AßPP-tau-HAS1 axis trigger HAS1-related nuclear speckles and gene transcription in Alzheimer's disease.

As the backbone of the extracellular matrix (ECM) and the perineuronal nets (PNNs), hyaluronic acid (HA) provides binding sites for proteoglycans and other ECM components. Although the pivotal of HA has been recognized in Alzheimer's disease (AD), few studies have addressed the relationship between AD pathology and HA synthases (HASs). Here, HASs in different regions of AD brains were screened in transcriptomic database and validated in AßPP/PS1 mice. We found that HAS1 was distributed along the axon and nucleus. Its transcripts were reduced in AD patients and AßPP/PS1 mice. Phosphorylated tau (p-tau) mediates AßPP-induced cytosolic-nuclear translocation of HAS1, and negatively regulated the stability, monoubiquitination, and oligomerization of HAS1, thus reduced the synthesis and release of HA. Furthermore, non-ubiquitinated HAS1 mutant lost its enzyme activity, and translocated from the cytosol into the nucleus, forming nuclear speckles (NS). Unlike the splicing-related NS, less than 1 % of the non-ubiquitinated HAS1 co-localized with SRRM2, proving the regulatory role of HAS1 in gene transcription, indirectly. Thus, differentially expressed genes (DEGs) related to both non-ubiquitinated HAS1 mutant and AD were screened using transcriptomic datasets. Thirty-nine DEGs were identified, with 64.1 % (25/39) showing consistent results in both datasets. Together, we unearthed an important function of the AßPP-p-tau-HAS1 axis in microenvironment remodeling and gene transcription during AD progression, involving the ubiquitin-proteasome, lysosome, and NS systems.

A "Bandwidth" in cortical representations of multiple faces.

The human ability to process multiple items simultaneously can be constrained by the extent to which those items are represented by distinct neural populations. In the current study, we used fMRI to investigate the cortical representation of multiple faces. We found that the addition of a second face to occupy both visual hemifields led to an increased response, whereas a further addition of faces within the same visual hemifield resulted in a decreased response. This pattern was widely observed in the occipital visual cortex, the intraparietal sulcus, and extended to the posterior inferotemporal cortex. A parallel trend was found in a behavioral change-detection task, revealing a perceptual "bandwidth" of multiface processing. The sensitivity to face clutter gradually decreased along the ventral pathway, supporting the notion of a buildup of clutter-tolerance representation. These cortical response patterns to face clutters suggest that adding signals with nonoverlapping cortical representation enhanced perception, while adding signals that competed for representation resources impaired perception.

Stable-Isotope Probing Reveals the Activity and Function of Autotrophic and Heterotrophic Denitrifiers in Nitrate Removal from Organic-Limited Wastewater.

Combined heterotrophic and autotrophic denitrification (HAD) is a sustainable and practical method for removing nitrate from organic-limited wastewater. However, the active microorganisms responsible for denitrification in wastewater treatment have not been clearly identified. In this study, a combined microelectrolysis, heterotrophic, and autotrophic denitrification (CEHAD) process was established. DNA-based stable isotope probing was employed to identify the active denitrifiers in reactors fed with either 13C-labeled inorganic or organic carbon sources. The total nitrogen removal efficiencies reached 87.2-92.8% at a low organic carbon concentration (20 mg/L COD). Real-time polymerase chain reaction of the  nirS gene as a function of the DNA buoyant density following the ultracentrifugation of the total DNA indicated marked 13C-labeling of active denitrifiers. High-throughput sequencing of the fractionated DNA in H13CO3-/12CH312COO--fed and H12CO3-/13CH313COO--fed reactors revealed that Thermomonas-like phylotypes were labeled by 13C-bicarbonate, while Thauera-like and Comamonas-like phylotypes were labeled by 13C-acetate. Meanwhile, Arenimonas-like and Rubellimicrobium-like phylotypes were recovered in the "heavy" DNA fractions from both reactors. These results suggest that nitrate removal in CEHAD is catalyzed by various active microorganisms, including autotrophs, heterotrophs, and mixotrophs. Our findings provide a better understanding of the mechanism of nitrogen removal from organic-limited water and wastewater and can be applied to further optimize such processes.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

Micro-electrolysis/retinervus luffae-based simultaneous autotrophic and heterotrophic denitrification for low C/N wastewater treatment.

