Cycling reduces the entropy of neuronal activity in the human adult cortex.

Brain Complexity (BC) have successfully been applied to study the brain electroencephalographic signal (EEG) in health and disease. In this study, we employed recurrence entropy to quantify BC associated with the neurophysiology of movement by comparing BC in both resting state and cycling movement. We measured EEG in 24 healthy adults and placed the electrodes on occipital, parietal, temporal and frontal sites on both the right and left sides of the brain. We computed the recurrence entropy from EEG measurements during cycling and resting states. Entropy is higher in the resting state than in the cycling state for all brain regions analysed. This reduction in complexity is a result of the repetitive movements that occur during cycling. These movements lead to continuous sensorial feedback, resulting in reduced entropy and sensorimotor processing.

Clinicogenetic characterization of cerebrotendinous xanthomatosis in Brazil.

There are few cerebrotendineous xanthomatosis (CTX) case series and observational studies including a significant number of Latin American patients. We describe a multicenter Brazilian cohort of patients with CTX highlighting their clinical phenotype, recurrent variants and assessing possible genotype-phenotype correlations. We analyzed data from all patients with clinical and molecular or biochemical diagnosis of CTX regularly followed at six genetics reference centers in Brazil between March 2020 and August 2023. We evaluated 38 CTX patients from 26 families, originating from 4 different geographical regions in Brazil. Genetic analysis identified 13 variants in the CYP27A1 gene within our population, including 3 variants that had not been previously described. The most frequent initial symptom of CTX in Brazil was cataract (27%), followed by xanthomas (24%), chronic diarrhea (13.5%), and developmental delay (13.5%). We observed that the median age at loss of ambulation correlates with the age of onset of neurological symptoms, with an average interval of 10 years (interquartile range 6.9 to 11 years). This study represents the largest CTX case series ever reported in South America. We describe phenotypic characteristics and report three new pathogenic or likely pathogenic variants.

Clinical, socio-demographic, and parental correlates of early autism traits in a community cohort of toddlers.

Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample.

Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment.

Brain dynamic functional connectivity characterises transient connections between brain regions. Features of brain dynamics have been linked to emotion and cognition in adult individuals, and atypical patterns have been associated with neurodevelopmental conditions such as autism. Although reliable functional brain networks have been consistently identified in neonates, little is known about the early development of dynamic functional connectivity. In this study we characterise dynamic functional connectivity with functional magnetic resonance imaging (fMRI) in the first few weeks of postnatal life in term-born (n = 324) and preterm-born (n = 66) individuals. We show that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain, characterised by six transient states of neonatal functional connectivity with changing dynamics through the neonatal period. The pattern of dynamic connectivity is atypical in preterm-born infants, and associated with atypical social, sensory, and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT) scores at 18 months of age.

From subcritical behavior to a correlation-induced transition in rumor models.

Rumors and information spreading emerge naturally from human-to-human interactions and have a growing impact on our everyday life due to increasing and faster access to information, whether trustworthy or not. A popular mathematical model for spreading rumors, data, or news is the Maki-Thompson model. Mean-field approximations suggested that this model does not have a phase transition, with rumors always reaching a fraction of the population. Conversely, here, we show that a continuous phase transition is present in this model. Moreover, we explore a modified version of the Maki-Thompson model that includes a forgetting mechanism, changing the Markov chain's nature and allowing us to use a plethora of analytic and numeric methods. Particularly, we characterize the subcritical behavior, where the lifespan of a rumor increases as the spreading rate drops, following a power-law relationship. Our findings show that the dynamic behavior of rumor models is much richer than shown in previous investigations.

Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies.

Infants born in early term (37-38 weeks gestation) experience slower neurodevelopment than those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor-based morphometry and tract-based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.

Are we there yet? The long walk towards the development of efficient symbiotic associations between nitrogen-fixing bacteria and non-leguminous crops.

