Dissecting whole-brain conduction delays through MRI microstructural measures

Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences

Assessment of structural characteristics of regenerated cellulolytic enzyme lignin based on a mild DMSO/[Emim]OAc dissolution system from triploid of Populus tomentosa Carr.

The structural characteristics of native lignin are essential for the further deconstruction of plant cell walls for value-added application of lignocellulosic biomass.</p

Modeling Hidden Nodes Collisions in Wireless Sensor Networks: Analysis Approach

This paper studied both types of collisions. In this paper, we show that advocated solutions for coping with hidden node collisions are unsuitable for sensor networks. We model both types of collisions and derive closed-form formula giving the probability of hidden and visible node collisions. To reduce these collisions, we propose two solutions. The first one based on tuning the carrier sense threshold saves a substantial amount of collisions by reducing the number of hidden nodes. The second one based on adjusting the contention window size is complementary to the first one. It reduces the probability of overlapping transmissions, which reduces both collisions due to hidden and visible nodes. We validate and evaluate the performance of these solutions through simulations

Estimating axial diffusivity in the NODDI model

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model ( d ∥ = 1.7 μ m 2 /ms ). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼ 2 − 2.5 μ m 2 /ms , in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Stock Assessment of Chub Mackerel (<i>Scomber japonicus</i>) in the Northwest Pacific Using a Multi-Model Approach

Chub mackerel (Scomber japonicus) is a major targeted species in the Northwest Pacific Ocean, fished by China, Japan, and Russia, and predominantly captured with purse seine fishing gear. A formal stock assessment of Chub mackerel in the region has yet to be implemented by the managing authority, that is, the North Pacific Fisheries Commission (NPFC). This study aims to provide a wider choice of potential models for the stock assessment of Chub mackerel in the Northwest Pacific using available data provided by members of the NPFC. The five models tested in the present study are CMSY, BSM, SPiCT, JABBA, and JABBA-Select. Furthermore, the influence of different data types and input parameters on the performance of the different models used was evaluated. These effects for each model are catch time series for CMSY, catch time series and prior of the relative biomass for BSM, prior information for SPiCT, and selectivity coefficients for JABBA-Select. Catch and CPUE (catch per unit effort) data used are derived from NPFC, while some life history information is referred from other references. The results indicate that Chub mackerel stock might be slightly overfished, as indicated by CMSY (B2020/BMSY = 0.98, F2020/FMSY = 1.12), BSM (B2020/BMSY = 0.97, F2020/FMSY = 1.21), and the base case run for the JABBA-Select (SB2020/SBMSY = 0.99, H2020/HMSY = 0.99) models. The results of the models SPiCT (B2020/BMSY = 2.30, F2020/FMSY = 0.31) and JABBA (B2020/BMSY = 1.40, F2020/FMSY = 0.62) showed that the state of this stock may be healthy. Changes in the catch time series did not affect CMSY results but did affect BSM. The present study confirms that prior information for BSM and SPiCT models is very important in order to obtain reliable results on the stock status. The results of JABBA-Select showed that different selectivity coefficients can affect the stock status of a species, as observed in the present study. Based on the optimistic stock status indicated by the best model, JABBA, a higher catch is allowable, but further projection is required for specific catch limit setting. Results suggested that, as a precautionary measure, management would be directed towards maintaining or slightly reducing the fishing effort for the sustainable harvest of this fish stock, while laying more emphasis on accurately estimating prior input parameters for use in assessment models

Environmental Characteristics Associated with the Presence of the Pelagic Stingray (<i>Pteroplatytrygon violacea</i>) in the Pacific High Sea

The pelagic stingray (Pteroplatytrygon violacea), perhaps the only stingray to inhabit open ocean waters, is highly interactive with longline and purse seine fisheries. The threat to P. violacea posed by high bycatch mortality has received widespread attention. To date, the environmental preference of P. violacea, which is important in designing conservation and management measures, has not been well studied. Based on data collected during a 2016–2019 survey in the Pacific Ocean by national observers of tuna longline fisheries, the relationship between the presence of P. violacea and spatiotemporal and environmental variables was first analyzed using the Generalized Additive Model. The results showed that geographic location (latitude and longitude) was the most influential variable. Monthly, P. violacea is frequently present in the Pacific high sea from December to May. The El Niño–Southern Oscillation had a significant impact on the presence of P. violacea in the Pacific high sea, with both the cold (Ocean Nino Index 1) phases leading to a decrease in its presence. Regarding the environmental factors, we found that high presence was associated with low salinity (33.0~34.5 psu), a relatively high concentration of chlorophyll (0.2–0.35 mg/m3), and warm water (>20 °C). P. violacea was most likely observed in the waters offshore, closer to seamounts, and with water depths between 4000 and 5000 m. Four areas, including those east of the Solomon Islands and east of Kiribati, areas west of the Galapagos Islands, and areas near the coastal upwelling of northern Peru, related to upwelling systems or seamounts, were identified as the potential key habitats of P. violacea. Predicted distribution maps showed a significant seasonal variation in the presence of P. violacea. Moreover, the yearly change in the presence of P. violacea in the Pacific high sea indicated a possible decreasing trend in recent years. The information first provided here is essential for developing conservation and management measures for P. violacea to prevent the unavoidable ecological consequences of bycatch or other anthropogenic factors

Genetic inhibition of glutamate allosteric potentiation of GABAARs in mice results in hyperexcitability, leading to neurobehavioral abnormalities

Abstract The imbalance between neuronal excitation and inhibition (E/I) in neural circuit has been considered to be at the root of numerous brain disorders. We recently reported a novel feedback crosstalk between the excitatory neurotransmitter glutamate and inhibitory γ‐aminobutyric acid type A receptor (GABAAR)‐glutamate allosteric potentiation of GABAAR functions through a direct binding of glutamate to the GABAAR itself. Here, we investigated the physiological significance and pathological implications of this cross‐talk by generating the β3E182G knock‐in (KI) mice. We found that β3E182G KI, while had little effect on basal GABAAR‐mediated synaptic transmission, significantly reduced glutamate potentiation of GABAAR‐mediated responses. These KI mice displayed lower thresholds for noxious stimuli, higher susceptibility to seizures and enhanced hippocampus‐related learning and memory. Additionally, the KI mice exhibited impaired social interactions and decreased anxiety‐like behaviors. Importantly, hippocampal overexpression of wild‐type β3‐containing GABAARs was sufficient to rescue the deficits of glutamate potentiation of GABAAR‐mediated responses, hippocampus‐related behavioral abnormalities of increased epileptic susceptibility, and impaired social interactions. Our data indicate that the novel crosstalk among excitatory glutamate and inhibitory GABAAR functions as a homeostatic mechanism in fine‐tuning neuronal E/I balance, thereby playing an essential role in ensuring normal brain functioning