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Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible. This response functions as a dynamic signaling network, with proteins as nodes and their interactions as edges. We analyze these protein-protein networks across different organisms to understand their intricate intra-cellular interactions and behaviors. In this work, analyzing twelve organisms, we assess how fundamental measures in network theory can individuate seed-proteins and specific pathways across organisms. We employ network robustness to evaluate and compare the strength of the investigated PPI networks, and the structural controllability of complex networks to find and compare the sets of driver nodes necessary to control the overall networks. We find that network measures are related to phylogenetics, and advanced network methods can identify main pathways of significance in the complete Unfolded Protein Response mechanism.

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity. First, long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of Visual Short Term Memory (VSTM) tasks. The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects. In simulations, we show that this method accurately discriminates differences in noisy Event-Related Potentials (ERPs) between two conditions where standard connectivity and other comparable methods fail. We then apply this to analyse activity related to visual short-term memory binding deficits in two cohorts of familial and sporadic Alzheimer's disease. Reproducible significant differences were found in the binding task with no significant difference in the shape task in the P300 ERP range. This allows new sensitive measurements of transient functional connectivity, which can be implemented to obtain results of clinical significance., Comment: Published in Network Neuroscience (2024) by MIT Press

Degree heterogeneity and latent geometry, also referred to as popularity and similarity, are key explanatory components underlying the structure of real-world networks. The relationship between these components and the statistical complexity of networks is not well understood. We introduce a parsimonious normalised measure of statistical complexity for networks. The measure is trivially 0 in regular graphs and we prove that this measure tends to 0 in Erd\"os-R\'enyi random graphs in the thermodynamic limit. We go on to demonstrate that greater complexity arises from the combination of heterogeneous and geometric components to the network structure than either on their own. Further, the levels of complexity achieved are similar to those found in many real-world networks. However, we also find that real-world networks establish connections in a way which increases complexity and which our null models fail to explain. We study this using ten link growth mechanisms and find that only one mechanism successfully and consistently replicates this phenomenon -- probabilities proportional to the exponential of the number of common neighbours between two nodes. Common neighbours is a mechanism which implicitly accounts for degree heterogeneity and latent geometry. This explains how a simple mechanism facilitates the growth of statistical complexity in real-world networks., Comment: 20 pages, 6 figures

Collisionless shocks are fundamental processes that are ubiquitous in space plasma physics throughout the Heliosphere and most astrophysical environments. Earth's bow shock and interplanetary shocks at 1 AU offer the most readily accessible opportunities to advance our understanding of the nature of collisionless shocks via fully-instrumented, in situ observations. One major outstanding question pertains to the energy budget of collisionless shocks, particularly how exactly collisionless shocks convert incident kinetic bulk flow energy into thermalization (heating), suprathermal particle acceleration, and a variety of plasma waves, including nonlinear structures. Furthermore, it remains unknown how those energy conversion processes change for different shock orientations (e.g., quasi-parallel vs. quasi-perpendicular) and driving conditions (upstream Alfv\'enic and fast Mach numbers, plasma beta, etc.). Required to address these questions are multipoint observations enabling direct measurement of the necessary plasmas, energetic particles, and electric and magnetic fields and waves, all simultaneously from upstream, downstream, and at the shock transition layer with observatory separations at ion to magnetohydrodynamic (MHD) scales. Such a configuration of spacecraft with specifically-designed instruments has never been available, and this white paper describes a conceptual mission design -- MAKOS -- to address these outstanding questions and advance our knowledge of the nature of collisionless shocks., Comment: White paper submitted to the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033; 9 pages, 3 figures, 5 tables

Collisionless shock waves are one of the main forms of energy conversion in space plasmas. They can directly or indirectly drive other universal plasma processes such as magnetic reconnection, turbulence, particle acceleration and wave phenomena. Collisionless shocks employ a myriad of kinetic plasma mechanisms to convert the kinetic energy of supersonic flows in space to other forms of energy (e.g., thermal plasma, energetic particles, or Poynting flux) in order for the flow to pass an immovable obstacle. The partitioning of energy downstream of collisionless shocks is not well understood, nor are the processes which perform energy conversion. While we, as the heliophysics community, have collected an abundance of observations of the terrestrial bow shock, instrument and mission-level limitations have made it impossible to quantify this partition, to establish the physics within the shock layer responsible for it, and to understand its dependence on upstream conditions. This paper stresses the need for the first ever spacecraft mission specifically designed and dedicated to the observation of both the terrestrial bow shock as well as Interplanetary shocks in the solar wind., Comment: White paper submitted to the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033; 9 pages, 4 figures

