Consciousness is supported by near-critical slow cortical electrodynamics.

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.

How inhibitory circuits in the thalamus serve vision.

Inhibitory neurons dominate the intrinsic circuits in the visual thalamus. Interneurons in the lateral geniculate nucleus innervate relay cells and each other densely to provide powerful inhibition. The visual sector of the overlying thalamic reticular nucleus receives input from relay cells and supplies feedback inhibition to them in return. Together, these two inhibitory circuits influence all information transmitted from the retina to the primary visual cortex. By contrast, relay cells make few local connections. This review explores the role of thalamic inhibition from the dual perspectives of feature detection and information theory. For example, we describe how inhibition sharpens tuning for spatial and temporal features of the stimulus and how it might enhance image perception. We also discuss how inhibitory circuits help to reduce redundancy in signals sent downstream and, at the same time, are adapted to maximize the amount of information conveyed to the cortex.

Efficient Decoding of Compositional Structure in Holistic Representations.

We investigate the task of retrieving information from compositional distributed representations formed by hyperdimensional computing/vector symbolic architectures and present novel techniques that achieve new information rate bounds. First, we provide an overview of the decoding techniques that can be used to approach the retrieval task. The techniques are categorized into four groups. We then evaluate the considered techniques in several settings that involve, for example, inclusion of external noise and storage elements with reduced precision. In particular, we find that the decoding techniques from the sparse coding and compressed sensing literature (rarely used for hyperdimensional computing/vector symbolic architectures) are also well suited for decoding information from the compositional distributed representations. Combining these decoding techniques with interference cancellation ideas from communications improves previously reported bounds (Hersche et al., 2021) of the information rate of the distributed representations from 1.20 to 1.40 bits per dimension for smaller codebooks and from 0.60 to 1.26 bits per dimension for larger codebooks.

Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching.

Energy-based models (EBMs) assign an unnormalized log probability to data samples. This functionality has a variety of applications, such as sample synthesis, data denoising, sample restoration, outlier detection, Bayesian reasoning and many more. But, the training of EBMs using standard maximum likelihood is extremely slow because it requires sampling from the model distribution. Score matching potentially alleviates this problem. In particular, denoising-score matching has been successfully used to train EBMs. Using noisy data samples with one fixed noise level, these models learn fast and yield good results in data denoising. However, demonstrations of such models in the high-quality sample synthesis of high-dimensional data were lacking. Recently, a paper showed that a generative model trained by denoising-score matching accomplishes excellent sample synthesis when trained with data samples corrupted with multiple levels of noise. Here we provide an analysis and empirical evidence showing that training with multiple noise levels is necessary when the data dimension is high. Leveraging this insight, we propose a novel EBM trained with multiscale denoising-score matching. Our model exhibits a data-generation performance comparable to state-of-the-art techniques such as GANs and sets a new baseline for EBMs. The proposed model also provides density information and performs well on an image-inpainting task.

Vector Symbolic Architectures as a Computing Framework for Emerging Hardware.

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.

Robust computation with rhythmic spike patterns.

Information coding by precise timing of spikes can be faster and more energy efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limited their use in theoretical neuroscience and computing applications. Here, we propose a type of attractor neural network in complex state space and show how it can be leveraged to construct spiking neural networks with robust computational properties through a phase-to-timing mapping. Building on Hebbian neural associative memories, like Hopfield networks, we first propose threshold phasor associative memory (TPAM) networks. Complex phasor patterns whose components can assume continuous-valued phase angles and binary magnitudes can be stored and retrieved as stable fixed points in the network dynamics. TPAM achieves high memory capacity when storing sparse phasor patterns, and we derive the energy function that governs its fixed-point attractor dynamics. Second, we construct 2 spiking neural networks to approximate the complex algebraic computations in TPAM, a reductionist model with resonate-and-fire neurons and a biologically plausible network of integrate-and-fire neurons with synaptic delays and recurrently connected inhibitory interneurons. The fixed points of TPAM correspond to stable periodic states of precisely timed spiking activity that are robust to perturbation. The link established between rhythmic firing patterns and complex attractor dynamics has implications for the interpretation of spike patterns seen in neuroscience and can serve as a framework for computation in emerging neuromorphic devices.

