Replicating content across a geographically distributed set of servers and redirecting clients to the closest server in terms of latency has emerged as a common paradigm for improving client performance.In this paper, we analyze latencies measured from servers in Google's content distribution network (CDN) to clients all across the Internet to study the effectiveness of latency-based server selection. Our main result is that redirecting every client to the server with least latency does not suffice to optimize client latencies. First, even though most clients are served by a geographically nearby CDN node, a sizeable fraction of clients experience latencies several tens of milliseconds higher than other clients in the same region. Second, we find that queueing delays often override the benefits of a client interacting with a nearby server.To help the administrators of Google's CDN cope with these problems, we have built a system called WhyHigh. First, WhyHigh measures client latencies across all nodes in the CDN and correlates measurements to identify the prefixes affected by inflated latencies. Second, since clients in several thousand prefixes have poor latencies, WhyHigh prioritizes problems based on the impact that solving them would have, e.g., by identifying either an AS path common to several inflated prefixes or a CDN node where path inflation is widespread. Finally, WhyHigh diagnoses the causes for inflated latencies using active measurements such as traceroutes and pings, in combination with datasets such as BGP paths and flow records. Typical causes discovered include lack of peering, routing misconfigurations, and side-effects of traffic engineering. We have used WhyHigh to diagnose several instances of inflated latencies, and our efforts over the course of a year have significantly helped improve the performance offered to clients by Google's CDN.

Why the cerebral cortex folds in some mammals but not in others has long fascinated and mystified neurobiologists. Over the past century-especially the past decade-researchers have used theory and experiment to support different folding mechanisms such as tissue buckling from mechanical stress, axon tethering, localized proliferation, and external constraints. In this review, we synthesize these mechanisms into a unifying framework and introduce a hitherto unappreciated mechanism, the radial intercalation of new neurons at the top of the cortical plate, as a likely proximate force for tangential expansion that then leads to cortical folding. The interplay between radial intercalation and various biasing factors, such as local variations in proliferation rate and connectivity, can explain the formation of both random and stereotypically positioned folds.

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.

There is increasing evidence that alterations in the focus of attention results in changes in neural responding at the most peripheral levels of the auditory system. To date, however, those studies have not ruled out differences in task demands or overall arousal in explaining differences in responding across intermodal attentional conditions. The present study sought to compare changes in the response of cochlear outer hair cells, employing distortion product otoacoustic emissions (DPOAEs), under different, balanced conditions of intermodal attention. DPOAEs were measured while the participants counted infrequent, brief exemplars of the DPOAE primary tones (auditory attending), and while counting visual targets, which were instances of Gabor gradient phase shifts (visual attending). Corroborating an earlier study from our laboratory, the results show that DPOAEs recorded in the auditory ignoring condition were significantly higher in overall amplitude, compared with DPOAEs recorded while participants attended to the eliciting primaries; a finding in apparent contradiction with more central measures of intermodal attention. Also consistent with our previous findings, DPOAE rapid adaptation, believed to be mediated by the medial olivocochlear efferents (MOC), was unaffected by changes in intermodal attention. The present findings indicate that manipulations in the conditions of attention, through the corticofugal pathway, and its last relay to cochlear OHCs, the MOC, alter cochlear sensitivity to sound. These data also suggest that the MOC influence on OHC sensitivity is composed of two independent processes, one of which is under attentional control.

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Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering.

Distributed circuits wherein connections between subcircuit components seem randomly distributed are common to the olfactory circuit, hippocampus, and cerebellum. In such circuits, activation patterns seem random too, showing no detectable spatial preference, and contrast with regions that have topographic connections between subcircuits and topographic activation patterns. Quantitative studies of topographic circuits in the neocortex have yielded common principles of organization. Whether distributed circuits share similar principles of organization is unknown because similar quantitative information is missing and understanding the way they encode information remains a challenge. We addressed these needs by providing a quantitative description of the mouse piriform cortex, a paleocortical distributed circuit that subserves olfaction. The quantitative information provided two insights. First, with a nearly parameter-free model of the olfactory circuit, we show that the piriform cortex robustly maintains odor information and discrimination ability present in the olfactory bulb. Second, the paleocortex is quantitatively different from the neocortex: it has a lower surface area density, which decreases from the anterior to posterior paleocortex contrasting with the uniform neuronal density of the neocortex. These insights might also apply to other distributed circuits.

Three decades ago, Rockel et al. proposed that neuronal surface densities (number of neurons under a square millimeter of surface) of primary visual cortices (V1s) in primates is 2.5 times higher than the neuronal density of V1s in nonprimates or many other cortical regions in primates and nonprimates. This claim has remained controversial and much debated. We replicated the study of Rockel et al. with attention to modern stereological precepts and show that indeed primate V1 is 2.5 times denser (number of neurons per square millimeter) than many other cortical regions and nonprimate V1s; we also show that V2 is 1.7 times as dense. As primate V1s are denser, they have more neurons and thus more pinwheels than similar-sized nonprimate V1s, which explains why primates have better visual acuity., in an influential and controversial article entitled "The basic uniformity in structure of the neocortex," reported that the number of neurons underneath a square millimeter of neocortical surface is constant for six cortical areas and five species with one exception: primate primary visual cortex (V1) has a surface density of about 250,000 neurons/mm 2 , around two and a half times the usual density for other areas studied.The Rockel et al. paper has, for a third of a century, continued to generate controversy for two reasons. One reason stems from its implications for an equally energetic debate among neuroscientist "lumpers" and "splitters." Cortical uniformity supports a theory of neocortical processing wherein different cortical areas are subserved by the same canonical circuit, a view favored by lumpers. Splitters, however, believe each cortical area to be different and doubt the paper's claims (2). The second reason is that studies from various laboratories using different measurement methods over the last three decades have alternately agreed and disagreed with Rockel et al.'s results (2).Notably, however, Rockel et al's studies have never been directly replicated. We set out to repeat the observations for the same areas and species Rockel et al. used. We used Rockel et al.'s counting techniques but with attention to the precepts of modern stereology (2). Our goal was to simply determine if Rockel et al.'s observations are repeatable rather than address the larger question of numerical uniformity of neocortex across species and areas. In an earlier publication (2), we confirmed Rockel et al.'s conclusions for nonvisual areas. Here we focus on the primary V1 for the same species used in the original report of Rockel et al.V1 is part of the visual circuit from the retina to the cortex, which is retinotopically organized, and the 2D image of the world that is mapped onto the retina is recreated in V1 (3). Cells within the retina capture visual information for each image location or pixel such as color and light intensity and convey it to structures in V1 (4, 5), which perform computations that contribute to visual abilities. An especially well-studied V1 structure is a pinwheel, which comprises orientation columns tha...