Elucidating Spatially-Resolved Changes in Host Signaling During Plasmodium Liver-Stage Infection

Upon transmission to the human host, Plasmodium sporozoites exit the skin, are taken up by the blood stream, and then travel to the liver where they infect and significantly modify a single hepatocyte. Low infection rates within the liver have made proteomic studies of infected hepatocytes challenging, particularly in vivo, and existing studies have been largely unable to consider how protein and phosphoprotein differences are altered at different spatial locations within the heterogeneous liver. Using digital spatial profiling, we characterized changes in host signaling during Plasmodium yoelii infection in vivo without disrupting the liver tissue. Moreover, we measured alterations in protein expression around infected hepatocytes and identified a subset of CD163+ Kupffer cells that migrate towards infected cells during infection. These data offer the first insight into the heterogeneous microenvironment that surrounds the infected hepatocyte and provide insights into how the parasite may alter its milieu to influence its survival and modulate immunity.

Numerical modeling of pedestal stability and broadband turbulence of wide-pedestal QH-mode plasmas on DIII-D

Wide pedestal Quiescent High confinement (QH) mode discovered on DIII-D in recent years is a stationary and quiescent H-mode with the pedestal width exceeding EPED prediction by at least 25%. Its characteristics, such as low rotation, high energy confinement and ELM-free operation, make it an attractive operation mode for future reactors. Linear and nonlinear simulations using BOUT++ reduced two fluid MHD model are carried out to investigate the bursty broadband turbulence often observed in the edge of wide-pedestal QH-mode plasmas. Two kinds of MHD-scale instabilities in different spatial locations within the pedestal were found in the simulations: one mild peeling-ballooning (PB) mode (γ_PB<0.04ω_A) located near the minimum in Er well propagating in ion diamagnetic drift direction; and one drift-Alfvén wave (DAW) locates at smaller radius compared to Er well propagating in the electron diamagnetic drift direction and unstable only when the parallel electron dynamics is included in the simulation. The coupling between drift wave and shear Alfvén wave provides a possible cause of the experimentally observed local profile flattening in the upper-pedestal. The rotation direction, mode location, as well as the wavenumber of these two modes from BOUT++ simulations agree reasonably well with the experimental measurements, while the lack of quantitatively agreement is likely due to the lack of trapped electron physics in current fluid model. This work presents improved physics understanding of the pedestal stability and turbulence dynamics for wide-pedestal QH-mode.

Alignment of spatial genomics and histology data using deep Gaussian processes

Spatially-resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of the local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals, and technologies. Here, we propose a probabilistic model that aligns a set of spatially-resolved genomics and histology slices onto a known or unknown common coordinate system into which the samples are aligned both spatially and in terms of the phenotypic readouts (e.g., gene or protein expression levels, cell density, open chromatin regions). Our method consists of a two-layer Gaussian process: the first layer maps the observed samples' spatial locations into a common coordinate system, and the second layer maps from the common coordinate system to the observed readouts. Our approach also allows for slices to be mapped to a known template coordinate space if one exists. We show that our registration approach enables complex downstream spatially-aware analyses of spatial genomics data at multiple resolutions that are impossible or inaccurate with unaligned data, including an analysis of variance, differential expression across the z-axis, and association tests across multiple data modalities.

Interpreting models interpreting brain dynamics

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.

Bacterial Community Is Affected by Locations and Time Rather Than Potato Varieties but Streptomyces spp. Are Related to Potato Varieties

Improved knowledge and a better understanding of the functions of bacterial communities are vital for effective crop disease management. This study was conducted to study a bacterial community’s relationship with the common scab in four different potato varieties (Dejima, DJ; Atlantic, DS; Seohong, SH; Haryeong, HY) at two different locations (Gangneung and Chuncheon) and spatial locations (rhizosphere and furrow) at two different times (preharvest and postharvest). In addition, metagenomic sequencing was performed by extracting genomic DNA from soil samples to observe the dominant bacterial microbes and disease severity of the common scab in all the tested varieties in spatial location and time. The results suggest that the most dominant bacterial phyla in all the soil samples were Proteobacteria, Acidobacteria, and Bacteroidetes. Additionally, Streptomyces spp. were found to be more abundant in the susceptible variety (DJ) than in other varieties (DS, SH, and HY). Interestingly, bacterial communities were found to be more diverse across the two different geographical locations, spatial locations, and harvesting times, rather than the variety of potato, according to PCoA analysis. There were no interlinked changes in bacterial communities among the varieties. Moreover, the 14 most dominant bacterial genus correlation networks with Streptomyces spp. suggested that there was a significant positive and negative correlation to some extent. Alpha and beta diversity results clearly indicated that the possible reason for differences in bacterial communities might have been due to the different spatial locations, in comparison with varieties, which suggests that there was no significant correlation between bacterial community richness and diversity among the varieties.

Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse V1

The neocortex is a network of rather stereotypical cortical microcircuits that share an exquisite genetically encoded architecture: Neurons of a fairly large number of different types are distributed over several layers (laminae), with specific probabilities of synaptic connections that depend on the neuron types involved and their spatial locations. Most available knowledge about this structure has been compiled into a detailed model [Billeh et al., 2020] for a generic cortical microcircuit in the primary visual cortex, consisting of 51,978 neurons of 111 different types. We add a noise model to the network that is based on experimental data, and analyze the results of network computations that can be extracted by projection neurons on layer 5. We show that the resulting model acquires through alignment of its synaptic weights via gradient descent training the capability to carry out a number of demanding visual processing tasks. Furthermore, this weight-alignment induces specific neural coding features in the microcircuit model that match those found in the living brain: High dimensional neural codes with an arguably close to optimal power-law decay of explained variance of PCA components, specific relations between signal- and noise-coding dimensions, and network dynamics in a critical regime. Hence these important features of neural coding and dynamics of cortical microcircuits in the brain are likely to emerge from aspects of their genetically encoded architecture that are captured by this data-based model in combination with learning processes. In addition, the model throws new light on the relation between visual processing capabilities and details of neural coding.

Experience-driven meaning affects lexical choices during language production

The role of meaning facets based on sensorimotor experiences is well-investigated in comprehension but has received little attention in language production research. In two experiments, we investigated whether experiential traces of space influenced lexical choices when participants completed visually-presented sentence fragments (e.g., ‘You are at the sea and you see a ...’) with spoken nouns (e.g., ‘dolphin’, ‘palm tree’). The words were presented consecutively in an ascending or descending direction, starting from the center of the screen. These physical spatial cues did not influence lexical choices. However, the produced nouns met the spatial characteristics of the broader sentence contexts such that the typical spatial locations of the produced noun referents were predicted by the location of the situations described by the sentence fragments (i. e., upper or lower sphere). By including distributional semantic similarity measures derived from computing cosine values between sentence nouns and produced nouns using a web-based text corpus, we show that the meaning dimension of ‘location in space’ guides lexical selection during speaking. We discuss the relation of this spatial meaning dimension to accounts of experientially grounded and usage-based theories of language processing and their combination in hybrid approaches. In doing so, we contribute to a more comprehensive understanding of the many facets of meaning processing during language production and their impact on the words we select to express verbal messages.

Effects of Frequency on the Directional Auditory Sensitivity of Northern Saw-Whet Owls (Aegolius acadicus)

Many animals use sound as a medium for detecting or locating potential prey items or predation threats. Northern saw-whet owls (<i>Aegolius acadicus</i>) are particularly interesting in this regard, as they primarily rely on sound for hunting in darkness, but are also subject to predation pressure from larger raptors. We hypothesized that these opposing tasks should favor sensitivity to low-frequency sounds arriving from many locations (potential predators) and high-frequency sounds below the animal (ground-dwelling prey items). Furthermore, based on the morphology of the saw-whet owl skull and the head-related transfer functions of related species, we expected that the magnitude of changes in sensitivity across spatial locations would be greater for higher frequencies than low frequencies (i.e., more “directional” at high frequencies). We used auditory-evoked potentials to investigate the frequency-specific directional sensitivity of Northern saw-whet owls to acoustic signals. We found some support for our hypothesis, with smaller-magnitude changes in sensitivity across spatial locations at lower frequencies and larger-magnitude changes at higher frequencies. In general, owls were most sensitive to sounds originating in front of and above their heads, but at 8 kHz there was also an area of high sensitivity below the animals. Our results suggest that the directional hearing of saw-whet owls should allow for both predator and prey detection.

Use of Picture Information Measure using Color Features for Image Retrieval in CBIR

Content-Based Image Retrieval systems backups the image retrieval process using the primary characteristics of image like colour, shape, texture and spatial locations clubbed with the semantic approaches for better efficiency and performance. Various information measures have been proposed in order to increase the level of Retrieval. A method of picture information measures based upon the concept of the minimum number of gray level changes to convert a picture into one with a desired histogram is presented. In search of finding a new perspective an integrated approach of Picture Information Measure (PIM) employed with the primitive visual feature color. The retrieval results obtained by applying color histogram (CH) on PIM (PIM of Red Green and Blue and there integrated variation) + Color Moment to a 1000 image database demonstrated significant improvement in retrieval effectively.

An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition

Interrupted sampling repeater jamming (ISRJ) is an effective method for implementing deception jamming on chirp radars. By means of frequency-shifting jamming processing of the target echo signal and pulse compression during image processing, a group of false targets will appear in different spatial locations around the true target. Extracting the features of these false targets is complex and limited to existing countering methods against ISRJ. This paper proposes an anti-jamming method to identify the spatial location characteristics of two-dimensional deception false targets. By adjusting the parameters of the radar transmitted signal, the method simultaneously transmits the anti-jamming signal and carries out false target identification and elimination in the range and azimuth dimensions. Eventually, the optimal signal parameter design of the anti-jamming signal is obtained by comparing different anti-jamming strategies in the range dimension. The validity of the proposed method is proved by deducing the mathematical model between the spatial distribution characteristics of the false targets and the radar transmitted signal parameters and demonstrated by simulations.