Intel OptaneTM DC Persistent Memory resides on the memory bus and approaches DRAM in access latency. One avenue for its adoption is to employ it in place of persistent storage; another is to use it as a cheaper and denser extension of DRAM. In pursuit of the latter goal, we present the design of a volatile Optane NVRAM cache as a component in a storage engine underlying MongoDB. The primary innovation in our design is a new cache admission policy. We discover that on Optane NVRAM, known for its limited write throughput, the presence of writes disproportionately affects the throughput of reads, much more so than on DRAM. Therefore, an admission policy that indiscriminately admits new data (and thus generates writes), severely limits the rate of data retrieval and results in exceedingly poor performance for the cache overall. We design an admission policy that balances the rate of admission with the rate of lookups using dynamically observed characteristics of the workload. Our implementation outperforms OpenCAS (an off-the-shelf Optane-based block cache) in all cases, and Intel Memory Mode in cases where the database size exceeds the available NVRAM. Our cache is decoupled from the rest of the storage engine and uses generic metrics to guide its admission policy; therefore our design can be easily adopted in other systems.

Several diffuse interstellar bands (DIBs) have profiles with resolved sub-peaks that resemble rotational bands of large molecules. Analysis of these profiles can constrain the sizes and geometries of the DIB carriers, especially if the profiles exhibit clear variations along lines of sight probing different physical conditions. Using the extensive data set from the EDIBLES survey we searched for systematic variations in the peak-to-peak separation of these sub-peaks for the $\lambda\lambda$5797, 6379, and 6614 DIBs in lines of sight with a single dominant interstellar cloud. We used the spectra of twelve single-cloud sight lines to measure the peak-to-peak separation in the band profile substructures for these DIBs. We adopted the rotational contour formalism to infer the rotational constant for each DIB carrier and the rotational excitation temperature in the sight lines. We compared these to rotational constants for linear and spherical molecules to estimate the DIB carrier sizes. All three DIBs have peak separations that vary systematically between lines of sight, indicating correlated changes in the rotational excitation temperatures. We derived $B_{6614}$=$(22.2\pm8.9)\times 10^{-3}$ cm$^{-1}$, consistent with previous estimates. Assuming a similar rotational temperature for the $\lambda$6614 DIB carrier and assuming a linear carrier, we found B$_{5797}^{\rm linear}=(5.1\pm2.0)\times10^{-3}~{\rm cm}^{-1}$ and B$_{6379}^{\rm linear} =(2.3\pm0.9)\times10^{-3}~{\rm cm}^{-1}$. If the carriers of those DIBs however are spherical species, their rotational constants are half that value, $B_{5797}^{\rm spherical} = (2.6\pm1.0)\times10^{-3}~{\rm cm}^{-1}$ and $B_{6379}^{\rm spherical} = (1.1\pm0.4)\times10^{-3}~{\rm cm}^{-1}$. We estimate molecule sizes that range from 7--9 carbon atoms ($\lambda$6614 carrier, linear) to 77--114 carbon atoms ($\lambda$6379, spherical)., Comment: 21 pages, 56 figures. Accepted for publication in Astronomy & Astrophysics

Networks of disparate phenomena-- be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions-- exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Modelling based on this theory considerably outperforms popular power-law fitness and hyperbolic geometry explanations across 110 networks. Importantly, the degree distributions of the model resemble power-laws at small densities and log-normal distributions at larger densities, posing a reconciliatory solution to the long-standing debate on the nature and existence of scale-free networks. Validating this theory, a surface factor inversion approach on an economic world city network and an fMRI connectome results in considerably more geometrically aligned nearest neighbour networks. This establishes new foundations from which to understand, analyse, deconstruct and interpret network phenomena., Comment: 13 pages, 4 figures

Network topology is a fundamental aspect of network science that allows us to gather insights into the complicated relational architectures of the world we inhabit. We provide a first specific study of neighbourhood degree sequences in complex networks. We consider how to explicitly characterise important physical concepts such as similarity, heterogeneity and organisation in these sequences, as well as updating the notion of hierarchical complexity to reflect previously unnoticed organisational principles. We also point out that neighbourhood degree sequences are related to a powerful subtree kernel for unlabelled graph classification. We study these newly defined sequence properties in a comprehensive array of graph models and over 200 real-world networks. We find that these indices are neither highly correlated with each other nor with classical network indices. Importantly, the sequences of a wide variety of real world networks are found to have greater similarity and organisation than is expected for networks of their given degree distributions. Notably, while biological, social and technological networks all showed consistently large neighbourhood similarity and organisation, hierarchical complexity was not a consistent feature of real world networks. Neighbourhood degree sequences are an interesting tool for describing unique and important characteristics of complex networks., Comment: 11 pages, 5 figures

Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network science studies. The degree variance is an important metric for characterising network degree heterogeneity. Here, we provide an analytically valid normalisation of degree variance to replace previous normalisations which are either invalid or not applicable to all networks. It is shown that this normalisation provides equal values for graphs and their complements; it is maximal in the star graph (and its complement); and its expected value is constant with respect to density for Erd\"os-R\'enyi (ER) random graphs of the same size. We strengthen these results with model observations in ER random graphs, random geometric graphs, scale-free networks, random hierarchy networks and resting-state brain networks, showing that the proposed normalisation is generally less affected by both network size and density than previous normalisation attempts. The closed form expression proposed also benefits from high computational efficiency and straightforward mathematical analysis. Analysis of 184 real-world binary networks across different disciplines shows that normalised degree variance is not correlated with average degree and is robust to node and edge subsampling. Comparisons across subdomains of biological networks reveals greater degree heterogeneity among brain connectomes and food webs than in protein interaction networks., Comment: 16 pages, 6 figures

Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains and one of the Hyperledger projects hosted by the Linux Foundation (www.hyperledger.org). Fabric is the first truly extensible blockchain system for running distributed applications. It supports modular consensus protocols, which allows the system to be tailored to particular use cases and trust models. Fabric is also the first blockchain system that runs distributed applications written in standard, general-purpose programming languages, without systemic dependency on a native cryptocurrency. This stands in sharp contrast to existing blockchain platforms that require "smart-contracts" to be written in domain-specific languages or rely on a cryptocurrency. Fabric realizes the permissioned model using a portable notion of membership, which may be integrated with industry-standard identity management. To support such flexibility, Fabric introduces an entirely novel blockchain design and revamps the way blockchains cope with non-determinism, resource exhaustion, and performance attacks. This paper describes Fabric, its architecture, the rationale behind various design decisions, its most prominent implementation aspects, as well as its distributed application programming model. We further evaluate Fabric by implementing and benchmarking a Bitcoin-inspired digital currency. We show that Fabric achieves end-to-end throughput of more than 3500 transactions per second in certain popular deployment configurations, with sub-second latency, scaling well to over 100 peers., Comment: Appears in proceedings of EuroSys 2018 conference

The carriers of the diffuse interstellar bands (DIBs) are largely unidentified molecules ubiquitously present in the interstellar medium (ISM). After decades of study, two strong and possibly three weak near-infrared DIBs have recently been attributed to the C60+ fullerene based on observational and laboratory measurements. There is great promise for the identification of the over 400 other known DIBs, as this result could provide chemical hints towards other possible carriers. In an effort to systematically study the properties of the DIB carriers, we have initiated a new large-scale observational survey: the ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES). The main objective is to build on and extend existing DIB surveys to make a major step forward in characterising the physical and chemical conditions for a statistically significant sample of interstellar lines-of-sight, with the goal to reverse-engineer key molecular properties of the DIB carriers. EDIBLES is a filler Large Programme using the Ultraviolet and Visual Echelle Spectrograph at the Very Large Telescope at Paranal, Chile. It is designed to provide an observationally unbiased view of the presence and behaviour of the DIBs towards early-spectral-type stars whose lines-of-sight probe the diffuse-to-translucent ISM. Such a complete dataset will provide a deep census of the atomic and molecular content, physical conditions, chemical abundances and elemental depletion levels for each sightline. Achieving these goals requires a homogeneous set of high-quality data in terms of resolution (R ~ 70000 -- 100000), sensitivity (S/N up to 1000 per resolution element), and spectral coverage (305--1042 nm), as well as a large sample size (100+ sightlines). In this first paper the goals, objectives and methodology of the EDIBLES programme are described and an initial assessment of the data is provided., Comment: 29 pages (including 14 pages appendix), 15 figures, accepted by A&A