Nitrogen bioremediation in organic insufficient wastewater generally requires an extra carbon source. In this study, nitrate-contaminated wastewater was treated effectively through simultaneous autotrophic and heterotrophic denitrification based on micro-electrolysis carriers (MECs) and retinervus luffae fructus (RLF), respectively. The average nitrate and total nitrogen removal rates reached 96.3 and 94.0% in the MECs/RLF-based autotrophic and heterotrophic denitrification (MRAHD) system without ammonia and nitrite accumulation. The performance of MRAHD was better than that of MEC-based autotrophic denitrification for the wastewater treatment with low carbon nitrogen (COD/N) ratio. Real-time quantitative polymerase chain reaction (qPCR) revealed that the relative abundance of nirS-type denitrifiers attached to MECs (4.9%) and RLF (5.0%) was similar. Illumina sequencing suggested that the dominant genera were Thiobacillus (7.0%) and Denitratisoma (5.7%), which attached to MECs and RLF, respectively. Sulfuritalea was discovered as the dominant genus in the middle of the reactor. The synergistic interaction between autotrophic and heterotrophic denitrifiers played a vital role in the mixotrophic substrate environment.

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells.

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.

TRANSPORTIN1 Promotes the Association of MicroRNA with ARGONAUTE1 in Arabidopsis.

In Arabidopsis thaliana, microRNAs (miRNAs) are mainly loaded into ARGONAUTE1 (AGO1) to posttranscriptionally regulate gene expression. We previously found that ENHANCED MiRNA ACTIVITY1 (EMA1), an importin ß family protein, negatively regulates miRNA loading into AGO1. In this study, through a suppressor screening of ema1, we identified another importin ß protein, TRANSPORTIN1 (TRN1), as a regulatory component in the miRNA pathway. Mutation of TRN1 did not reduce miRNA accumulation, but it impaired miRNA activity. We found that TRN1 interacted with AGO1. Mutation of the three conserved residues required for cargo recognition of TRN1 reduced its interaction with AGO1 and compromised its function in regulating miRNA activity. Intriguingly, TRN1 dysfunction did not change the cytoplasmic-nuclear distribution of miRNAs and AGO1 but reduced the amount of miRNAs associated with AGO1. These results indicate that TRN1 positively regulates miRNA activity by promoting the association of miRNAs with AGO1, and they reveal opposing roles of two importin ß family proteins in miRNA loading.

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods-autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

Nonlinear computations shaping temporal processing of precortical vision.

Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage.

Inferring Cortical Variability from Local Field Potentials.

The responses of sensory neurons can be quite different to repeated presentations of the same stimulus. Here, we demonstrate a direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials (LFPs). Spikes and LFPs were recorded with a multielectrode array from the middle temporal (MT) area of the visual cortex of macaques during the presentation of continuous optic flow stimuli. A maximum likelihood-based modeling framework was used to predict single-neuron spiking responses using the stimulus, the LFPs, and the activity of other recorded neurons. MT neuron responses were strongly linked to gamma oscillations (maximum at 40 Hz) as well as to lower-frequency delta oscillations (1-4 Hz), with consistent phase preferences across neurons. The predicted modulation associated with the LFP was largely complementary to that driven by visual stimulation, as well as the activity of other neurons, and accounted for nearly half of the trial-to-trial variability in the spiking responses. Moreover, the LFP model predictions accurately captured the temporal structure of noise correlations between pairs of simultaneously recorded neurons, and explained the variation in correlation magnitudes observed across the population. These results therefore identify signatures of network activity related to the variability of cortical neuron responses, and suggest their central role in sensory cortical function. SIGNIFICANCE STATEMENT: The function of sensory neurons is nearly always cast in terms of representing sensory stimuli. However, recordings from visual cortex in awake animals show that a large fraction of neural activity is not predictable from the stimulus. We show that this variability is predictable given the simultaneously recorded measures of network activity, local field potentials. A model that combines elements of these signals with the stimulus processing of the neuron can predict neural responses dramatically better than current models, and can predict the structure of correlations across the cortical population. In identifying ways to understand stimulus processing in the context of ongoing network activity, this work thus provides a foundation to understand the role of sensory cortex in combining sensory and cognitive variables.

Transcription and processing of primary microRNAs are coupled by Elongator complex in Arabidopsis.

MicroRNAs (miRNAs) are a class of small non-coding RNAs that play important regulatory roles in gene expression in plants and animals. The biogenesis of miRNAs involves the transcription of primary miRNAs (pri-miRNAs) by RNA polymerase II (RNAPII) and subsequent processing by Dicer or Dicer-like (DCL) proteins. Here we show that the Elongator complex is involved in miRNA biogenesis in Arabidopsis. Disruption of Elongator reduces RNAPII occupancy at miRNA loci and pri-miRNA transcription. We also show that Elongator interacts with the DCL1-containing Dicing complex and lack of Elongator impairs DCL1 localization in the nuclear Dicing body. Finally, we show that pri-miRNA transcripts as well as DCL1 associate with the chromatin of miRNA genes and the chromatin association of DCL1 is compromised in the absence of Elongator. Our results suggest that Elongator functions in both transcription and processing of pri-miRNAs and probably couples these two processes.

