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Although internalizing problems are the most common forms of psychological distress among adolescents and young adults, they have precursors in multiple risk domains established during childhood. This study examined cascading risk pathways leading to depression and anxiety symptoms in emerging adulthood by integrating broad contextual (i.e., multiple contextual risks), parental (i.e., negative parenting), and child (i.e., internalizing behaviors) characteristics in early and middle childhood. We also compared common and differential pathways to depression and anxiety symptoms depending on the conceptualization of symptom outcomes (traditional symptom dimension vs. bifactor dimensional model). Participants were 235 children (109 girls) and their families. Data were collected at 3, 6, 10, and 19 years of child age, using multiple informants and contexts. Results from a symptom dimension approach indicated mediation pathways from early childhood risk factors to depression and anxiety symptoms in emerging adulthood, suggesting common and distinct risk processes between the two disorders. Results from a bifactor modeling approach indicated several indirect pathways leading to a general internalizing latent factor, but not to symptom-specific (i.e., depression, anxiety) latent factors. Our findings highlighted comparative analytic approaches to examining transactional processes associated with later internalizing symptoms and shed light on issues of early identification and prevention.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

While it is normal to be scared of things, some children are so scared that it disrupts their lives. This is called an anxiety disorder. To help children with anxiety disorders, we need to know how anxiety disorders start. From other research we knew that toddlers who avoid new situations are more likely to become anxious later in life. They are more likely, which does not mean they all become more anxious, just some of them. We wanted to know if brain activity could predict which children would become anxious. We studied avoidance in toddlers and invited them back as preteens. We studied their brains while they were resting and while they were making mistakes in front of somebody else. We found that brain responses to mistakes predicted which avoidant toddlers became very anxious as preteens. This helps us understand who might develop anxiety, so that we can help!

This work evaluates two distinct calculations of central tendency for sea-ice thickness and quantifies the impact such calculations have on ice volume for the Southern Ocean. The first calculation, area-weighted average thickness, is computed from polygonal ice features and then upscaled to regions. The second calculation, integrated thickness, is a measure of the central value of thickness categories tracked across different scales and subsequently summed to chosen regions. Both methods yield the same result from one scale to the next, but subsequent scales develop diverging solutions when distributions are strongly non-Gaussian. Data for this evaluation are sea-ice stage-of-development records from US National Ice Center ice charts from 1995 to 1998, as proxy records of ice thickness. Results show regionally integrated thickness exceeds area-weighted average thickness by as much as 60% in summer with as few as five bins in thickness distribution. Year-round, the difference between the two calculations yields volume differences consistently >10%. The largest discrepancies arise due to bimodal distributions which are common in ice charts based on current subjective-analysis protocols. We recommend that integrated distribution be used for regional-scale sea-ice thickness and volume estimates from ice charts and encourage similar testing of other large-scale thickness data archives.

We explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and >1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er= bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats.

BackgroundQuality standards (QS) for people with multiple sclerosis (PwMS) are suboptimal, ambiguous and restricted to certain patient subgroups and care pathways.AimDevelop and pilot MS metrics measuring service provision quality to identify areas for improvement.MethodsA multidisciplinary Working Group developed clinician and patient-reported metrics and standardised data collection forms through an iterative process.ResultsMetrics covered: referral; diagnosis; treatment; annual review; general management; education. Pilot (n=76) showed: 31% of PwMS were referred to MS specialist within 4 weeks of suspected/confirmed demyelination; 28% and 56% had uncomplicated MS confirmed and were offered specialist MS nurse appointment, respectively, within 4 weeks of specialist referral; 75% of eligible PwMS were offered disease-modifying therapy within 8 weeks of confirmed MS diagnosis; 85% had comprehensive multidisciplinary team (MDT) annual review; 90% had a defined point of contact within the MS service; 86% of unscheduled contacts by PwMS, MDT or general practitioners were responded to within 3 days; 53% of MS services maintained a single database of PwMS; and 76% of PwMS were offered ongoing education. Data collection continues and updated findings will be reported.DiscussionMS metrics/data forms are feasible for routine clinical settings, simple to interpret and provide a valuable benchmark for guiding MS service improvements.

Uncertainties due to spatial resolution (E R) of gridded sea-ice thickness (z) distributions remain largely unquantified. We address this issue using remotely sensed and in situ observations of the Southern Ocean (south of −60°) to determine appropriate data sources based on length scales for continental-scale sea-ice studies. Sea-ice thickness is not normally distributed such that the mean, median, and mode are distinct from each other. Averaging only retains the mean value, reducing bias and natural variability as z is aggregated to coarse resolutions. The rate of smoothing as a function of resolution influences the sea-ice thickness distribution represented by each product. Analysis of E R for 1° through 5° resolutions shows absolute E R increasing with grid-cell size and sea-ice extent in both datasets. The absolute E R for gridded thickness distribution ranges from 0.02 to 0.40 m at 1° and from 0.03 to 0.78 m at 5°. The E R value and slope (m) is lower (E R≤0.20 m, m

A negative urine pregnancy test in the emergency department traditionally excludes the diagnosis of pregnancy. We report a rare case of ruptured ectopic pregnancy in a patient with a negative urine pregnancy test but with a serum beta-human chorionic gonadotropin (β-hCG) of 10 mIU/mL. The patient developed hemoperitoneum and required laparoscopy by Obstetrics and Gynecology (OB/Gyn). This case highlights the fallibility of the urine pregnancy test in diagnosing early pregnancy.

Intermediates along a protein's folding pathway can play an important role in its biology. Previous kinetics studies have revealed an early folding intermediate for T4 lysozyme, a small, well-characterized protein composed of an N-terminal and a C-terminal subdomain. Pulse-labeling hydrogen exchange studies suggest that residues from both subdomains contribute to the structure of this intermediate. On the other hand, equilibrium native state hydrogen experiments have revealed a high-energy, partially unfolded form of the protein that has an unstructured N-terminal subdomain and a structured C-terminal subdomain. To resolve this discrepancy between kinetics and equilibrium data, we performed detailed kinetics analyses of the folding and unfolding pathways of T4 lysozyme, as well as several point mutants and large-scale variants. The data support the argument for the presence of two distinct intermediates, one present on each side of the rate-limiting transition state barrier. The effects of circular permutation and site-specific mutations in the wild-type and circular permutant background, as well as a fragment containing just the C-terminal subdomain, support a model for the unfolding intermediate with an unfolded N-terminal and a folded C-terminal subdomain. Our results suggest that the partially unfolded form identified by native state hydrogen exchange resides on the folded side of the rate-limiting transition state and is, therefore, under most conditions, a “hidden” intermediate.

In the midst of his graduate chemistry studies at the Massachusetts Institute of Technology (MIT), Carl Brozek stumbled into a new challenge: a yearlong campaign to improve advising for graduate students. His effort began 2 years ago, after the head of the chemistry department called a town hall