Nitrogen is an essential element of life, and nitrogen availability often limits crop yields. Since the Green Revolution, massive amounts of synthetic nitrogen fertilizers have been produced from atmospheric nitrogen and natural gas, threatening the sustainability of global food production and degrading the environment. There is a need for alternative means of bringing nitrogen to crops, and taking greater advantage of biological nitrogen fixation seems a logical option. Legumes are used in most cropping systems around the world because of the nitrogen-fixing symbiosis with rhizobia. However, the world's three major cereal crops-rice, wheat, and maize-do not associate with rhizobia. In this review, we will survey how genetic approaches in rhizobia and their legume hosts allowed tremendous progress in understanding the molecular mechanisms controlling root nodule symbioses, and how this knowledge paves the way for engineering such associations in non-legume crops. We will also discuss challenges in bringing these systems into the field and how they can be surmounted by interdisciplinary collaborations between synthetic biologists, microbiologists, plant biologists, breeders, agronomists, and policymakers.

Multicenter validation of automated trajectories for selective laser amygdalohippocampectomy.

OBJECTIVE: Laser interstitial thermal therapy (LITT) is a novel minimally invasive alternative to open mesial temporal resection in drug-resistant mesial temporal lobe epilepsy (MTLE). The safety and efficacy of the procedure are dependent on the preplanned trajectory and the extent of the planned ablation achieved. Ablation of the mesial hippocampal head has been suggested to be an independent predictor of seizure freedom, whereas sparing of collateral structures is thought to result in improved neuropsychological outcomes. We aim to validate an automated trajectory planning platform against manually planned trajectories to objectively standardize the process. METHODS: Using the EpiNav platform, we compare automated trajectory planning parameters derived from expert opinion and machine learning to undertake a multicenter validation against manually planned and implemented trajectories in 95 patients with MTLE. We estimate ablation volumes of regions of interest and quantify the size of the avascular corridor through the use of a risk score as a marker of safety. We also undertake blinded external expert feasibility and preference ratings. RESULTS: Automated trajectory planning employs complex algorithms to maximize ablation of the mesial hippocampal head and amygdala, while sparing the parahippocampal gyrus. Automated trajectories resulted in significantly lower calculated risk scores and greater amygdala ablation percentage, whereas overall hippocampal ablation percentage did not differ significantly. In addition, estimated damage to collateral structures was reduced. Blinded external expert raters were significantly more likely to prefer automated to manually planned trajectories. SIGNIFICANCE: Retrospective studies of automated trajectory planning show much promise in improving safety parameters and ablation volumes during LITT for MTLE. Multicenter validation provides evidence that the algorithm is robust, and blinded external expert ratings indicate that the trajectories are clinically feasible. Prospective validation studies are now required to determine if automated trajectories translate into improved seizure freedom rates and reduced neuropsychological deficits.

Mediation of plant-mycorrhizal interaction by a lectin receptor-like kinase.

The molecular mechanisms underlying mycorrhizal symbioses, the most ubiquitous and impactful mutualistic plant-microbial interaction in nature, are largely unknown. Through genetic mapping, resequencing and molecular validation, we demonstrate that a G-type lectin receptor-like kinase (lecRLK) mediates the symbiotic interaction between Populus and the ectomycorrhizal fungus Laccaria bicolor. This finding uncovers an important molecular step in the establishment of symbiotic plant-fungal associations and provides a molecular target for engineering beneficial mycorrhizal relationships.

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach.

Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE.

Subsystem organization of axonal connections within and between the right and left cerebral cortex and cerebral nuclei (endbrain).