Visual Information Processing in the Ventral Division of the Mouse Lateral Geniculate Nucleus of the Thalamus.

Even though the lateral geniculate nucleus of the thalamus (LGN) is associated with form vision, that is not its sole role. Only the dorsal portion of LGN (dLGN) projects to V1. The ventral division (vLGN) connects subcortically, sending inhibitory projections to sensorimotor structures, including the superior colliculus (SC) and regions associated with certain behavioral states, such as fear (Monavarfeshani et al., 2017; Salay et al., 2018). We combined computational, physiological, and anatomical approaches to explore visual processing in vLGN of mice of both sexes, making comparisons to dLGN and SC for perspective. Compatible with past, qualitative descriptions, the receptive fields we quantified in vLGN were larger than those in dLGN, and most cells preferred bright versus dark stimuli (Harrington, 1997). Dendritic arbors spanned the length and/or width of vLGN and were often asymmetric, positioned to collect input from large but discrete territories. By contrast, arbors in dLGN are compact (Krahe et al., 2011). Consistent with spatially coarse receptive fields in vLGN, visually evoked changes in spike timing were less precise than for dLGN and SC. Notably, however, the membrane currents and spikes of some cells in vLGN displayed gamma oscillations whose phase and strength varied with stimulus pattern, as for SC (Stitt et al., 2013). Thus, vLGN can engage its targets using oscillation-based and conventional rate codes. Finally, dark shadows activate SC and drive escape responses, whereas vLGN prefers bright stimuli. Thus, one function of long-range inhibitory projections from vLGN might be to enable movement by releasing motor targets, such as SC, from suppression.SIGNIFICANCE STATEMENT Only the dorsal lateral geniculate nucleus (dLGN) connects to cortex to serve form vision; the ventral division (vLGN) projects subcortically to sensorimotor nuclei, including the superior colliculus (SC), via long-range inhibitory connections. Here, we asked how vLGN processes visual information, making comparisons with dLGN and SC for perspective. Cells in vLGN versus dLGN had wider dendritic arbors, larger receptive fields, and fired with lower temporal precision, consistent with a modulatory role. Like SC, but not dLGN, visual stimuli entrained oscillations in vLGN, perhaps reflecting shared strategies for visuomotor processing. Finally, most neurons in vLGN preferred bright shapes, whereas dark stimuli activate SC and drive escape behaviors, suggesting that vLGN enables rapid movement by releasing target motor structures from inhibition.

A Neural Network MCMC Sampler That Maximizes Proposal Entropy.

Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probability distributions and offer guarantees of exact sampling. However, in the continuous case, unfavorable geometry of the target distribution can greatly limit the efficiency of MCMC methods. Augmenting samplers with neural networks can potentially improve their efficiency. Previous neural network-based samplers were trained with objectives that either did not explicitly encourage exploration, or contained a term that encouraged exploration but only for well structured distributions. Here we propose to maximize proposal entropy for adapting the proposal to distributions of any shape. To optimize proposal entropy directly, we devised a neural network MCMC sampler that has a flexible and tractable proposal distribution. Specifically, our network architecture utilizes the gradient of the target distribution for generating proposals. Our model achieved significantly higher efficiency than previous neural network MCMC techniques in a variety of sampling tasks, sometimes by more than an order magnitude. Further, the sampler was demonstrated through the training of a convergent energy-based model of natural images. The adaptive sampler achieved unbiased sampling with significantly higher proposal entropy than a Langevin dynamics sample. The trained sampler also achieved better sample quality.

Central blockage of nesfatin-1 has anxiolytic effects but does not prevent corticotropin-releasing factor-induced anxiety in male rats.

