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Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol

Introduction Osteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fracture risk, is a significant health concern. Traditional diagnostic methods, such as Dual-energy X-ray Absorptiometry (DXA), have limitations in sensitivity and accessibility. However, the emergence of artificial intelligence (AI) and machine learning (ML) has brought promising tools capable of analysing complex medical data to enhance the detection and prediction of osteoporosis-related bone properties. This systematic review protocol outlines the methodology to evaluate the application and effectiveness of AI and ML methods in detecting bone properties and osteoporosis. It underscores their potential to revolutionise healthcare by providing more accurate and accessible osteoporosis detection and prediction tools. Methods This systematic review, which will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines, will be comprehensive in its approach. A thorough search will be conducted across PubMed, Embase, IEEE Xplore, Scopus, Cochrane Library, and GitHub from their inception to March 2025. Studies involving adults aged 40 years and older that utilise AI/ML techniques to detect or predict bone density or other bone-related properties will be included. Two independent reviewers will perform screening, data extraction, and risk of bias assessments using appropriate tools such as RoB 2, ROBINS-I, QUADAS-2, PROBAST, and NOS. The comprehensive nature of this review ensures that no relevant study is overlooked. Data synthesis will involve narrative synthesis and, if applicable, meta-analysis using Review Manager (RevMan) and R software. Discussion This systematic review will comprehensively evaluate current AI and ML applications in detecting bone properties and osteoporosis. By identifying and analysing various AI/ML models and comparing them with traditional diagnostic methods, the review aims to highlight the effectiveness and potential of these technologies in clinical practice. The findings are expected to significantly impact healthcare professionals, researchers, and policymakers regarding advancements in AI/ML for bone health assessment and guide future research directions. Understanding the strengths and limitations of existing studies will be crucial in developing standardised protocols and facilitating the integration of AI/ML tools into routine osteoporosis screening and management. Systematic review registration This Systematic Review Protocol was registered in PROSPERO (CRD42024587326).

Stress and stiffness as predictors of shear wave velocity in peripheral nerve

Shear wave elastography (SWE) is a promising non-invasive indicator for diagnosing peripheral neuropathy. Emerging validation studies using ultrasound-based measures of shear wave velocity (SWV) in other biological tissues, such as muscle, demonstrate there is a concern of whether SWE is an accurate measure of tensile stress or stiffness. Distinguishing between these two parameters and their relationship with SWV is crucial if SWE is to be used as a biomarker for peripheral neuropathies, where changes in mechanical properties are known to occur. In this study, we use cat sciatic nerves to first evaluate SWV in situ at knee positions known to reduce (90° flexion) or increase (180° extension) stress, and then excise nerves to directly quantify the relationships between SWV, stress, and stiffness with ex vivo tensile testing. Our ex vivo findings show that although SWV can be predicted using either stress or stiffness, stress explains more variability in sciatic nerve SWV. However, while stress remains the better predictor of SWV ex vivo, within the SWV range established in situ, stiffness improves its accuracy at estimating SWV, especially when also accounting for factors related to nerve viscoelasticity. Therefore, if SWE is to be used in clinical settings as an indicator of nerve stiffness in peripheral neuropathy, it is essential to standardize parameters such as limb positioning and nerve preloading, which could potentially mask pathological changes in nerve stiffness.

Utility of UAS-LIDAR for estimating forest structural attributes of the Miombo woodlands in Zambia

