Prognostic implication of 1-year decline in diffusing capacity in newly diagnosed idiopathic pulmonary fibrosis.

The progression of idiopathic pulmonary fibrosis (IPF) is assessed through serial monitoring of forced vital capacity (FVC). Currently, data regarding the clinical significance of longitudinal changes in diffusing capacity for carbon monoxide (DLCO) is lacking. We investigated the prognostic implications of a 1-year decline in DLCO in 319 patients newly diagnosed with IPF at a tertiary hospital between January 2010 and December 2020. Changes in FVC and DLCO over the first year after the initial diagnosis were reviewed; a decline in FVC ≥ 5% and DLCO ≥ 10% predicted were considered significant changes. During the first year after diagnosis, a significant decline in FVC and DLCO was observed in 101 (31.7%) and 64 (20.1%) patients, respectively. Multivariable analysis showed that a 1-year decline in FVC ≥ 5% predicted (aHR 2.74, 95% CI 1.88-4.00) and 1-year decline in DLCO ≥ 10% predicted (aHR 2.31, 95% CI 1.47-3.62) were independently associated with a higher risk of subsequent mortality. The prognostic impact of a decline in DLCO remained significant regardless of changes in FVC, presence of emphysema, or radiographic indications of pulmonary hypertension. Therefore, serial monitoring of DLCO should be recommended because it may offer additional prognostic information compared with monitoring of FVC alone.

Comparison of visual quantities in untrained neural networks.

The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains.

A Study on TiO2 Surface Texturing Effect for the Enhancement of Photocatalytic Reaction in a Total Phosphorous Concentration Measurement System.

Powerful sunlight, a high water temperature, and stagnation in the water flow induce eutrophication in rivers and lakes, which destroys the aquatic ecosystem and threatens the downstream water supply systems. Accordingly, it is very important to perform real-time measurements of nutrients that induce algal growth, especially total phosphorus, to preserve and manage the aquatic ecosystem. To conduct quantitative analysis of the total phosphorus in the aquatic ecosystem, it is essential to perform a pretreatment process and quickly separate the phosphorus, combined with organic and inorganic materials, into a phosphate. In this study, the sandblasting process was used for the physical etching of the wafer, and photocatalytic materials were deposited on the surface with various roughness in order to improve the photocatalytic reaction surface and efficiency. The photocatalytic reaction was applied to combine the pretreated sample with the coloring agent for color development, and the absorbance of the colored sample was analyzed quantitatively to compare and evaluate the characteristics, followed by the surface increase in the photocatalytic materials. In addition, the pretreatment and measurement parts were materialized in a single chip to produce a small and light total phosphorus analysis sensor.

Interhemispheric Cortico-Cortical Pathway for Sequential Bimanual Movements in Mice.

Animals precisely coordinate their left and right limbs for various adaptive purposes. While the left and right limbs are clearly controlled by different cortical hemispheres, the neural mechanisms that determine the action sequence between them remains elusive. Here, we have established a novel head-fixed bimanual-press (biPress) sequence task in which mice sequentially press left and right pedals with their forelimbs in a predetermined order. Using this motor task, we found that the motor cortical neurons responsible for the first press (1P) also generate independent motor signals for the second press (2P) by the opposite forelimb during the movement transitions between forelimbs. Projection-specific calcium imaging and optogenetic manipulation revealed these motor signals are transferred from one motor cortical hemisphere to the other via corticocortical projections. Together, our results suggest the motor cortices coordinate sequential bimanual movements through corticocortical pathways.

Parallel processing of working memory and temporal information by distinct types of cortical projection neurons.

It is unclear how different types of cortical projection neurons work together to support diverse cortical functions. We examined the discharge characteristics and inactivation effects of intratelencephalic (IT) and pyramidal tract (PT) neurons-two major types of cortical excitatory neurons that project to cortical and subcortical structures, respectively-in the deep layer of the medial prefrontal cortex in mice performing a delayed response task. We found stronger target-dependent firing of IT than PT neurons during the delay period. We also found the inactivation of IT neurons, but not PT neurons, impairs behavioral performance. In contrast, PT neurons carry more temporal information than IT neurons during the delay period. Our results indicate a division of labor between IT and PT projection neurons in the prefrontal cortex for the maintenance of working memory and for tracking the passage of time, respectively.

Distinct role of flexible and stable encodings in sequential working memory.

The serial-position effect in working memory is considered important for studying how a sequence of sensory information can be retained and manipulated simultaneously in neural memory circuits. Here, via a precise analysis of the primacy and recency effects in human psychophysical experiments, we propose that stable and flexible codings take distinct roles of retaining and updating information in working memory, and that their combination induces serial-position effects spontaneously. We found that stable encoding retains memory to induce the primacy effect, while flexible encoding used for learning new inputs induces the recency effect. A model simulation based on human data, confirmed that a neural network with both flexible and stable synapses could reproduce the major characteristics of serial-position effects. Our new prediction, that the control of resource allocation by flexible-stable coding balance can modulate memory performance in sequence-specific manner, was supported by pre-cued memory performance data in humans.

Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data.

Various patterns of neural activity are observed in dynamic cortical imaging data. Such patterns may reflect how neurons communicate using the underlying circuitry to perform appropriate functions; thus it is crucial to investigate the spatiotemporal characteristics of the observed neural activity patterns. In general, however, neural activities are highly nonlinear and complex, so it is a demanding job to analyze them quantitatively or to classify the patterns of observed activities in various types of imaging data. Here, we present our implementation of a novel method that successfully addresses the above issues for precise comparison and classification of neural activity patterns. Based on two-dimensional representations of the geometric structure and temporal evolution of activity patterns, our method successfully classified a number of computer-generated sample patterns created from combinations of various spatial and temporal patterns. In addition, we validated our method with voltage-sensitive dye imaging data of Alzheimer's disease (AD) model mice. Our analysis algorithm successfully distinguished the activity data of AD mice from that of wild type with significantly higher performance than previously suggested methods. Our result provides a pragmatic solution for precise analysis of spatiotemporal patterns of neural imaging data.