Nesfatin-1, a pleotropic peptide, was recently implicated in the regulation of anxiety and depression-like behavior in rats. However, the underlying mechanisms remain unclear so far. Thus, this study aimed to investigate the role of endogenous nesfatin-1 in the mediation of anxiety and depression-like behavior induced by corticotropin-releasing factor (CRF). Therefore, normal weight male intracerebroventricularly (icv) cannulated Sprague Dawley rats received two consecutive icv injections of anti-nesfatin-1 antibody or IgG control antibody followed by CRF or saline, before being exposed to a behavioral test. In the elevated zero maze test, assessing anxiety and explorative behavior, blockade of nesfatin-1 using an anti-nesfatin-1 antibody under basal conditions increased the number of entries into the open arms compared to control antibody/vehicle (1.6-fold, p < 0.05) and the time in open arms compared to the other groups (p < 0.05). Control antibody/CRF-treated animals tended to spend less time in the open arms compared to control antibody/vehicle (0.7-fold, p = 0.17), an effect not altered by the nesfatin-1 antibody (control antibody/CRF-treated animals vs. nesfatin-1 antibody/CRF group, p = 1.00). In the novelty-induced hypophagia test, assessing anhedonia as part of depression-like behavior, no significant differences were observed between the four groups for the latency to the first bout, number of bouts and the amount of palatable snack eaten (p > 0.05). In summary, CRF tended to increase anxiety and explorative behavior an effect not altered by blockade of nesfatin-1, whereas no significant effect of CRF on anhedonia was observed. Blockade of endogenous nesfatin-1 significantly decreased anxiety-like behavior giving rise to a physiological role of brain nesfatin-1 in the mediation of anxiety.

Resonator Networks, 2: Factorization Performance and Capacity Compared to Optimization-Based Methods.

We develop theoretical foundations of resonator networks, a new type of recurrent neural network introduced in Frady, Kent, Olshausen, and Sommer (2020), a companion article in this issue, to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures. Given a composite vector formed by the Hadamard product between a discrete set of high-dimensional vectors, a resonator network can efficiently decompose the composite into these factors. We compare the performance of resonator networks against optimization-based methods, including Alternating Least Squares and several gradient-based algorithms, showing that resonator networks are superior in several important ways. This advantage is achieved by leveraging a combination of nonlinear dynamics and searching in superposition, by which estimates of the correct solution are formed from a weighted superposition of all possible solutions. While the alternative methods also search in superposition, the dynamics of resonator networks allow them to strike a more effective balance between exploring the solution space and exploiting local information to drive the network toward probable solutions. Resonator networks are not guaranteed to converge, but within a particular regime they almost always do. In exchange for relaxing the guarantee of global convergence, resonator networks are dramatically more effective at finding factorizations than all alternative approaches considered.

Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures.

The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporting rule-based symbolic reasoning, a central property of cognition. Here we show how this may be accomplished within the framework of Vector Symbolic Architectures (VSAs) (Plate, 1991; Gayler, 1998; Kanerva, 1996), whereby data structures are encoded by combining high-dimensional vectors with operations that together form an algebra on the space of distributed representations. In particular, we propose an efficient solution to a hard combinatorial search problem that arises when decoding elements of a VSA data structure: the factorization of products of multiple codevectors. Our proposed algorithm, called a resonator network, is a new type of recurrent neural network that interleaves VSA multiplication operations and pattern completion. We show in two examples-parsing of a tree-like data structure and parsing of a visual scene-how the factorization problem arises and how the resonator network can solve it. More broadly, resonator networks open the possibility of applying VSAs to myriad artificial intelligence problems in real-world domains. The companion article in this issue (Kent, Frady, Sommer, & Olshausen, 2020) presents a rigorous analysis and evaluation of the performance of resonator networks, showing it outperforms alternative approaches.

Information integration in large brain networks.

An outstanding problem in neuroscience is to understand how information is integrated across the many modules of the brain. While classic information-theoretic measures have transformed our understanding of feedforward information processing in the brain's sensory periphery, comparable measures for information flow in the massively recurrent networks of the rest of the brain have been lacking. To address this, recent work in information theory has produced a sound measure of network-wide "integrated information", which can be estimated from time-series data. But, a computational hurdle has stymied attempts to measure large-scale information integration in real brains. Specifically, the measurement of integrated information involves a combinatorial search for the informational "weakest link" of a network, a process whose computation time explodes super-exponentially with network size. Here, we show that spectral clustering, applied on the correlation matrix of time-series data, provides an approximate but robust solution to the search for the informational weakest link of large networks. This reduces the computation time for integrated information in large systems from longer than the lifespan of the universe to just minutes. We evaluate this solution in brain-like systems of coupled oscillators as well as in high-density electrocortigraphy data from two macaque monkeys, and show that the informational "weakest link" of the monkey cortex splits posterior sensory areas from anterior association areas. Finally, we use our solution to provide evidence in support of the long-standing hypothesis that information integration is maximized by networks with a high global efficiency, and that modular network structures promote the segregation of information.

