Reversed flow of Atlantic deep water during the Last Glacial Maximum

The meridional overturning circulation (MOC) of the Atlantic Ocean is considered to be one of the most important components of the climate system. This is because its warm surface currents, such as the Gulf Stream, redistribute huge amounts of energy from tropical to high latitudes and influence regional weather and climate patterns, whereas its lower limb ventilates the deep ocean and affects the storage of carbon in the abyss, away from the atmosphere. Despite its significance for future climate, the operation of the MOC under contrasting climates of the past remains controversial. Nutrient-based proxies1, 2 and recent model simulations3 indicate that during the Last Glacial Maximum the convective activity in the North Atlantic Ocean was much weaker than at present. In contrast, rate-sensitive radiogenic 231Pa/230Th isotope ratios from the North Atlantic have been interpreted to indicate only minor changes in MOC strength4, 5, 6. Here we show that the basin-scale abyssal circulation of the Atlantic Ocean was probably reversed during the Last Glacial Maximum and was dominated by northward water flow from the Southern Ocean. These conclusions are based on new high-resolution data from the South Atlantic Ocean that establish the basin-scale north to south gradient in 231Pa/230Th, and thus the direction of the deep ocean circulation. Our findings are consistent with nutrient-based proxies and argue that further analysis of 231Pa/230Th outside the North Atlantic basin will enhance our understanding of past ocean circulation, provided that spatial gradients are carefully considered. This broader perspective suggests that the modern pattern of the Atlantic MOC—with a prominent southerly flow of deep waters originating in the North Atlantic—arose only during the Holocene epoch

Markov Chain Monte Carlo and the Application to Geodetic Time Series Analysis

The time evolution of geophysical phenomena can be characterised by stochastic time series. The stochastic nature of the signal stems from the geophysical phenomena involved and any noise, which may be due to, e.g., un-modelled effects or measurement errors. Until the 1990's, it was usually assumed that white noise could fully characterise this noise. However, this was demonstrated to be not the case and it was proven that this assumption leads to underestimated uncertainties of the geophysical parameters inferred from the geodetic time series. Therefore, in order to fully quantify all the uncertainties as robustly as possible, it is imperative to estimate not only the deterministic but also the stochastic parameters of the time series. In this regard, the Markov Chain Monte Carlo (MCMC) method can provide a sample of the distribution function of all parameters, including those regarding the noise, e.g., spectral index and amplitudes. After presenting the MCMC method and its implementation in our MCMC software we apply it to synthetic and real time series and perform a cross-evaluation using Maximum Likelihood Estimation (MLE) as implemented in the CATS software. Several examples as to how the MCMC method performs as a parameter estimation method for geodetic time series are given in this chapter. These include the applications to GPS position time series, superconducting gravity time series and monthly mean sea level (MSL) records, which all show very different stochastic properties. The impact of the estimated parameter uncertainties on sub-sequentially derived products is briefly demonstrated for the case of plate motion models. Finally, the MCMC results for weekly downsampled versions of the benchmark synthetic GNSS time series as provided in Chapter 2 are presented separately in an appendix

A Worldwide Test of the Predictive Validity of Ideal Partner Preference-Matching

©American Psychological Association, [2024]. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available, upon publication, at: [ARTICLE DOI]”Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference-matching (i.e., do people positively evaluate partners who match versus mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered report—partnered with the Psychological Science Accelerator—uses a highly powered design (N=10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The “corrected pattern metric” that collapses across 35 traits revealed a zero-order effect of β=.19 and an effect of β=.11 when included alongside a normative preference-matching metric. Specific traits in the “level metric” (interaction) tests revealed very small (average β=.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, men’s and (especially) women’s stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, men’s stated preferences underestimated—and women’s stated preferences overestimated—revealed preferences. Implications for the literature on human mating are discussed.Unfunde

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Functional and informatics analysis enables glycosyltransferase activity prediction

The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical–bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions

EAACI guidelines on the diagnosis of IgE-mediated food allergy

This European Academy of Allergy and Clinical Immunology guideline provides recommendations for diagnosing IgE-mediated food allergy and was developed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Food allergy diagnosis starts with an allergy-focused clinical history followed by tests to determine IgE sensitization, such as serum allergen-specific IgE (sIgE) and skin prick test (SPT), and the basophil activation test (BAT), if available. Evidence for IgE sensitization should be sought for any suspected foods. The diagnosis of allergy to some foods, such as peanut and cashew nut, is well supported by SPT and serum sIgE, whereas there are less data and the performance of these tests is poorer for other foods, such as wheat and soya. The measurement of sIgE to allergen components such as Ara h 2 from peanut, Cor a 14 from hazelnut and Ana o 3 from cashew can be useful to further support the diagnosis, especially in pollen-sensitized individuals. BAT to peanut and sesame can be used additionally. The reference standard for food allergy diagnosis is the oral food challenge (OFC). OFC should be performed in equivocal cases. For practical reasons, open challenges are suitable in most cases. Reassessment of food allergic children with allergy tests and/or OFCs periodically over time will enable reintroduction of food into the diet in the case of spontaneous acquisition of oral tolerance

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century