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In the Western Antarctic Peninsula (WAP), marine plankton dynamics are tightly linked to the interannual variability in environmental conditions, including phenological shifts in sea‐ice seasonality. To explore these linkages, we use a 1‐dimensional vertical ocean‐ice‐ecosystem model (KPP‐Eco‐Ice, or KEI) that simulates physical and ecosystem conditions at a continental shelf mooring location in the Palmer Long Term Ecological Research program sampling grid. KEI allows for year‐round examination of the ecosystem in a region where in situ observations on the shelf are limited to January. Comparisons are made between seasonal sea‐ice retreat, mixed layer depth, primary productivity, and phytoplankton relative abundance, grazing, and loss rates. KEI successfully captures seasonal patterns in the WAP, demonstrating that total seasonal primary production was highest following a winter with late sea‐ice retreat. Stability in the surface mixed layer enables high photosynthetic rates by alleviating light limitation, while wind‐induced surface mixing results in lower phytoplankton production and biomass in years with early sea‐ice retreat. However, mixing reduces iron limitation in surface waters, which may influence phytoplankton species composition. Small, non‐diatom phytoplankton are better‐adapted to high light and low iron conditions, thriving longer in a year with late sea‐ice retreat and higher seasonal primary production, while larger diatoms are more abundant in the years with early sea‐ice retreat and lower seasonal production. These findings have implications for grazer populations and subsequent carbon export from the surface to depth in the WAP region. This study validates the role that sea ice plays in shaping Antarctic ecosystem dynamics. 

The Southern Ocean plays a vital role in global CO₂uptake, but the magnitude and even the sign of the flux remain uncertain, and the influence of phytoplankton phenology is underexplored. This study focuses on the West Antarctic Peninsula, a region experiencing rapid climate change, to examine shifts in seasonal carbon uptake. Using 20 years of in situ air‐sea CO₂flux and satellite‐derived Chlorophyll‐a, we observe that the seasonal cycles of both air‐sea CO₂flux and Chlorophyll‐a intensify poleward. The amplitude of the seasonal cycle of the non‐thermal component of surface ocean pCO₂increases with increasing latitude, while the amplitude of the thermal component remains relatively stable. Pronounced biological uptake occurs over the shelf in austral summer despite reduced CO₂solubility in warmer waters, which typically limits carbon uptake through physical processes. These findings underscore the prominence of biological mechanisms in regulating carbon fluxes in this rapidly changing region. 

ABSTRACT Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro‐scale level as measured by resting‐state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel‐wise network‐specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level‐dependent (BOLD) signal for 6–8 s, whereas the visual network propagates much quicker. We also show that summaries of network‐specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large‐scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia. 

IntroductionTypical adolescent neurodevelopment is marked by decreases in grey matter (GM) volume, increases in myelination, measured by fractional anisotropy (FA), and improvement in cognitive performance. MethodsTo understand how epigenetic changes, methylation (DNAm) in particular, may be involved during this phase of development, we studied cognitive assessments, DNAm from saliva, and neuroimaging data from a longitudinal cohort of normally developing adolescents, aged nine to fourteen. We extracted networks of methylation with patterns of correlated change using a weighted gene correlation network analysis (WCGNA). Modules from these analyses, consisting of co-methylation networks, were then used in multivariate analyses with GM, FA, and cognitive measures to assess the nature of their relationships with cognitive improvement and brain development in adolescence. ResultsThis longitudinal exploration of co-methylated networks revealed an increase in correlated epigenetic changes as subjects progressed into adolescence. Co-methylation networks enriched for pathways involved in neuronal systems, potassium channels, neurexins and neuroligins were both conserved across time as well as associated with maturation patterns in GM, FA, and cognition. DiscussionOur research shows that correlated changes in the DNAm of genes in neuronal processes involved in adolescent brain development that were both conserved across time and related to typical cognitive and brain maturation, revealing possible epigenetic mechanisms driving this stage of development. 