The endbrain (telencephalon) is at the rostral end of the central nervous system and is primarily responsible for supporting cognition and affect. Structurally, it consists of right and left cerebral hemispheres, each parceled into multiple cortical and nuclear gray matter regions. The global network organization of axonal macroconnections between the 244 regions forming the endbrain was analyzed with a multiresolution consensus clustering (MRCC) method that provides a hierarchical description of community clustering (modules or subsystems) within the network. Experimental evidence was collated from the neuroanatomical literature for the existence of 10,002 of a possible 59,292 connections within the network, and they cluster into four top-level subsystems and 60 bottom-level subsystems arranged in a 50-level hierarchy. Two top-level subsystems are bihemispheric: One deals with auditory and visual information, and the other corresponds broadly to the default mode network. The other two top-level subsystems are bilaterally symmetrical, and each deals broadly with somatic and visceral information. Because the entire endbrain connection matrix was assembled from multiple subconnectomes, it was easy to show that the status of a region as a connectivity hub is not absolute but, instead, depends on the size and coverage of its anatomical neighborhood. It was also shown numerically that creating an ultradense connection matrix by converting all "absent" connections to a "very weak" connection weight has virtually no effect on the clustering hierarchy. The next logical step in this project is to complete the forebrain connectome by adding the thalamus and hypothalamus (together, the interbrain) to the endbrain analysis.

Multiresolution Consensus Clustering in Networks.

Networks often exhibit structure at disparate scales. We propose a method for identifying community structure at different scales based on multiresolution modularity and consensus clustering. Our contribution consists of two parts. First, we propose a strategy for sampling the entire range of possible resolutions for the multiresolution modularity quality function. Our approach is directly based on the properties of modularity and, in particular, provides a natural way of avoiding the need to increase the resolution parameter by several orders of magnitude to break a few remaining small communities, necessitating the introduction of ad-hoc limits to the resolution range with standard sampling approaches. Second, we propose a hierarchical consensus clustering procedure, based on a modified modularity, that allows one to construct a hierarchical consensus structure given a set of input partitions. While here we are interested in its application to partitions sampled using multiresolution modularity, this consensus clustering procedure can be applied to the output of any clustering algorithm. As such, we see many potential applications of the individual parts of our multiresolution consensus clustering procedure in addition to using the procedure itself to identify hierarchical structure in networks.

A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers.

OBJECTIVE: To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. METHOD: Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers' spike identification and individual spike class labels visually and quantitatively. RESULTS: The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. CONCLUSIONS: WC performance is indistinguishable to that of EEG reviewers' suggesting it could be a valid clinical tool for the assessment of IEDs. SIGNIFICANCE: WC can be used to provide quantitative analysis of epileptic spikes.

Glycobiology Modifications in Intratumoral Hypoxia: The Breathless Side of Glycans Interaction.

Post-translational and co-translational enzymatic addition of glycans (glycosylation) to proteins, lipids, and other carbohydrates, is a powerful regulator of the molecular machinery involved in cell cycle, adhesion, invasion, and signal transduction, and is usually seen in both in vivo and in vitro cancer models. Glycosyltransferases can alter the glycosylation pattern of normal cells, subsequently leading to the establishment and progression of several diseases, including cancer. Furthermore, a growing amount of research has shown that different oxygen tensions, mainly hypoxia, leads to a markedly altered glycosylation, resulting in altered glycan-receptor interactions. Alteration of intracellular glucose metabolism, from aerobic cellular respiration to anaerobic glycolysis, inhibition of integrin 3α1ß translocation to the plasma membrane, decreased 1,2-fucosylation of cell-surface glycans, and galectin overexpression are some consequences of the hypoxic tumor microenvironment. Additionally, increased expression of gangliosides carrying N-glycolyl sialic acid can also be significantly affected by hypoxia. For all these reasons, it is possible to realize that hypoxia strongly alters glycobiologic events within tumors, leading to changes in their behavior. This review aims to analyze the complexity and importance of glycoconjugates and their molecular interaction network in the hypoxic context of many solid tumors.

Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

The rho-kinase inhibitors Y-27632 and fasudil act synergistically with imatinib to inhibit the expansion of ex vivo CD34(+) CML progenitor cells.