Fresnel diffraction of fractal grating and self-imaging effect.

Based on the self-similarity property of fractal, two types of fractal gratings are produced according to the production and addition operations of multiple periodic gratings. Fresnel diffractions of fractal grating are analyzed theoretically, and the general mathematic expressions of the diffraction intensity distributions of fractal grating are deduced. The gray-scale patterns of the 2D diffraction distributions of fractal grating are provided through numerical calculations. The diffraction patterns take on the periodicity along the longitude and transverse directions. The 1D diffraction distribution at some certain distances shows the same structure as the fractal grating. This indicates that the self-image of fractal grating is really formed in the Fresnel diffraction region. The experimental measurement of the diffraction intensity distribution of fractal grating with different fractal dimensions and different fractal levels is performed, and the self-images of fractal grating are obtained successfully in experiments. The conclusions of this paper are helpful for the development of the application of fractal grating.

Diverse suppressive influences in area MT and selectivity to complex motion features.

Neuronal selectivity results from both excitatory and suppressive inputs to a given neuron. Suppressive influences can often significantly modulate neuronal responses and impart novel selectivity in the context of behaviorally relevant stimuli. In this work, we use a naturalistic optic flow stimulus to explore the responses of neurons in the middle temporal area (MT) of the alert macaque monkey; these responses are interpreted using a hierarchical model that incorporates relevant nonlinear properties of upstream processing in the primary visual cortex (V1). In this stimulus context, MT neuron responses can be predicted from distinct excitatory and suppressive components. Excitation is spatially localized and matches the measured preferred direction of each neuron. Suppression is typically composed of two distinct components: (1) a directionally untuned component, which appears to play the role of surround suppression and normalization; and (2) a direction-selective component, with comparable tuning width as excitation and a distinct spatial footprint that is usually partially overlapping with excitation. The direction preference of this direction-tuned suppression varies widely across MT neurons: approximately one-third have overlapping suppression in the opposite direction as excitation, and many other neurons have suppression with similar direction preferences to excitation. There is also a population of MT neurons with orthogonally oriented suppression. We demonstrate that direction-selective suppression can impart selectivity of MT neurons to more complex velocity fields and that it can be used for improved estimation of the three-dimensional velocity of moving objects. Thus, considering MT neurons in a complex stimulus context reveals a diverse set of computations likely relevant for visual processing in natural visual contexts.

Inferring nonlinear neuronal computation based on physiologically plausible inputs.

The computation represented by a sensory neuron's response to stimuli is constructed from an array of physiological processes both belonging to that neuron and inherited from its inputs. Although many of these physiological processes are known to be nonlinear, linear approximations are commonly used to describe the stimulus selectivity of sensory neurons (i.e., linear receptive fields). Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neuron's inputs. Incorporating such 'upstream nonlinearities' within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron's response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. Parameter estimation is robust and efficient even with large numbers of model components and in the context of high-dimensional stimuli with complex statistical structure (e.g. natural stimuli). We describe detailed methods for estimating the model parameters, and illustrate the advantages of the NIM using a range of example sensory neurons in the visual and auditory systems. We thus present a modeling framework that can capture a broad range of nonlinear response functions while providing physiologically interpretable descriptions of neural computation.

Impact of noise structure and network topology on tracking speed of neural networks.

Understanding why neural systems can process information extremely fast is a fundamental question in theoretical neuroscience. The present study investigates the effect of noise on accelerating neural computation. To evaluate the speed of network response, we consider a computational task in which the network tracks time-varying stimuli. Two noise structures are compared, namely, the stimulus-dependent and stimulus-independent noises. Based on a simple linear integrate-and-fire model, we theoretically analyze the network dynamics, and find that the stimulus-dependent noise, whose variance is proportional to the mean of external inputs, has better effect on speeding up network computation. This is due to two good properties in the transient network dynamics: (1) the instant firing rate of the network is proportional to the mean of external inputs, and (2) the stationary state of the network is robust to stimulus changes. We investigate two network models with varying recurrent interactions, and find that recurrent interactions tend to slow down the tracking speed of the network. When the biologically plausible Hodgkin-Huxley model is considered, we also observe that the stimulus-dependent noise accelerates neural computation, although the improvement is smaller than that in the case of linear integrate-and-fire model.