ABSTRACT With the increasing availability of large‐scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross‐modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross‐modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities—structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI—in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age‐associated decline, schizophrenia‐related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality‐specific brain‐age deltas were evaluated for association with non‐imaging variables. In addition, each set of linked sources reveals a corresponding set of cross‐modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age‐related sources replicates across two independent and separately analyzed datasets. The same set also presented age‐adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset. 

Introduction:Adolescence, a critical phase of human neurodevelopment, is marked by a tremendous reorganization of the brain and accompanied by improved cognitive performance. This development is driven in part by gene expression, which in turn is partly regulated by DNA methylation (DNAm). Methods:We collected brain imaging, cognitive assessments, and DNAm in a longitudinal cohort of approximately 200 typically developing participants, aged 9–14. This data, from three time points roughly 1 year apart, was used to explore the relationships between seven cytosine–phosphate–guanine (CpG) sites in genes highly expressed in brain tissues (GRIN2D,GABRB3,KCNC1,SLC12A9,CHD5,STXBP5, andNFASC), seven networks of grey matter (GM) volume change, and scores from seven cognitive tests. Results:The demethylation of the CpGs as well as the rates of change in DNAm were significantly related to improvements in total, crystalized, and fluid cognition scores, executive function, episodic memory, and processing speed, as well as several networks of GM volume increases and decreases that highlight typical patterns of brain maturation. Discussion:Our study provides a first look at the DNAm of genes involved in myelination, excitatory and inhibitory receptors, and connectivity, how they are related to the large-scale changes occurring in the brain structure as well as cognition during adolescence. 

Abstract Interactions between phytoplankton and bacteria play critical roles in shaping marine ecosystems. However, the intricate relationships within these communities—particularly in rapidly changing polar environments—remain poorly understood. We use targeted methods to directly characterize the microbiomes of individual colonies ofPhaeocystis antarctica, a keystone phytoplankton species in the Southern Ocean, and showed that colony microbiomes were consistent across individual colonies collected 108 nautical miles apart. These results suggest that hosting specific colony microbiomes is a shared trait across colony‐formingPhaeocystisspecies, with different species hosting colony microbiomes suited to their respective environments. The bacterial orders Alteromonadales, Oceanospirillales, and Sphingomonadales dominated the microbiomes of all field‐collectedP. antarcticacolonies. The relative abundances of bacterial taxa comprising the majority of field‐collected colony microbiomes—for example,Paraglaciecolasp. (Alteromonadales) and Nitrincolaceae (Oceanospirillales)—correlated withPhaeocystisabundance in surface waters, highlighting their potential roles in bloom dynamics and carbon cycling. After a year of laboratory culture, we observed a reduction in colony microbiome diversity, and Caulobacterales, Cellvibrionales, and Rhodobacterales dominated the cultured colony microbiomes. Notably, abundant genera in field‐collected colony microbiomes that were lost in culture were psychrophiles. The shift in microbiome structure emphasizes the importance of field‐based studies to capture the complexity of microbial interactions, especially for species from polar environments that are difficult to replicate in laboratory conditions. This research provides valuable insights into the ecological significance of prokaryotic interactions with a key phytoplankton species and underscores the necessity of considering these dynamics in the context of climate‐driven shifts in marine ecosystems. 

A bstract We explore the possibility that dark matter is a pair of vector-like fermionic SU(2) L doublets and propose a novel mechanism of dark matter production that proceeds through the confinement of the weak sector of the Standard Model. This confinement phase causes the Standard Model doublets and dark matter to confine into pions. The dark pions freeze-out before the weak sector deconfines and generate a relic abundance of dark matter. We solve the Boltzmann equations for this scenario to determine the scale of confinement and constituent dark matter mass required to produce the observed relic density. We determine which regions of this parameter space evade direct detection, collider bounds, and successfully produce the observed relic density of dark matter. For a TeV scale pair of vector-like fermionic SU(2) L doublets, we find the weak confinement scale to be ∼ 700 TeV. 

Background Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance. Methods Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score. Results Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem. Conclusion The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ.