A Call for Gene Expression Analysis in Whole Blood of Patients With Rheumatoid Arthritis (RA) as a Biomarker for RA-Associated Interstitial Lung Disease.

OBJECTIVE: Rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) is one of the most common and prognostic organ manifestations of RA. Therefore, to allow effective treatment, it is of crucial importance to diagnose RA-ILD at the earliest possible stage. So far, the gold standard of early detection has been high-resolution computed tomography (HRCT) of the lungs. This procedure involves considerable radiation exposure for the patient and is therefore unsuitable as a routine screening measure for ethical reasons. Here, we propose the analysis of characteristic gene expression patterns as a biomarker to aid in the early detection and initiation of appropriate, possibly antifibrotic, therapy. METHODS: To investigate unique molecular patterns of RA-ILD, whole blood samples were taken from 12 female patients with RA-ILD (n = 7) or RA (n = 5). The RNA was extracted, sequenced by RNA-Seq, and analyzed for characteristic differences in the gene expression patterns between patients with RA-ILD and those with RA without ILD. RESULTS: The differential gene expression analysis revealed 9 significantly upregulated genes in RA-ILD compared to RA without ILD: arginase 1 (ARG1), thymidylate synthetase (TYMS), sortilin 1 (SORT1), marker of proliferation Ki-67 (MKI67), olfactomedin 4 (OLFM4), baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5), membrane spanning 4-domains A4A (MS4A4A), C-type lectin domain family 12 member A (CLEC12A), and the long intergenic nonprotein coding RNA (LINC02967). CONCLUSION: All gene products of these genes (except for LINC02967) are known from the literature to be involved in the pathogenesis of fibrosis. Further, for some, a contribution to the development of pulmonary fibrosis has even been demonstrated in experimental studies. Therefore, the results presented here provide an encouraging perspective for using specific gene expression patterns as biomarkers for the early detection and differential diagnosis of RA-ILD as a routine screening test.

FIOLA: an accelerated pipeline for fluorescence imaging online analysis.

Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.

Rapid, real-time sucrase characterization: Showcasing the feasibility of a one-pot activity assay.

Sucrases can modify numerous carbohydrates, and short-chain oligosaccharides produced by the unique transfructosylation activity of levansucrases are promising candidates for the growing sugar substitute market. These compounds could counteract the increasing number of diseases associated with the consumption of high-calorie sugars. Thus, there is great interest in the characterization of novel levansucrases. The commonly used method for sucrase activity determination is to quantify d-glucose released in the sucrose-splitting reaction. This is usually done in a discontinuous mode, i.e., several samples taken from the sucrase reaction are applied to a separately performed d-glucose determination (e.g., GOPOD assay). Employing the newly isolated levansucrase LevSKK21 from Pseudomonas sp. KK21, the feasibility of a one-pot sucrase characterization was investigated by combining sucrase reaction and GOPOD-based d-glucose determination into a single, continuous assay (Real-time GOPOD). The enzyme was characterized with respect to kinetic parameters, ion dependency, pH value, and reaction temperature in a comparative approach employing Real-time GOPOD and HPLC. High data consistency for all investigated enzyme parameters demonstrated that current processes for sucrase characterization can be considerably accelerated by the continuous assay while maintaining data validity. However, the assay was not applicable at acidic pH, as decolorization of the quinoneimine dye formed during the GOPOD reaction was observed. Overall, the study presents valuable data on the potentials of real-time sucrase activity assessment for an accelerated discovery and characterization of interesting enzymes such as the hereby introduced levansucrase LevSKK21. Progress in sucrase discovery will finally foster the development of health-promoting sucrose substitutes.

Experimental Evaluation of IEEE 802.15.4z UWB Ranging Performance under Interference.

The rise of precise wireless localization for industrial and consumer use is continuing to challenge a significant amount of research. Recently the new ultra-wideband standard IEEE 802.15.4z was released to increase both the robustness and security of the underlying message exchanges. Due to the lack of accessible transceivers, most of the current research on this is of theoretical nature though. This work provides the first experimental evaluation of the ranging performance in realistic environments and also assesses the robustness to different sources of interference. To evaluate the individual aspects, a set of three different experiments are conducted. One experiment with realistic movement and two additional with targeted interference. It could be shown that the cryptographic additions of the new standard can provide sufficient information to improve the reliability of the ranging results under multi-user interference significantly.

VolPy: Automated and scalable analysis pipelines for voltage imaging datasets.

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy's performance in spike extraction and scalability are state-of-the-art.

Online analysis of microendoscopic 1-photon calcium imaging data streams.

In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop.

Bright and photostable chemigenetic indicators for extended in vivo voltage imaging.

Genetically encoded voltage indicators (GEVIs) enable monitoring of neuronal activity at high spatial and temporal resolution. However, the utility of existing GEVIs has been limited by the brightness and photostability of fluorescent proteins and rhodopsins. We engineered a GEVI, called Voltron, that uses bright and photostable synthetic dyes instead of protein-based fluorophores, thereby extending the number of neurons imaged simultaneously in vivo by a factor of 10 and enabling imaging for significantly longer durations relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In the mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously over a 15-minute period of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

CaImAn an open source tool for scalable calcium imaging data analysis.

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

Host-feeding patterns of Culex mosquitoes in Iran.