The ability to collect precise three-dimensional (3D) forest structural information at a fraction of the cost of airborne light detection and ranging (lidar) makes uncrewed aerial systems-lidar (UAS-lidar) a remote sensing tool with high potential for estimating forest structural attributes for enhanced forest management. The estimation of forest structural data in area-based forest inventories relies on the relationship between field-based estimates of forest structural attributes (FSA) and lidar-derived metrics at plot level, which can be modeled using either parametric or non-parametric regression techniques. In this study, the performance of UAS-lidar metrics was assessed and applied to estimate four FSA (above ground biomass (AGB), basal area (BA), diameter at breast height (DBH), and volume (Vol)) using multiple linear regression (MLR), a parametric technique, at two wet Miombo woodland sites in the Copperbelt province of Zambia. FSA were estimated using site-specific MLR models at the Mwekera and Miengwe sites and compared with FSA estimates from generic MLR models that employed combined data from the two sites. The results revealed that the model fit of site-specific MLR models was marginally better (Adj-R2: AGB =  0.87–0.93; BA =  0.88–0.89; DBH =  0.86–0.96; and Vol =  0.87–0.98 than when using a generic combined data model (AGB =  0.80; BA =  0.81; DBH =  0.85; and Vol =  0.85). However, the rRMSE (2.01 – 20.89%) and rBias (0.01-1.03%) of site specific MLR models and combined data model rRMSE (3.40-16.71%) and rBias (0.55-1.16%) were within the same range, suggesting agreement between the site specific and combined data models. Furthermore, we assessed the applicability of a site-specific model to a different site without using local training data. The results obtained were inferior to both site-specific and combined data models (rRMSE: AGB =  36.29%–37.25%; BA =  52.98–54.52%; DBH =  55.57%–64.59%; and Vol =  26.10%–30.17%). The results obtained from this indicate potential for application in estimating FSA using UAS-lidar data in the Miombo woodlands and are a stepping stone towards sustainable local forest management and attaining international carbon reporting requirements. Further research into the performance of UAS-lidar data in the estimation of FSA under different Miombo vegetation characteristics, such as different age groups, hilly terrain, and dry Miombo, is recommended.

Cancer screening prevalence and preference among hospitalized women with and without diabetes mellitus

Objective To determine the prevalence of nonadherence to breast cancer and colorectal cancer screening, associated risk factors, and screening preference among hospitalized women with and without diabetes aged 50–75 years who were cancer-free at baseline. Methods A prospective study compared women with and without diabetes who were cancer-free (except for skin cancer) at baseline and between 50 and 75 years of age, admitted to the general medical service at an academic center were approached for study participation from December 1, 2014, to May 31, 2017. The study evaluated breast and colorectal cancer screening nonadherence prevalence, preference for screening locale, sociodemographic and clinical variables associated with nonadherence using multivariable logistic regression model. Results Of 510 women, 39% had a prior diagnosis of diabetes mellitus, and 36% were African American. Women with diabetes were more likely to have obesity, reliance on assistive devices for ambulation, inability to work (have a disability), and a greater average number of comorbidities compared to women without diabetes. Women with or without diabetes were equally nonadherent with BRC (28% vs 36%, p = 0.6) and CRC (25% vs 28%, p = 0.51) screening guidelines. After adjustment for sociodemographic and clinical risk factors, only high risk for CRC (OR = 3.20, 95%CI; 1.03–9.91) was an independent risk factor associated with nonadherence to BRC among hospitalized women with diabetes. Whereas after similar adjustment, age younger than 60 years (OR = 2.91, 95%CI; 1.15–7.35) and current or prior smoking (OR = 2.80, 95%CI; 1.14–6.86) were associated with nonadherence to CRC among women with diabetes. 46% of women with diabetes expressed a preference for in-hospital screening for BRC, while 45% expressed a similar preference for CRC. Conclusion Hospitalizations may offer additional screening opportunities as almost half of the women with diabetes preferred undergoing breast and colorectal cancer screening during a hospital stay.

