Antibody production is a metabolically demanding process that is regulated by gut microbiota, but the microbial products supporting B cell responses remain incompletely identified. We report that ...short-chain fatty acids (SCFAs), produced by gut microbiota as fermentation products of dietary fiber, support host antibody responses. In B cells, SCFAs increase acetyl-CoA and regulate metabolic sensors to increase oxidative phosphorylation, glycolysis, and fatty acid synthesis, which produce energy and building blocks supporting antibody production. In parallel, SCFAs control gene expression to express molecules necessary for plasma B cell differentiation. Mice with low SCFA production due to reduced dietary fiber consumption or microbial insufficiency are defective in homeostatic and pathogen-specific antibody responses, resulting in greater pathogen susceptibility. However, SCFA or dietary fiber intake restores this immune deficiency. This B cell-helping function of SCFAs is detected from the intestines to systemic tissues and conserved among mouse and human B cells, highlighting its importance.
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•Short-chain fatty acids (SCFAs) produced by gut microbiota promote antibody responses•SCFAs activate B cell metabolism for production of energy and building blocks•SCFAs control gene expression for plasma B cell differentiation•SCFAs boost antibody responses during infection, decreasing susceptibility to pathogens
Kim et al. demonstrate that short-chain fatty acids (SCFAs), produced by the gut microbiota as fermentation products of dietary fiber, support host antibody responses by regulating gene expression and enhancing cellular metabolism and plasma B cell differentiation. SCFAs boost mucosal and systemic antibody responses during steady state and infection.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The gut is connected to the CNS by immunological mediators, lymphocytes, neurotransmitters, microbes and microbial metabolites. A mounting body of evidence indicates that the microbiome exerts ...significant effects on immune cells and CNS cells. These effects frequently result in the suppression or exacerbation of inflammatory responses, the latter of which can lead to severe tissue damage, altered synapse formation and disrupted maintenance of the CNS. Herein, we review recent progress in research on the microbial regulation of CNS diseases with a focus on major gut microbial metabolites, such as short-chain fatty acids, tryptophan metabolites, and secondary bile acids. Pathological changes in the CNS are associated with dysbiosis and altered levels of microbial metabolites, which can further exacerbate various neurological disorders. The cellular and molecular mechanisms by which these gut microbial metabolites regulate inflammatory diseases in the CNS are discussed. We highlight the similarities and differences in the impact on four major CNS diseases, i.e., multiple sclerosis, Parkinson's disease, Alzheimer's disease, and autism spectrum disorder, to identify common cellular and molecular networks governing the regulation of cellular constituents and pathogenesis in the CNS by microbial metabolites.
This paper presents 28 GHz wideband propagation channel characteristics for millimeter wave (mmWave) urban cellular communication systems. The mmWave spectrum is considered as a key-enabling feature ...of 5G cellular communication systems to provide an enormous capacity increment; however, mmWave channel models are lacking today. The paper compares measurements conducted with a spherical scanning 28 GHz channel sounder system in the urban street-canyon environments of Daejeon, Korea and NYU campus, Manhattan, with ray-tracing simulations made for the same areas. Since such scanning measurements are very costly and time-intensive, only a relatively small number of channel samples can be obtained. The measurements are thus used to quantify the accuracy of a ray-tracer; the ray-tracer is subsequently used to obtain a large number of channel samples to fill gaps in the measurements. A set of mmWave radio propagation parameters is presented based on both the measurement results and ray-tracing, and the corresponding channel models following the 3GPP spatial channel model (SCM) methodology are also described.