BACKGROUND: Different mosquito-borne pathogens are circulating in Iran including Sindbis virus, West Nile virus, filarioid worms and malaria parasites. However, the local transmission cycles of these pathogenic agents are poorly understood, especially because ecological data on vector species are scarce and there is limited knowledge about the host range; this understanding could help to direct species-specific vector control measurements or to prioritize research. METHODS: In the summers of 2015 and 2016, blood-fed mosquitoes were collected at 13 trapping sites on the coast of the Caspian Sea in northern Iran and at an additional trapping site in western Iran. Mosquitoes were generally collected with either a Biogents Sentinel trap or a Heavy Duty Encephalitis Vector Survey trap installed outside. A handheld aspirator was used at the trapping site in western Iran, in addition to a few samplings around the other trapping sites. On average, eight trapping periods were conducted per trapping site. The sources of blood meals were identified using a DNA barcoding approach targeting the cytochrome b or 16S rRNA gene fragment. RESULTS: The source of blood meals for 580 blood-fed mosquito specimens of 20 different taxa were determined, resulting in the identification of 13 different host species (9 mammals including humans, 3 birds and 1 reptile), whereby no mixed blood meals were detected. Five mosquito species represented more than 85.8% of all collected blood-fed specimens: Culex pipiens pipiens form pipiens (305 specimens, 55.7% of all mosquito specimens), Cx. theileri (60, 10.9%), Cx. sitiens (51, 9.3%), Cx. perexiguus (29, 5.3%) and Anopheles superpictus (25, 4.6%). The most commonly detected hosts of the four most abundant mosquito species were humans (Homo sapiens; 224 mosquito specimens, 40.9% of all mosquito specimens), cattle (Bos taurus; 171, 31.2%) and ducks (Anas spp.; 75, 13.7%). These four mosquito species had similar host-feeding patterns. The only exceptions were a relatively high proportion of birds for Cx. pipiens pipiens f. pipiens (23.2% of detected blood meal sources) and a high proportion of non-human mammals for Cx. theileri (73.4%). Trapping month, surrounding area, or trapping method had no statistically significant impact on the observed host-feeding patterns of Cx. pipiens pipiens f. pipiens. CONCLUSIONS: Due to the diverse and overlapping host-feeding patterns, several mosquito species must be considered as potential enzootic and bridge vectors for diverse mosquito-borne pathogens in Iran. Most species can potentially transmit pathogens between mammals as well as between mammals and birds, which might be the result of a similar host selection or a high dependence on the host availability.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data.

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

Multi-scale approaches for high-speed imaging and analysis of large neural populations.

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

Fast online deconvolution of calcium imaging data.

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O(105) traces of whole-brain larval zebrafish imaging data on a laptop.

Goal-Directed Decision Making with Spiking Neurons.

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT: Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level.

Human and machine learning in non-Markovian decision making.

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision making, where the next state depends on more than the current state and action. Learning is non-Markovian, for example, when there is no unique mapping between actions and feedback. We have produced a model based on spiking neurons that can handle these non-Markovian conditions by performing policy gradient descent [1]. Here, we examine the model's performance and compare it with human learning and a Bayes optimal reference, which provides an upper-bound on performance. We find that in all cases, our population of spiking neurons model well-describes human performance.

Code-specific learning rules improve action selection by populations of spiking neurons.

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Spike-based decision learning of Nash equilibria in two-player games.

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Rigidity evaluation of quartz-fiber splints compared with wire-composite splints.

AIM: To evaluate the influence of reinforcement material on in vitro dental splint rigidity. MATERIALS AND METHODS: A custom-made artificial model was used. The central incisors simulated 'injured' teeth with increased mobility, and the lateral incisors served as 'uninjured' teeth with physiologic mobility. The Periotest and Zwick methods were used to assess horizontal and vertical tooth mobility before and after splinting, and relative splint effect (SpErel) was calculated. Teeth 12-22 were splinted using two wire-composite splints (WCS), WCS1 (Dentaflex 0.45mm), and WCS2 (Strengtheners 0.8×1.8mm) as well as four quartz-fiber splints, QS1 (Quartz Splint UD 1.5mm), QS2 (Quartz Splint Rope 1.5mm), QS3 (Quartz Splint Woven 2.5mm), and QS4 (dry fibers 667 tex). The influence of the splint type was evaluated using anova, Tukey range, and the Dunnett-T3 test (α=0.05). To test the influence of initial tooth mobility, the t-test was applied (α=0.05). RESULTS: Reinforcement materials significantly influenced splint rigidity (P<0.05). The horizontal and vertical SpErel of WCS1 compared with WCS2 and QFSs1-4 was statistically significant (P<0.05). Significant differences were found when comparing the horizontal SpErel of WCS2 with WCS1 and QSs1-4 (P<0.05). SpErels of the 'injured' and 'uninjured' teeth showed significant differences (P<0.05). CONCLUSION: WCS1 is flexible compared with the more rigid WCS2 and QSs1-4. Initial tooth mobility influences SpErel. The flexible WCS1 can be recommended for splinting dislocation injuries whereas the semi-rigid/rigid WCS2 and QS1-4 can be used for horizontal root fractures and alveolar process fractures. The QS1-4 provide good esthetic outcome.

Spatio-temporal credit assignment in neuronal population learning.

In learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.