive. However, these data may yet provide important insights.     

Connecting ORC and Heterochromatin: Why?

Considerable evidence connects heterochromatin or silenced chromatin with the Origin Recognition Complex (ORC) which is needed for initiation of DNA replication.1-7 In this review we consider biological forces that might be served by this connection. The prevailing view in the literature is that ORC recruits heterochromatin. This seems paradoxical because a replication initiator, ORC, would be recruiting factors which seem to oppose replication by forming inaccessible chromatin structures. Here we suggest a different view, that heterochromatin recruits ORC to facilitate replication of hard-to-replicate heterochromatic regions. We consider how existing data can be reconciled with this viewpoint, and we consider the biological predictions that arise from this perspective.     

Were Gram-positive rods the first bacteria?

At some point in the evolution of life, the domain Bacteria arose from prokaryotic progenitors. The cell that gave rise to the first bacterium has been given the name (among several other names) "last universal ancestor (LUA)". This cell had an extensive, well-developed suite of biochemical strategies that increased its ability to grow. The first bacterium is thought to have acquired a covering, called a sacculus or exoskeleton, that made it stress-resistant. This protected it from rupturing as a result of turgor pressure stress arising from the success of its metabolic abilities. So what were the properties of this cell's wall? Was it Gram-positive or Gram-negative? And was it a coccus or a rod?     

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 are behavioral and immune traits linked?

Through behavior, animals interact with a world where parasites abound. It is easy to understand how behavioral traits can thus have a differential effect on pathogen exposure. Harder to understand is why we observe behavioral traits to be linked to immune defense traits. Is variation in immune traits a consequence of behavior-induced variation in immunological experiences? Or is variation in behavioral traits a function of immune capabilities? Is our immune system a much bigger driver of personality than anticipated? In this review, I provide examples of how behavioral and immune traits co-vary. I then explore the different routes linking behavioral and immune traits, emphasizing on the physiological/hormonal mechanisms that could lead to immune control of behavior. Finally, I discuss why we should aim at understanding more about the mechanisms connecting these phenotypic traits.     

Which Neurodevelopmental Disorders Get Researched and Why?

Aim

There are substantial differences in the amount of research concerned with different disorders. This paper considers why.

Methods

Bibliographic searches were conducted to identify publications (1985–2009) concerned with 35 neurodevelopmental disorders: Developmental dyslexia, Developmental dyscalculia, Developmental coordination disorder, Speech sound disorder, Specific language impairment, Attention deficit hyperactivity disorder, Autistic spectrum disorder, Tourette syndrome, Intellectual disability, Angelman syndrome, Cerebral palsy, Cornelia de Lange syndrome, Cri du chat syndrome, Down syndrome, Duchenne muscular dystrophy, Fetal alcohol syndrome, Fragile X syndrome, Galactosaemia, Klinefelter syndrome, Lesch-Nyhan syndrome, Lowe syndrome, Marfan syndrome, Neurofibromatosis type 1, Noonan syndrome, Phenylketonuria, Prader-Willi syndrome, Rett syndrome, Rubinstein-Taybi syndrome, Trisomy 18, Tuberous sclerosis, Turner syndrome, Velocardiofacial syndrome, Williams syndrome, XXX and XYY. A publication index reflecting N publications relative to prevalence was derived.

Results

The publication index was higher for rare than common conditions. However, this was partly explained by the tendency for rare disorders to be more severe.

Interpretation

Although research activity is predictable from severity and prevalence, there are exceptions. Low rates of research, and relatively low levels of NIH funding, characterise conditions that are the domain of a single discipline with limited research resources. Growth in research is not explained by severity, and was exceptionally steep for autism and ADHD.     

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 weight?

Whether phylogenetic data should be differentially or equally weighted is currently debated. Further, if differential weighting is to be explored, there is no consensus among investigators as to which weighting scheme is most appropriate. Mitochondrial genome data offer a powerful tool in assessment of differential weighting schemes because taxa can be selected from which a highly corroborated phylogeny is available (so that accuracy can be assessed), and it can be assumed that different data partitions share the same history (so that gene-sorting issues are not so problematic). Using mitochondrial data from 17 mammalian genomes, we evaluated the most commonly used weighting schemes, such as successive weighting, transversion weighting, codon-based weighting, and amino acid coding, and compared them to more complex weighting schemes including a 6-parameter weighting, pseudoreplicate reweighting, and tri-level weighting. We found that the most commonly used weighting schemes perform the worst with these data. Some of the more complex schemes perform well, however, none of them is consistently superior. These results support ones biases; if one has a predilection to avoid differential weighting, these data support equally weighted parsimony and maximum likelihood. Others might be encouraged by these results to try weighting as a form of data exploration.