Spatial distribution characteristics and influencing factors of milk tea stores in Wuhan based on sDNA and OPGD models

Milk tea stores have rapidly expanded in Wuhan due to residents’ increased consumption demand. Therefore, studying the spatial distribution and influencing factors of stores is important for optimizing their layout and promoting economic development. Using milk-tea store data from Amap City’s Point of Interest function and road network data from Baidu HeatMap, we analyzed the spatial distribution characteristics of stores within the third ring road of Wuhan City using ArcGIS. We then examined the influencing factors by combining spatial design network analysis, optimal parameters-based geographical detection, and location-based service big data. Our results revealed the following: (1) The spatial distribution of stores was concentrated in areas with high closeness and betweenness centrality, forming a multi-core “northwest–southeast” distribution pattern with significant spatial positive correlation. (2) The stores’ spatial pattern was influenced by the road network betweenness and the presence of office buildings, shopping malls, shopping centers, and tourism resources. The road network betweenness had the greatest impact on the stores’ spatial distribution, while the kernel density of betweenness presented a “one major and multiple sub-core” structure consistent with that of the stores’ spatial distribution. The kernel density of closeness and betweenness regulated the formation of the stores’ core area and multiple sub-core areas, respectively, and both factors governed the stores’ spatial distribution, which was characterized by a “widely-scattered and sporadically-clustered” pattern. (3) The stores’ distribution was closely associated with the spatial and dynamic population distribution at different times of the day. By demonstrating the big data for the spatial distribution and driving factors of milk tea stores at the urban regional scale, we fill the research gap on the spatial distribution of milk tea stores at the meso-scale. Our results offer insights into the future urban planning of milk tea stores amid the current milk tea craze.

Correction: “When I talk about it, my eyes light up!” Impacts of a national laboratory internship on community college student success

The study on the adsorption characteristics of anthracite under different temperature and pressure conditions

The study of the adsorption characteristics of coal is of great significance to gas prevention and CO2 geological storage. To explore the adsorption mechanism of coal, this study focuses on columnar anthracite. Adsorption tests on coal rock under a range of physical field conditions were conducted using the volumetric method. The adsorption characteristics of anthracite for CO2, CH4, and N2 gases under different conditions were investigated using Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) methods. The results showed that the adsorption capacities of anthracite for these three gases are in the order of CO2 > CH4 > N2, and that the adsorption capacity increases with increasing gas injection pressure. The CO2/CH4/N2 gas molecule adsorption capacity of the anthracite macromolecular structure model decreases with increasing temperature. The increase in temperature has the greatest influence on the CO2 absorption capacity, followed by the CH4 and N2 adsorption capacities. The research offers a theoretical basis for the control of coal mine gas and the geological storage of CO2.

Characterizing the genetic diversity and population structure of Plasmodium knowlesi in Aceh Province, Indonesia

As in other parts of Southeast Asia, efforts to achieve or sustain malaria elimination in Indonesia have been threatened by the emergence of human infection with the primate species P. knowlesi. To understand the transmission dynamics of this species, investigation of P. knowlesi genetic diversity and population structure is needed. A molecular surveillance study was conducted in two phases between June 2014 and September 2018 at five primary health facilities in Aceh Province, Indonesia, an area nearing malaria elimination. Dried blood spot samples were collected from patients presenting with suspected malaria and testing positive for malaria by microscopy. PCR was performed for molecular confirmation and species identification. Forty-six samples were confirmed to be P. knowlesi, of which 41 were amplified with genotyping targeting ten known P. knowlesi microsatellite markers. For samples within a site, nearly all (9 of 10 loci) or all loci were polymorphic. Across sites, multiple identical haplotypes were observed, though linkage distribution in the population was low (index of association (IAS) = 0.008). The parasite population was indicative of low diversity (expected heterozygosity [HE] =  0.63) and low complexity demonstrated by 92.7% monoclonal infections, a mean multiplicity of infection of 1.06, and a mean within-host infection fixation index (FST) of 0.05. Principal coordinate and neighbour-joining tree analyses indicated that P. knowlesi strains from Aceh were distinct from those reported in Malaysia. In a near-elimination setting in Indonesia, we demonstrate the first evidence that P. knowlesi strains were minimally diverse and were genetically distinct from Malaysian strains, suggesting highly localized transmission and limited connectivity to Malaysia. Ongoing genetic surveillance of P. knowlesi in Indonesia can inform tracking and planning of malaria control and elimination efforts.

