Explore the vast range of titles available.

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

Alzheimer's disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions. Although connections between changes in brain networks of Alzheimer's disease patients have been established, the mechanisms that drive these alterations remain incompletely understood. This study, which was conducted in 2018 at Northeastern University in China, included data from 97 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset covering genetics, imaging, and clinical data. All participants were divided into two groups: normal control (n = 52; 20 males and 32 females; mean age 73.90 ± 4.72 years) and Alzheimer's disease (n = 45, 23 males and 22 females; mean age 74.85 ± 5.66). To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer's disease patients, we proposed a local naïve Bayes brain network model based on graph theory. Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined, including clustering coefficient, modularity, characteristic path length, network efficiency, betweenness, and degree distribution compared with empirical methods. This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer's disease patients. Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions. The ADNI was performed in accordance with the Good Clinical Practice guidelines, US 21CFR Part 50-Protection of Human Subjects, and Part 56-Institutional Review Boards (IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards (IRBs)/Research Ethics Boards (REBs).

The ability of motor-inhibitory control is critical in daily life. The physiological mechanisms underlying motor inhibitory control deficits remain to be elucidated. Beta band oscillations have been suggested to be related to motor performance, but whether they relate to motor-inhibitory control remains unclear. This study is aimed at systematically investigating the relationship between beta band oscillations and motor-inhibitory control to determine whether beta band oscillations were related to the ability of motor-inhibitory control. We studied 30 healthy young adults (age: 21.6 ± 1.5 years). Stop-signal reaction time (SSRT) was derived from stop signal task, indicating the ability of motor-inhibitory control. Resting-state electroencephalography (EEG) was recorded for 12 min. Beta band power and functional connectivity (including global efficiency) were calculated. Correlations between beta band oscillations and SSRT were performed. Beta band EEG power in left and right motor cortex (MC), right somatosensory cortex (SC), and right inferior frontal cortex (IFC) was positively correlated with SSRT ( 's = 0.031, 0.021, 0.045, and 0.015, respectively). Beta band coherence between bilateral MC, SC, and IFC was also positively correlated with SSRT ( 's < 0.05). Beta band global efficiency was positively correlated with SSRT ( = 0.01). This is the first study to investigate the relationship between resting-state cortical beta oscillations and response inhibition. Our findings revealed that individuals with better ability of motor inhibitory control tend to have less cortical beta band power and functional connectivity. This study has clinical significance on the underlying mechanisms of motor inhibitory control deficits.

•The noise-assisted EMD method, EEMD, was utilized to decompose the BOLD signals and further establish the timescale-specific functional brain networks (FBNs).•There were differences between intrinsic and traditional FBNs regarding global and regional network properties.•The intrinsic FBNs reveal more precise physiological information than traditional FBN.•The method that examined intrinsic FBNs could shed light on the critical components of the BOLD signals. Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20–85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features—global efficiency and local efficiency values—were estimated to determine their relationship with age and cognitive performance. The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

INTRODUCTION The retina may provide non‐invasive, scalable biomarkers for monitoring cerebral neurodegeneration. METHODS We used cross‐sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency). RESULTS After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency. DISCUSSION The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

Abstract Sensitivity to the social environment is linked to suicidal ideation in adolescence, and little research has examined variance in neural functioning that may underlie this sensitivity and increase risk. Neural-based pathways to suicidal ideation are likely mediated by subjective experiences of the social environment. Loneliness is associated with both salience network connectivity and suicidal ideation. This longitudinal study tested whether greater salience network functional integration (i.e. global efficiency) in early adolescence, which may underlie hypervigilance to social experiences, predicts risk for future suicidal ideation via loneliness. Participants (N = 96; Mage=12.94) completed a fMRI scan to measure resting-state salience network functional integration. Loneliness, suicidal ideation, and a sociometric measure of adolescents’ real-world peer environment were assessed over several years. Greater salience network global efficiency was associated with suicidal ideation two years later via higher levels of loneliness approximately one year later, particularly for girls. Across boys and girls, the effect of salience network global efficiency on loneliness appeared stronger for youth experiencing relatively larger decreases in peer acceptance over the prior year. While findings should be interpreted as preliminary given the sample size, they suggest a possible social-developmental pathway from early-adolescent salience network integration to future vulnerability for loneliness and suicidal thinking.