Shingled magnetic recording (SMR) increases the capacity of magnetic hard drives, but it requires that each zone of a disk be written sequentially and erased in bulk. This makes SMR a good fit for workloads dominated by large data objects with limited churn. To explore this possibility, we have developed SMORE, an object storage system designed to reliably and efficiently store large, seldom-changing data objects on an array of host-managed or host-aware SMR disks. SMORE uses a log-structured approach to accommodate the constraint that all writes to an SMR drive must be sequential within large shingled zones. It stripes data across zones on separate disks, using erasure coding to protect against drive failure. A separate garbage collection thread reclaims space by migrating live data out of the emptiest zones so that they can be trimmed and reused. An index stored on flash and backed up to the SMR drives maps object identifiers to on-disk locations. SMORE interleaves log records with object data within SMR zones to enable index recovery after a system crash (or failure of the flash device) without any additional logging mechanism. SMORE achieves full disk bandwidth when ingesting data---with a variety of object sizes---and when reading large objects. Read performance declines for smaller object sizes where inter- object seek time dominates. With a worst-case pattern of random deletions, SMORE has a write amplification (not counting RAID parity) of less than 2.0 at 80% occupancy. By taking an index snapshot every two hours, SMORE recovers from crashes in less than a minute. More frequent snapshots allow faster recovery., Comment: 13 pages, 8 figures, full version of 6 page paper published at MSST 2017

Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis. Essentially, graph-variate signal analysis leverages graphs of reliable connectivity information to filter instantaneous bivariate functions of the multivariate signal. This opens up a new and robust approach to analyse joint signal and network dynamics at sample resolution. Furthermore, our method can be formulated as instantaneous networks on which standard network analysis can be implemented. When graph connectivity is estimated from the multivariate signal itself, the appropriate consideration of instantaneous graph signal functions allows for a novel dynamic connectivity measure-- graphvariate dynamic (GVD) connectivity-- which is robust to spurious short-term dependencies. Particularly, we present appropriate functions for three pertinent connectivity metrics-- correlation, coherence and the phase-lag index. We show that our approach can determine signals with a single correlated couple against wholly uncorrelated data of up to 128 nodes in signal size (1 out of 8128 weighted edges). GVD connectivity is also shown to be more robust than i) other GSP approaches at detecting a randomly traveling spheroid on a 3D grid and ii) standard dynamic connectivity in determining differences in EEG restingstate and task-related activity. We also demonstrate its use in revealing hidden depth correlations from geophysical gamma ray data. We expect that the methods and framework presented will provide new approaches to data analysis in a variety of applied settings.

No. We prove that Erdos- Renyi Random Graphs are not topologically random., Comment: This paper has been withdrawn by the author due to known results. See B. Bollobas, The Automorphism Group, Chapter 9 of Random Graphs, Cambridge University Press, 2011

Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functional connectivity, we test the hypothesis that such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks.We find that the CST performs consistenty well in state-of-the-art modelling of EEG network topology, robustness to topological network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical complexity. This provides interesting new evidence into the relevance of considering a large number of edges in EEG functional connectivity research to provide informational density in the topology., Comment: Accepted for publication in PLOS One, 27th September 2017

Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained from encoding and maintenance periods of tasks performed by healthy young volunteers. We probe the task's transient physiological underpinnings by contrasting shape only (Shape) and shape-colour binding (Bind) conditions, displayed in the left and right sides of the screen, separately. Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision. We find that connectivity in the Bind condition is less integrated with the global network than in the Shape condition in occipital and frontal modules during the encoding period of the right screen condition. Using MDE we are able to discern driving effects in the occipital module between 100-140ms, coinciding with the P100 visually evoked potential, followed by a driving effect in the frontal module between 140-180ms, suggesting that the differences found constitute an information processing difference between these modules. This provides temporally precise information over a heterogeneous population in promising tasks for the detection of AD.

We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term Memory (VSTM) tasks which have proven useful in the assessment of clinical and preclinical Alzheimer's disease. We compare network analysis using Maximum Spanning Trees (MSTs) with network analysis obtained using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising method of network analysis negating the more classical use of thresholds which are so far chosen arbitrarily. However, we find that the threshold analyses outperforms MSTs for detection of functional network differences. Particularly, MSTs fail to find any significant differences. Further, the thresholds detect significant differences between shape and shape-colour binding tasks when these are tested in the left side of the display screen, but no such differences are detected when these tasks are tested for in the right side of the display screen. This provides evidence that contralateral activity is a significant factor in sensitivity for detection of cognitive task differences., Comment: Proceeds from the IEEE Engineering in Medicine and Biology Conference 2015

We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of `clustering' to `spanning' triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend. We compare results from an electroencephalogram data set of volunteers performing visual short term memory tasks of the CST alongside other thresholds, including maximum spanning trees. We find that the CST holds as a sensitive threshold for distinguishing differences in the functional connectivity between tasks. This provides a sensitive and objective method for setting a threshold on weighted complete networks which may prove influential on the future of functional connectivity research., Comment: conference paper from the IEEE Engineering in Medicine and Biology Conference 2015