Endozoochory by the cooperation between beetles and ants in the holoparasitic plant Cynomorium songaricum in the deserts of Northwest China

Cynomorium songaricum Rupr. first described by Carl Johann (Ivanovič) Ruprecht in 1840 is a desert parasitic plant that mainly parasitizes the roots of Nitraria L. (especially of Nitraria tangutorum Bobrov., Nitraria sibirica Pall.). During seed maturation, C. songaricum releases a distinct smell, and its seeds are round and dust-like. Previous studies indicated that most parasitic plants produce small seeds, which are primarily dispersed by the wind. Recent studies reveal the significant role of animals in the seed dispersal of parasitic plants. In this study, we combined (1) the direct observation of the seed dispersal of C. songaricum, and (2) the indoor breeding of beetles and ants to assess the viability of seeds, clarify the seed dispersal system, and explore the mechanisms by which the seeds attract dispersal agents. By a population study, we identified beetles (Mantichorula semenowi Reitter, 1888) and ants (Messor desertora He & Song, 2009) as the primary seed dispersal agents for the C. songaricum. These plants rely on the visits from these agents to transfer their seeds near the roots of the host plant, Nitraria L.. The release of a distinct volatile compound from C. songaricum seeds attracts M. semenowi and M. desertora to consume and/or transport the seeds. This study provides the first evidence of a multi-medium and inter-species seed dispersal system in the C. songaricum. This study elucidates the role of invertebrates in the seed dispersal of desert parasitic plants. We propose that the two seed dispersal agents play distinct roles in the sequential seed dispersal of C. songaricum, representing two key stages in the overall seed dispersal mechanism.

Characterization of microRNA candidates at the primary site of infectious bronchitis virus infection: A comparative study of in vitro and in vivo avian models

Infectious bronchitis virus (IBV) is an important avian pathogen with a positive-sense single-stranded RNA genome. IBV is the causative agent of infectious bronchitis (IB), a primarily respiratory disease affecting chickens, with the ability to disseminate to other organ systems, such as the gastrointestinal, renal, lymphoid, and reproductive systems. Tracheal epithelial cells are the primary target of IBV, and these cells play a vital role in the effective induction of the antiviral response and eventual clearance of IBV. The host immune system is regulated by a number of different molecular players, including micro-ribonucleic acids (microRNAs), which are small, conserved, non-coding RNA molecules that regulate gene expression of complementary messenger RNA (mRNA) sequences, resulting in gene silencing through translational repression or target degradation. The goal of this study was to characterize and compare the microRNA expression profiles in chicken tracheal epithelial cells (cTECs) in vitro and the trachea in vivo upon IBV Delmarva/1639 (DMV/1639) or IBV Massachusetts 41 (Mass41) infections. We hypothesized that IBV infection influences the expression of the host microRNA expression profiles. cTECs and young specific pathogen-free (SPF) chickens were infected with IBV DMV/1639 or IBV Mass41 and the microRNA expression at 3 and 18 hours post-infection (hpi) in the cTECs and at 4 and 11 days post-infection (dpi) in the trachea were determined using small RNA-sequencing (RNA-seq). We found that the profile of differentially expressed (DE) microRNAs is largely dependent on the IBV strain and time point of sample collection. Furthermore, we predicted the interaction between host microRNA and IBV viral RNA using microRNA-RNA interaction prediction platforms. We identified several candidate microRNAs suitable for future functional studies, such as gga-miR-155, gga-miR-1388a, gga-miR-7/7b and gga-miR-21-5p. Characterizing the interaction between IBV and the host cells at the level of microRNA regulation provides further insight into the regulatory mechanisms involved in viral infection and host defense in chickens following IBV infection.