Millennial-scale variability in Antarctic ice-sheet discharge during the last deglaciation.

Our understanding of the deglacial evolution of the Antarctic Ice Sheet (AIS) following the Last Glacial Maximum (26,000-19,000 years ago) is based largely on a few well-dated but temporally and geographically restricted terrestrial and shallow-marine sequences. This sparseness limits our understanding of the dominant feedbacks between the AIS, Southern Hemisphere climate and global sea level. Marine records of iceberg-rafted debris (IBRD) provide a nearly continuous signal of ice-sheet dynamics and variability. IBRD records from the North Atlantic Ocean have been widely used to reconstruct variability in Northern Hemisphere ice sheets, but comparable records from the Southern Ocean of the AIS are lacking because of the low resolution and large dating uncertainties in existing sediment cores. Here we present two well-dated, high-resolution IBRD records that capture a spatially integrated signal of AIS variability during the last deglaciation. We document eight events of increased iceberg flux from various parts of the AIS between 20,000 and 9,000 years ago, in marked contrast to previous scenarios which identified the main AIS retreat as occurring after meltwater pulse 1A and continuing into the late Holocene epoch. The highest IBRD flux occurred 14,600 years ago, providing the first direct evidence for an Antarctic contribution to meltwater pulse 1A. Climate model simulations with AIS freshwater forcing identify a positive feedback between poleward transport of Circumpolar Deep Water, subsurface warming and AIS melt, suggesting that small perturbations to the ice sheet can be substantially enhanced, providing a possible mechanism for rapid sea-level rise.

A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks.

To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA) and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy, and crosstalk noise limits the retrieval accuracy and information capacity. A novel theory to optimize memory performance in such networks is presented and compared with simulation experiments. The theory describes linear readout of analog data and readout with winner-take-all error correction of symbolic data as proposed in VSA models. We find that diverse VSA models from the literature have universal performance properties, which are superior to what previous analyses predicted. Further, we propose novel VSA models with the statistically optimal Wiener filter in the readout that exhibit much higher information capacity, in particular for storing analog data. The theory we present also applies to memory buffers, networks with gradual forgetting, which can operate on infinite data streams without memory overflow. Interestingly, we find that different forgetting mechanisms, such as attenuating recurrent weights or neural nonlinearities, produce very similar behavior if the forgetting time constants are matched. Such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size. These results enable the design of new types of VSA models for the online processing of data streams.

Synaptic Contributions to Receptive Field Structure and Response Properties in the Rodent Lateral Geniculate Nucleus of the Thalamus.

Comparative physiological and anatomical studies have greatly advanced our understanding of sensory systems. Many lines of evidence show that the murine lateral geniculate nucleus (LGN) has unique attributes, compared with other species such as cat and monkey. For example, in rodent, thalamic receptive field structure is markedly diverse, and many cells are sensitive to stimulus orientation and direction. To explore shared and different strategies of synaptic integration across species, we made whole-cell recordings in vivo from the murine LGN during the presentation of visual stimuli, analyzed the results with different computational approaches, and compared our findings with those from cat. As for carnivores, murine cells with classical center-surround receptive fields had a "push-pull" structure of excitation and inhibition within a given On or Off subregion. These cells compose the largest single population in the murine LGN (∼40%), indicating that push-pull is key in the form vision pathway across species. For two cell types with overlapping On and Off responses, which recalled either W3 or suppressed-by-contrast ganglion cells in murine retina, inhibition took a different form and was most pronounced for spatially extensive stimuli. Other On-Off cells were selective for stimulus orientation and direction. In these cases, retinal inputs were tuned and, for oriented cells, the second-order subunit of the receptive field predicted the preferred angle. By contrast, suppression was not tuned and appeared to sharpen stimulus selectivity. Together, our results provide new perspectives on the role of excitation and inhibition in retinothalamic processing. SIGNIFICANCE STATEMENT: We explored the murine lateral geniculate nucleus from a comparative physiological perspective. In cat, most retinal cells have center-surround receptive fields and push-pull excitation and inhibition, including neurons with the smallest (highest acuity) receptive fields. The same is true for thalamic relay cells. In mouse retina, the most numerous cell type has the smallest receptive fields but lacks push-pull. The most common receptive field in rodent thalamus, however, is center-surround with push-pull. Thus, receptive field structure supersedes size per se for form vision. Further, for many orientation-selective cells, the second-order component of the receptive field aligned with stimulus preference, whereas suppression was untuned. Thus, inhibition may improve spatial resolution and sharpen other forms of selectivity in rodent lateral geniculate nucleus.