The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating the development of performance indices to enhance their efficiency and energy quality. In this study, four performance indices are proposed to evaluate the efficiency and energy quality of photovoltaic systems quantitatively. The entire process is analyzed, encompassing solar energy capture, DC-DC and DC-AC conversion, and filtering, to deliver maximum energy and quality to the load. Furthermore, eight system parameters are optimized using advanced techniques such as genetic algorithms, particle swarm optimization, and gray wolf optimization. These optimizations enhance the global performance of two critical stages: (1) the maximum power point tracking algorithm based on sliding mode control, which minimizes switching losses in the boost stage, and (2) the effective transfer of captured solar power to the load by optimizing the gains of a PI controller. The PI controller computes the switching triggers for the inverter stage, significantly improving the total harmonic distortion of voltage and current waveforms. Simulation results validate the proposed approach, demonstrating a marked improvement in overall system efficiency (95.8%) when compared to the incremental conductance method (−11.8%) and a baseline sliding mode control configuration (−1.14%).

Let a network be represented by a simple graph G with n vertices. A common approach to investigate properties of a network is to use the adjacency matrix A = [ a ij ] i , j = 1 n ∈ R n × n associated with the graph G , where a ij > 0 if there is an edge pointing from vertex v i to vertex v j , and a ij = 0 otherwise. Both A and its positive integer powers reveal important properties of the graph. This paper proposes to study properties of a graph G by also using the path length matrix for the graph. The ( i j ) th entry of the path length matrix is the length of the shortest path from vertex v i to vertex v j ; if there is no path between these vertices, then the value of the entry is ∞ . Powers of the path length matrix are formed by using min-plus matrix multiplication and are important for exhibiting properties of G . We show how several known measures of communication such as closeness centrality, harmonic centrality, and eccentricity are related to the path length matrix, and we introduce new measures of communication, such as the harmonic K -centrality and global K -efficiency, where only (short) paths made up of at most K edges are taken into account. The sensitivity of the global K -efficiency to changes of the entries of the adjacency matrix also is considered.

In the current industry scenario, traditional production analysis, focused on isolated operations, no longer meets market demands. Previously, the production process was viewed as a simple sum of operations, where improving one would automatically benefit the entire process. However, increasing internal competition now requires more efficient approaches for better decision-making, aimed at optimizing production processes and reducing factory costs. A common challenge is the lack of complete product traceability and the integration of production flows. Operators and managers often lack precise information on the product’s stage or location, leading to inefficiencies. To address this, modern solutions like manufacturing execution system (MES) and overall equipment effectiveness (OEE) are essential. This study, conducted in a large metal-mechanical company, targets a 12% productivity increase and a 10% reduction in non-quality annually. The implementation of MES and OEE aims to enhance synchronization, reduce downtime, optimize resource use, and improve traceability. The project involved identifying bottlenecks, setting key performance indicators, and comprehensive training. As a result, productivity increased by 8% in the last semester. The objective of this study is to demonstrate that the implementation of Industry 4.0 technology, combined with the use of OEE as a performance indicator, can result in a projected productivity increase of more than 12%. This improvement adds measurable value, making the results actionable for industry stakeholders.

In myriad biological systems, multiple lines of evidence indicate that modularity, wherein parts of a network are organized into modules such as subgroups in animal networks, may affect social transmission processes. In animal societies, there is increased interest in understanding variation in the effects of modularity on transmission as it may provide important insight into a given network's performance, in addition to the evolutionary consequences the structure of the network may have for individual fitness. Yet, to our knowledge, the degree to which network efficiency is modularity dependent has not yet been investigated in great detail in behavioral and evolutionary ecology. Here, we investigated to what degree network efficiency, as a proxy for social transmission, is modularity dependent. We created 2798 networks varying in group size and density, and tested whether network structure (density, Newman's modularity, eigenvector centralization) and group size shape network efficiency. We also used published data from 41 primate social networks to test whether the predictions generated in our simulations were supported by empirical observations. Our results show a non-linear relationship between modularity and global efficiency, with the latter peaking at intermediate values of modularity in both theoretical and empirical networks. This phenomenon may have relevance for observed variation in social structure and its link with network performance. Our results may thus provide a basis from which to discuss the evolution of complex systems such as animal societies.