Evidence from cell line-based studies indicates that rho-kinase may play a role in the leukaemic transformation of human cells mediated by the BCR/ABL tyrosine kinase, manifest clinically as chronic myeloid leukaemia (CML). We therefore employed two separate inhibitors, Y-27632 and fasudil, to inhibit the activity of rho-kinase against ex vivo CD34(+) cells collected from patients with CML. We compared the effects of rho-kinase inhibition in those cells with the effects of direct inhibition of BCR/ABL using the specific inhibitor imatinib. We found that inhibition of rho-kinase inhibited the effective proliferation, and reduced survival of CML progenitor cells. When combined with imatinib, rho-kinase inhibition added to the anti-proliferative and pro-apoptotic effects of the BCR/ABL inhibitor. Our studies may indicate therapeutic benefit in some cases for the combination of rho-kinase inhibitors with imatinib.

Clinical, biochemical, and immunohistochemical features of necrobiotic xanthogranulomatosis.

AIMS: To describe the clinical features of two patients with paraproteinaemia and necrobiotic xanthogranulomatosis together with detailed immunohistochemistry of the lesions in one. METHODS: The clinical history and results of biochemical investigations of the patients were retrieved from the files. Immunohistochemistry was used to investigate the expression of macrophage and mast cell markers, amyloid A and P, S-100 protein, and apolipoprotein AI and B in xanthogranulomatous skin lesions from patient 2. In addition, protein A-sepharose chromatography was used to separate serum from patient 2 and apolipoprotein B and the IgG paraprotein were measured in the fractions eluted. RESULTS: Monocytes/macrophages comprised the major cellular component of the lesion, and unusually for xanthomata, areas of collagen necrosis were also seen. Activated mast cells were present at the margins of macrophage clusters and adjacent to areas of collagen necrosis. Serum paraprotein was bound to low density lipoproteins as judged by protein A-sepharose chromatography, and was also located within macrophagic foam cells of the lesion on immunohistochemistry. CONCLUSIONS: These observations demonstrate many features similar to atherosclerosis including collagen necrosis and mast cell activation.

The effect of Bcr-Abl protein tyrosine kinase on maturation and proliferation of primitive haematopoietic cells.

BACKGROUND: Chronic Myeloid Leukaemia (CML) is characterised by the chromosomal translocation resulting in expression of the Bcr-Abl protein tyrosine kinase (PTK) in early stem cells and their progeny. However the precise nature of Bcr-Abl effects in primitive CML stem cells remains a matter of active debate. MATERIALS AND METHODS: Extremely primitive Bcr-Abl fusion positive cells were purified from patients with CML using multiparameter flow cytometric analysis of CD34, Thy, and lineage marker (Lin) expression, plus rhodamine-123 (Rh-123) brightness. Progenitor cells of increasing maturity were examined for cycling status by flow cytometry and their proliferative status directly correlated with cell phenotype. The activation status of a key transcription factor, signal transducers and activators of transcription (STAT-5), was also analyzed by immunocytochemistry. RESULTS: The most primitive stem cells currently defined (CD34+Lin-Thy+ Rh-1231o) were present as a lower proportion of the stem cell compartment (CD34+Lin-) of CML patients at presentation than of normal individuals (2.3% +/- 0.4 compared with 5.1% +/- 0.6 respectively). Conversely there was a significantly higher proportion of the more mature cells (CD34+Lin-Thy-Rh-123 hi) in CML patients than in normal individuals (79.3 +/- 1.8 compared with 70.9 +/- 3.3). No primitive subpopulation of CML CD34+Lin- cells was cycling to a significantly greater degree than cells from normal donors, in fact, late progenitor cells (CD34+Lin+) were cycling significantly less in CML samples than normal samples. STAT5, however, was observed to be activated in CML cells. CONCLUSIONS: We conclude that no subpopulation of CML stem cells displays significantly increased cell cycling. Thus, increased cycling cannot be a direct consequence of Bcr-Abl PTK acquisition in highly enriched stem cells from patients with CML. In vivo CML need not be considered a disease of unbridled stem cell proliferation, but a subtle defect in the balance between self renewal and maturation.