Did online publishers "get it right"? Using a naturalistic search strategy to review cognitive health promotion content on internet webpages.

BACKGROUND: One of the most common uses of the Internet is to search for health-related information. Although scientific evidence pertaining to cognitive health promotion has expanded rapidly in recent years, it is unclear how much of this information has been made available to Internet users. Thus, the purpose of our study was to assess the reliability and quality of information about cognitive health promotion encountered by typical Internet users. METHODS: To generate a list of relevant search terms employed by Internet users, we entered seed search terms in Google Trends and recorded any terms consistently used in the prior 2 years. To further approximate the behaviour of typical Internet users, we entered each term in Google and sampled the first two relevant results. This search, completed in October 2014, resulted in a sample of 86 webpages, 48 of which had content related to cognitive health promotion. An interdisciplinary team rated the information reliability and quality of these webpages using a standardized measure. RESULTS: We found that information reliability and quality were moderate, on average. Just one retrieved page mentioned best practice, national recommendations, or consensus guidelines by name. Commercial content (i.e., product promotion, advertising content, or non-commercial) was associated with differences in reliability and quality, with product promoter webpages having the lowest mean reliability and quality ratings. CONCLUSIONS: As efforts to communicate the association between lifestyle and cognitive health continue to expand, we offer these results as a baseline assessment of the reliability and quality of cognitive health promotion on the Internet.

A comparative study of three signaling forms of the orange carotenoid protein.

Orange carotenoid protein (OCP) is a water-soluble photoactive protein responsible for a photoprotective mechanism of nonphotochemical quenching in cyanobacteria. Under blue-green illumination, OCP converts from the stable orange into the signaling red quenching form; however, the latter form could also be obtained by chemical activation with high concentrations of sodium thiocyanate (NaSCN) or point mutations. In this work, we show that a single replacement of tryptophan-288, normally involved in protein-chromophore interactions, by alanine, results in formation of a new protein form, hereinafter referred to as purple carotenoid protein (PCP). Comparison of resonance Raman spectra of the native photoactivated red form, chemically activated OCP, and PCP reveals that carotenoid conformation is sensitive to the structure of the C-domain, implicating that the chromophore retains some interactions with this part of the protein in the active red form. Combination of differential scanning fluorimetry and picosecond time-resolved fluorescence anisotropy measurements allowed us to compare the stability of different OCP forms and to estimate relative differences in protein rotation rates. These results were corroborated by hydrodynamic analysis of proteins by dynamic light scattering and analytical size-exclusion chromatography, indicating that the light-induced conversion of the protein is accompanied by a significant increase in its size. On the whole, our data support the idea that the red form of OCP is a molten globule-like protein in which, however, interactions between the carotenoid and the C-terminal domain are preserved.

The time course of non-photochemical quenching in phycobilisomes of Synechocystis sp. PCC6803 as revealed by picosecond time-resolved fluorimetry.

As high-intensity solar radiation can lead to extensive damage of the photosynthetic apparatus, cyanobacteria have developed various protection mechanisms to reduce the effective excitation energy transfer (EET) from the antenna complexes to the reaction center. One of them is non-photochemical quenching (NPQ) of the phycobilisome (PB) fluorescence. In Synechocystis sp. PCC6803 this role is carried by the orange carotenoid protein (OCP), which reacts to high-intensity light by a series of conformational changes, enabling the binding of OCP to the PBs reducing the flow of energy into the photosystems. In this paper the mechanisms of energy migration in two mutant PB complexes of Synechocystis sp. were investigated and compared. The mutant CK is lacking phycocyanin in the PBs while the mutant ΔPSI/PSII does not contain both photosystems. Fluorescence decay spectra with picosecond time resolution were registered using a single photon counting technique. The studies were performed in a wide range of temperatures - from 4 to 300 K. The time course of NPQ and fluorescence recovery in darkness was studied at room temperature using both steady-state and time-resolved fluorescence measurements. The OCP induced NPQ has been shown to be due to EET from PB cores to the red form of OCP under photon flux densities up to 1000 µmolphotonsm⁻²s⁻¹. The gradual changes of the energy transfer rate from allophycocyanin to OCP were observed during the irradiation of the sample with blue light and consequent adaptation to darkness. This fact was interpreted as the revelation of intermolecular interaction between OCP and PB binding site. At low temperatures a significantly enhanced EET from allophycocyanin to terminal emitters has been shown, due to the decreased back transfer from terminal emitter to APC. The activation of OCP not only leads to fluorescence quenching, but also affects the rate constants of energy transfer as shown by model based analysis of the decay associated spectra. The results indicate that the ability of OCP to quench the fluorescence is strongly temperature dependent. This article is part of a special issue entitled: photosynthesis research for sustainability: keys to produce clean energy.

Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus.

AIMS: Early detection of individuals with Type 2 diabetes mellitus or hypertension at risk for micro- or macroalbuminuria may facilitate prevention and treatment of renal disease. We aimed to discover plasma and urine metabolites that predict the development of micro- or macroalbuminuria. METHODS: Patients with Type 2 diabetes (n = 90) and hypertension (n = 150) were selected from the community-cohort 'Prevention of REnal and Vascular End-stage Disease' (PREVEND) and the Steno Diabetes Center for this case-control study. Cases transitioned in albuminuria stage (from normo- to microalbuminuria or micro- to macroalbuminuria). Controls, matched for age, gender, and baseline albuminuria stage, remained in normo- or microalbuminuria stage during follow-up. Median follow-up was 2.9 years. Metabolomics were performed on plasma and urine. The predictive performance of a metabolite for albuminuria transition was assessed by the integrated discrimination index. RESULTS: In patients with Type 2 diabetes with normoalbuminuria, no metabolites discriminated cases from controls. In patients with Type 2 diabetes with microalbuminuria, plasma histidine was lower (fold change = 0.87, P = 0.02) and butenoylcarnitine was higher (fold change = 1.17, P = 0.007) in cases vs. controls. In urine, hexose, glutamine and tyrosine were lower in cases vs. controls (fold change = 0.20, P < 0.001; 0.32, P < 0.001; 0.51, P = 0.006, respectively). Adding the metabolites to a model of baseline albuminuria and estimated glomerular filtration rate metabolites improved risk prediction for macroalbuminuria transition (plasma integrated discrimination index = 0.28, P < 0.001; urine integrated discrimination index = 0.43, P < 0.001). These metabolites did not differ between hypertensive cases and controls without Type 2 diabetes. CONCLUSIONS: Type 2 diabetes-specific plasma and urine metabolites were discovered that predict the development of macroalbuminuria beyond established renal risk markers. These results should be confirmed in a large, prospective cohort.

Spectral composition of secondary electrons based on the Kiefer-Straaten ion track structure model.

The major part of energy deposition of ionizing radiation is caused by secondary electrons, independent of the primary radiation type. However, their spatial concentration and their spectral properties strongly depend on the primary radiation type and finally determine the pattern of molecular damage e.g. to biological targets as the DNA, and thus the final effect of the radiation exposure. To describe the physical and to predict the biological consequences of charged ion irradiation, amorphous track structure approaches have proven to be pragmatic and helpful. There, the local dose deposition in the ion track is equated by considering the emission and slowing down of the secondary electrons from the primary particle track. In the present work we exploit the model of Kiefer and Straaten and derive the spectral composition of secondary electrons as function of the distance to the track center. The spectral composition indicates differences to spectra of low linear energy transfer (LET) photon radiation, which we confirm by a comparison with Monte Carlo studies. We demonstrate that the amorphous track structure approach provides a simple tool for evaluating the spectral electron properties within the track structure. Predictions of the LET of electrons across the track structure as well as the electronic dose build-up effect are derived. Implications for biological effects and corresponding predicting models based on amorphous track structure are discussed.