Abstract While human vision spans 220°, traditional functional MRI setups display images only up to central 10-15°. Thus, it remains unknown how the brain represents a scene perceived across the full ...visual field. Here, we introduce a method for ultra-wide angle display and probe signatures of immersive scene representation. An unobstructed view of 175° is achieved by bouncing the projected image off angled-mirrors onto a custom-built curved screen. To avoid perceptual distortion, scenes are created with wide field-of-view from custom virtual environments. We find that immersive scene representation drives medial cortex with far-peripheral preferences, but shows minimal modulation in classic scene regions. Further, scene and face-selective regions maintain their content preferences even with extreme far-periphery stimulation, highlighting that not all far-peripheral information is automatically integrated into scene regions computations. This work provides clarifying evidence on content vs. peripheral preferences in scene representation and opens new avenues to research immersive vision.
We can easily perceive the spatial scale depicted in a picture, regardless of whether it is a small space (e.g., a close-up view of a chair) or a much larger space (e.g., an entire class room). How ...does the human visual system encode this continuous dimension? Here, we investigated the underlying neural coding of depicted spatial scale, by examining the voxel tuning and topographic organization of brain responses. We created naturalistic yet carefully-controlled stimuli by constructing virtual indoor environments, and rendered a series of snapshots to smoothly sample between a close-up view of the central object and far-scale view of the full environment (object-to-scene continuum). Human brain responses were measured to each position using functional magnetic resonance imaging. We did not find evidence for a smooth topographic mapping for the object-to-scene continuum on the cortex. Instead, we observed large swaths of cortex with opposing ramp-shaped profiles, with highest responses to one end of the object-to-scene continuum or the other, and a small region showing a weak tuning to intermediate scale views. However, when we considered the population code of the entire ventral occipito-temporal cortex, we found smooth and linear representation of the object-to-scene continuum. Our results together suggest that depicted spatial scale information is encoded parametrically in large-scale population codes across the entire ventral occipito-temporal cortex.
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The millimeter-wave (mm-wave) band will be a key component of fifth-generation (5G) wireless communication systems. This paper presents radio propagation measurements and analysis investigating the ...wideband directional channel characteristics of the mm-wave transmission for in-building and urban cellular communication systems in the 28-GHz band. Based on the measurements, we analyze and model the spatio-temporal channel characteristics such as multipath delay, angular statistics, and path loss. In particular we investigate the clustering of the multipath components, and investigate both the intra-cluster and inter-cluster distributions. Based on these investigations, we present a complete channel model suitable for system simulations in the in-building and urban environments.
This paper considers a fundamental issue of path loss (PL) modeling in urban micro cell (UMi) environments, namely the spatial consistency of the model as the mobile station moves along a trajectory ...through street canyons. This paper is motivated by the observed non-stationarity of the PL. We show that the traditional model of power law PL plus lognormally distributed variations can provide misleading results that can have serious implications for system simulations. Rather, the PL parameters have to be modeled as random variables that change from street to street and also as a function of the street orientation. Variations of the PL, taken over the ensemble of the whole cell (or multiple cells), thus consist of the compound effect of these PL parameter variations together with the traditional shadowing variations along the trajectory of movement. Ray-tracing results demonstrate that ignoring this effect can lead to a severe overestimation of the local standard deviation in a given area. Then, a spatially consistent stochastic street-by-street PL model is established, and a parameterization for 28-GHz UMi cells is given. The model correctly describes the PL as a function of the street orientation as well as the large variance observed for all the PL model parameters.
With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research ...topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.
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A colorimetric loop-mediated isothermal amplification assay detects changes in pH during amplification based on color changes at a constant temperature. Currently, various studies have focused on ...developing and assessing molecular point-of-care testing instruments. In this study, we evaluated amplified DNA concentrations measured using the colorimetric LAMP assay of the 1POT™ Professional device (1drop Inc, Korea). Results of the 1POT analysis of clinical samples were compared with measurements obtained from the Qubit™ 4 and NanoDrop™ 2000 devices (both from Thermo Fisher Scientific, MA, USA). These results showed a correlation of 0.98 (95% CI: 0.96–0.99) and 0.96 (95% CI: 0.92–0.98) between 1POT and the Qubit and NanoDrop. 1POT can measure amplified DNA accurately and is suitable for on-site molecular diagnostics.
The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the ...various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.
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