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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. 

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. 

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. 

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. 

Abstract Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data. 

As the oyster aquaculture industry grows and becomes incorporated into management practices, it is important to understand its effects on local environments. This study investigated how water quality and hydrodynamics varied among farms as well as inside versus outside the extent of caged grow-out areas located in southern Chesapeake Bay. Current speed and water quality variables (chlorophyll-a fluorescence, turbidity, and dissolved oxygen) were measured along multiple transects within and adjacent to four oyster farms during two seasons. At the scale of individual aquaculture sites, we were able to detect statistically significant differences in current speed and water quality variables between the areas inside and outside the farms. However, the magnitudes of the water quality differences were minor. Differences between sites and between seasons for water quality variables were typically an order of magnitude greater than those observed within each site (i.e. inside and outside the farm footprint). The relatively small effect of the presence of oysters on water quality is likely attributable to a combination of high background variability, relatively high flushing rates, relatively low oyster density, and small farm footprints. Minimal impacts overall suggest that low-density oyster farms located in adequately-flushed areas are unlikely to negatively impact local water quality. 

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.