Genetic ablation of the TET family in retinal progenitor cells impairs photoreceptor development and leads to blindness

The retina is responsible for converting light into electrical signals that, when transmitted to the brain, create the sensation of vision. The mammalian retina is epigenetically unique since the differentiation of retinal progenitor cells (RPCs) into retinal cells is accompanied by a decrease in DNA methylation in the promoters of many genes important for retinal development and function. However, the pathway responsible for DNA demethylation and its role in retinal development and function were unknown. We hypothesized that the Ten-Eleven Translocation (TET) family of dioxygenases plays a key role in this pathway. To this end, we knocked out the TET family in RPCs and characterized the TET-deficient and control retinas using various approaches including electron microscopy, electroretinogram tests, TUNEL, RNA-seq, WGBS, and 5hmC-Seal. We found that while the TET-dependent DNA demethylation pathway contributes to the development of many retinal cell types, it is the most significant contributor to rod and cone photoreceptor development and function. We found that genetic ablation of TET enzymes in RPCs prevents demethylation and the activity of genes essential for rod specification and for rod and cone maturation. Reduced activity of genes responsible for rod specification results in the TET-deficient retina being depleted of these neurons. Meanwhile, reduced activity of genes responsible for rod and cone maturation leads to the underdevelopment or complete absence of outer segments and synaptic termini in the TET-deficient photoreceptors, which results in loss of their function and leads to blindness. These function-deprived, underdeveloped photoreceptors die over time, leading to retinal dystrophy.

Atomic ionization: sd energy imbalance and Perdew–Zunger self-interaction correction energy penalty in 3d atoms

To accurately describe the energetics of transition metal systems, density functional approximations (DFAs) must provide a balanced description of s- and d- electrons. One measure of this is the sd transfer error, which has previously been defined as E ( 3 d n − 1 4 s 1 ) − E ( 3 d n − 2 4 s 2 ) . Theoretical concerns have been raised about this definition due to its evaluation of excited-state energies using ground-state DFAs. A more serious concern appears to be strong correlation in the 4s 2 configuration. Here, we define a ground-state measure of the sd energy imbalance, based on the errors of s- and d-electron second ionization energies of the 3d atoms, that effectively circumvents the aforementioned problems. We find an improved performance as we move from the local spin density approximation (LSDA) to the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) to the regularized and restored Strongly Constrained and Appropriately Normed (r 2 SCAN) meta-GGA for first-row transition metal atoms. However, we find large (∼2 eV) ground-state sd energy imbalances when applying a Perdew–Zunger 1981 self-interaction correction. This is attributed to an “energy penalty” associated with the noded 3d orbitals. A local scaling of the self-interaction correction to LSDA results in a balance of s- and d-errors.

Scaling language model size yields diminishing returns for single-message political persuasion

Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 US political issues from 24 language models spanning several orders of magnitude in size. We then deploy these messages in a large-scale randomized survey experiment ( N = 25,982) to estimate the persuasive capability of each model. Our findings are twofold. First, we find evidence that model persuasiveness is characterized by sharply diminishing returns, such that current frontier models are only slightly more persuasive than models smaller in size by an order of magnitude or more. Second, we find that the association between language model size and persuasiveness shrinks toward zero and is no longer statistically significant once we adjust for mere task completion (coherence, staying on topic), a pattern that highlights task completion as a potential mediator of larger models’ persuasive advantage. Given that current frontier models are already at ceiling on this task completion metric in our setting, taken together, our results suggest that further scaling model size may not much increase the persuasiveness of static LLM-generated political messages.

Visualizing agonist-induced M2 receptor activation regulated by aromatic ring dynamics

Despite the growing number of G protein–coupled receptor (GPCR) structures being resolved, the dynamic process of how GPCRs transit from the inactive toward the active state remains unclear. In this study, comprehensive molecular dynamics simulations were performed to explore how ligand binding modulates the conformational dynamics of the M2 muscarinic acetylcholine receptor (M2R). We observed a sequential occurrence of structural changes in the inactive-to-active transition of M2R induced by a superagonist iperoxo, which includes the orthosteric binding site contraction, the TM6 opening into an intermediate conformation, and a further structural change toward full activation upon binding to G protein or a G protein mimetic nanobody. Two activation intermediates were identified, which show structural features different from those reported for apo-GPCRs. Moreover, our results suggest that stabilization of a specific W400 6.48 conformation and enhanced F396 6.44 dynamics are crucial for activation, whereas distinct side-chain rotamer equilibriums of Y206 5.58 in the cytoplasmic cavity are correlated with agonist efficacies. Our work provides atomic-level structural insights into the agonist-induced M2R activation pathway and highlights a mechanism by which ligand efficacy can be encoded and transduced in the form of aromatic ring dynamics.

Bacterial estrogenesis without oxygen: Wood–Ljungdahl pathway likely contributed to the emergence of estrogens in the biosphere

Androgen and estrogen, key sex hormones, were long thought to be exclusively produced by vertebrates. The O 2 -dependent aromatase that converts androgen to estrogen (estrogenesis) has never been identified in any prokaryotes. Here, we report the finding of anaerobic estrogenesis in a Peptococcaceae bacterium ( Phosphitispora sp. strain TUW77) isolated from the gut of the great blue-spotted mudskipper ( Boleophthalmus pectinirostris ). This strain exhibits testosterone fermentation pathways, transforming testosterone into estrogens and androstanediol under anaerobic conditions. Physiological experiments revealed that strain TUW77 grows exclusively on testosterone, utilizing the androgenic C-19 methyl group as both the carbon source and electron donor. The genomic analysis identified three copies of a polycistronic gene cluster, abeABC (anaerobic bacterial estrogenesis), encoding components of a classic cobalamin-dependent methyltransferase system. These genes, highly expressed under testosterone-fed conditions, show up to 57% protein identity to the characterized EmtAB from denitrifying Denitratisoma spp., known for methylating estrogen into androgen (the reverse reaction). Tiered transcriptomic and proteomic analyses suggest that the removed C-19 methyl group is completely oxidized to CO 2 via the oxidative Wood–Ljungdahl pathway (WLP), while the reducing equivalents (NADH) fully reduce remaining testosterone to androstanediol. Consistently, the addition of anthraquinone-2,6-disulfonate, an extracellular electron acceptor, to testosterone-fed TUW77 cultures enabled complete testosterone conversion into estrogen without androstanediol accumulation (anaerobic testosterone oxidation). This finding of aromatase-independent estrogenesis in anaerobic bacteria suggests that the ancient WLP may have contributed to the emergence of estrogens in the early biosphere.

Candidate transmission survival genome of <i>Mycobacterium tuberculosis</i>

Mycobacterium tuberculosis (Mtb), a leading cause of death from infection, completes its life cycle entirely in humans except for transmission through the air. To begin to understand how Mtb survives aerosolization, we mimicked liquid and atmospheric conditions experienced by Mtb before and after exhalation using a model aerosol fluid (MAF) based on the water-soluble, lipidic, and cellular constituents of necrotic tuberculosis lesions. MAF induced drug tolerance in Mtb, remodeled its transcriptome, and protected Mtb from dying in microdroplets desiccating in air. Yet survival was not passive: Mtb appeared to rely on hundreds of genes to survive conditions associated with transmission. Essential genes subserving proteostasis offered most protection. A large number of conventionally nonessential genes appeared to contribute as well, including genes encoding proteins that resemble antidesiccants. The candidate transmission survival genome of Mtb may offer opportunities to reduce transmission of tuberculosis.