Understanding the complex hierarchical topology of functional brain networks is a key aspect of functional connectivity research. Such topics are obscured by the widespread use of sparse binary network models which are fundamentally different to the complete weighted networks derived from functional connectivity. We introduce two techniques to probe the hierarchical complexity of topologies. Firstly, a new metric to measure hierarchical complexity; secondly, a Weighted Complex Hierarchy (WCH) model. To thoroughly evaluate our techniques, we generalise sparse binary network archetypes to weighted forms and explore the main topological features of brain networks- integration, regularity and modularity- using curves over density. By controlling the parameters of our model, the highest complexity is found to arise between a random topology and a strict 'class-based' topology. Further, the model has equivalent complexity to EEG phase-lag networks at peak performance. Hierarchical complexity attains greater magnitude and range of differences between different networks than the previous commonly used complexity metric and our WCH model offers a much broader range of network topology than the standard scale-free and small-world models at a full range of densities. Our metric and model provide a rigorous characterisation of hierarchical complexity. Importantly, our framework shows a scale of complexity arising between 'all nodes are equal' topologies at one extreme and 'strict class-based' topologies at the other., Comment: 12 pages, 7 figures, accepted for publication in Journal of Neuroscience Methods, 8/11/2016

We have conducted a high signal-to-noise spectroscopic survey of 670 nearby early-type stars, to map Diffuse Interstellar Band (DIB) absorption in and around the Local Bubble. The project started with a Southern hemisphere survey conducted at the European Southern Observatory's New Technology Telescope and has since been extended to an all-sky survey using the Isaac Newton Telescope. In this first paper in the series, we introduce the overall project and present the results from the Southern hemisphere survey. We make available a catalogue of equivalent-width measurements of the DIBs at 5780, 5797, 5850, 6196, 6203, 6270, 6283 \& 6614 \AA, the interstellar Na\,{\sc i} D lines at 5890 \& 5896 \AA, and the stellar He\,{\sc i} line at 5876 \AA. We find that the 5780 \AA\ DIB is relatively strong throughout, as compared to the 5797 \AA\ DIB, but especially within the Local Bubble and at the interface with more neutral medium. The 6203 \AA\ DIB shows a similar behaviour, but with respect to the 6196 \AA\ DIB. Some nearby stars show surprisingly strong DIBs whereas some distant stars show very weak DIBs, indicating small-scale structure within as well as outside the Local Bubble. The sight-lines with non-detections trace the extent of the Local Bubble especially clearly, and show it opening out into the Halo. The Local Bubble has a wall which is in contact with hot gas and/or a harsh interstellar radiation field. That wall is perforated though, causing leakage of radiation and possibly hot gas. On the other hand, compact self-shielded cloudlets are present much closer to the Sun, probably within the Local Bubble itself. As for the carriers of the DIBs, our observations confirm the notion that these are large molecules, whose differences in behaviour are mainly governed by their differing resilience and/or electrical charge, with more subtle differences possibly related to varying excitation., Comment: Accepted for publication in Astronomy & Astrophysics

The structure and properties of the diffuse interstellar medium (ISM) on small scales, sub-au to 1 pc, are poorly understood. We compare interstellar absorption-lines, observed towards a selection of O- and B-type stars at two or more epochs, to search for variations over time caused by the transverse motion of each star combined with changes in the structure in the foreground ISM. Two sets of data were used: 83 VLT- UVES spectra with approximately 6 yr between epochs and 21 McDonald observatory 2.7m telescope echelle spectra with 6 - 20 yr between epochs, over a range of scales from 0 - 360 au. The interstellar absorption-lines observed at the two epochs were subtracted and searched for any residuals due to changes in the foreground ISM. Of the 104 sightlines investigated with typically five or more components in Na I D, possible temporal variation was identified in five UVES spectra (six components), in Ca II, Ca I and/or Na I absorption-lines. The variations detected range from 7\% to a factor of 3.6 in column density. No variation was found in any other interstellar species. Most sightlines show no variation, with 3{\sigma} upper limits to changes of the order 0.1 - 0.3 dex in Ca II and Na I. These variations observed imply that fine-scale structure is present in the ISM, but at the resolution available in this study, is not very common at visible wavelengths. A determination of the electron densities and lower limits to the total number density of a sample of the sightlines implies that there is no striking difference between these parameters in sightlines with, and sightlines without, varying components., Comment: 19 pages, 11 figures, accepted for publication in MNRAS