Structure of activity in multiregion recurrent neural networks

Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to underlie computational flexibility, yet the structure of multiregion neural activity and its origins in synaptic connectivity remain poorly understood. We investigate recurrent neural networks with multiple regions, each containing neurons with random and structured connections. Inspired by experimental evidence of communication subspaces, we use low-rank connectivity between regions to enable selective activity routing. These networks exhibit high-dimensional fluctuations within regions and low-dimensional signal transmission between them. Using dynamical mean-field theory, with cross-region currents as order parameters, we show that regions act as both generators and transmitters of activity—roles that are often in tension. Taming within-region activity can be crucial for effective signal routing. Unlike previous models that suppressed neural activity to control signal flow, our model achieves routing by exciting different high-dimensional activity patterns through connectivity structure and nonlinear dynamics. Our analysis of this disordered system offers insights into multiregion neural data and trained neural networks.

Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics

Information processing in the human brain can be modeled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of nonequilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analyzing neural recordings using network analysis of time-series. Our approach constructs directed multilayer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of nonequilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions while for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most nonequilibrium interactions are between cognitive–sensorial pairs alongside medial regions. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organization of neural dynamics from the perspective of brain network dynamics.

Input-driven circuit reconfiguration in critical recurrent neural networks

Changing a circuit dynamically, without actually changing the hardware itself, is called reconfiguration, and is of great importance due to its manifold technological applications. Circuit reconfiguration appears to be a feature of the cerebral cortex, so understanding the dynamical principles underlying self-reconfiguration may prove of import to elucidate brain function. We present a very simple example of dynamical reconfiguration: a family of networks whose signal pathways can be switched on the fly, only through use of their inputs, with no changes to their synaptic weights. These are single-layer convolutional recurrent network with local unitary synaptic weights and a smooth sigmoidal activation function. We generate traveling waves using the high spatiotemporal frequencies of the input, and we use the low spatiotemporal frequencies of the input to landscape the ongoing activity, channeling said traveling waves through an input-specified spatial pattern. This mechanism uses inherent properties of marginally stable, dynamically critical systems, which are a direct consequence of their unitary convolution kernels: every network in the family can do this. We show these networks solve the classical connectedness detection problem, by allowing signal propagation only along the regions to be evaluated for connectedness, and forbidding it elsewhere.

Evolution of the <i>JULGI–SMXL4/5</i> module for phloem development in angiosperms

Bifacial cambium, which produces xylem and phloem, and monopodial architecture, characterized by apical dominance and lateral branching from axillary buds, are key developmental features of seed plants, consisting of angiosperms and gymnosperms. These allow seed plants to adapt to diverse environments by optimizing resource allocation and structural integrity. In seed plants, SUPPRESSOR OF MAX2-LIKE ( SMXL ) family members function in phloem development and strigolactone-induced inhibition of axillary bud outgrowth. Although strigolactone signaling regulates most SMXL family members, the only known regulator of SMXL4 and SMXL5 is the RNA-binding protein JULGI. We demonstrate that in angiosperms, by directly regulating SMXL4/5 expression, JULGI uncouples SMXL4/5 activity from strigolactone signaling. JULGI and ancestral SMXL s from seedless vascular plants or SMXL4/5 from seed plants are coexpressed in the phloem tissues of vascular plants, from lycophytes to angiosperms. Core angiosperm SMXL4/5 mRNAs contain a G-rich element in the 5′ untranslated region (UTR) that serves as a target sequence for JULGI to negatively regulate SMXL4/5 expression. Heterologous expression of JULGI s from various angiosperms rescued the Arabidopsis jul1 jul2 mutant. Expressing SMXL4/5 s from seed plants and ancestral SMXL s rescued Arabidopsis smxl4 smxl5 . Angiosperm SMXL4/5s lack an RGKT motif for proteasomal degradation. Indeed, treatment with the synthetic strigolactone analog rac -GR24 induced proteasomal degradation of SMXL from ferns and SMXL5a from gymnosperms, but not SMXL4/5 from angiosperms. These findings suggest that in ancestral angiosperms, the 5′ UTR of SMXL4/5 gained G-rich elements, creating a regulatory module with JULGI that allows the phloem development pathway to act independently of strigolactone signaling.