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𝘼 𝙠𝙚𝙮 𝙗𝙧𝙖𝙞𝙣 𝙧𝙚𝙜𝙞𝙤𝙣 𝙧𝙚𝙨𝙥𝙤𝙣𝙙𝙨 𝙩𝙤 𝙛𝙖𝙘𝙚𝙨 𝙨𝙞𝙢𝙞𝙡𝙖𝙧𝙡𝙮 𝙞𝙣 𝙞𝙣𝙛𝙖𝙣𝙩𝙨 𝙖𝙣𝙙 𝙖𝙙𝙪𝙡𝙩𝙨
𝙎𝙩𝙪𝙙𝙮 𝙨𝙪𝙜𝙜𝙚𝙨𝙩𝙨 𝙩𝙝𝙞𝙨 𝙖𝙧𝙚𝙖 𝙤𝙛 𝙩𝙝𝙚 𝙫𝙞𝙨𝙪𝙖𝙡 𝙘𝙤𝙧𝙩𝙚𝙭 𝙚𝙢𝙚𝙧𝙜𝙚𝙨 𝙢𝙪𝙘𝙝 𝙚𝙖𝙧𝙡𝙞𝙚𝙧 𝙞𝙣 𝙙𝙚𝙫𝙚𝙡𝙤𝙥𝙢𝙚𝙣𝙩 𝙩𝙝𝙖𝙣 𝙥𝙧𝙚… See more.
Continue reading “A key brain region responds to faces similarly in infants and adults” »
The short-term effectiveness of a two-dose regimen of the BioNTech/Pfizer mRNA BNT162b2 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine was widely demonstrated. However, long term effectiveness is still unknown. Leveraging the centralized computerized database of Maccabi Healthcare Services (MHS), we assessed the correlation between time-from-vaccine and incidence of breakthrough infection between June 1 and July 27, the date of analysis. After controlling for potential confounders as age and comorbidities, we found a significant 1.51 fold (95% CI, 1.38−1.66) increased risk for infection for early vaccinees compared to those vaccinated later that was similar across all ages groups. The increased risk reached 2.26-fold (95% CI, 1.80−3.01) when comparing those who were vaccinated in January to those vaccinated in April. This preliminary finding of vaccine waning as a factor of time from vaccince should prompt further investigations into long-term protection against different strains.
The duration of effectiveness of SARS-CoV-2 vaccination is not yet known. Here, the authors present preliminary evidence of BNT162b2 vaccine waning across all age groups above 16, with a higher incidence of infection in people who received their second dose early in 2021 compared to later in the year.
How are chemical elements produced in our Universe? Where do heavy elements like gold and uranium come from? Using computer simulations, a research team from the GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt, together with colleagues from Belgium and Japan, shows that the synthesis of heavy elements is typical for certain black holes with orbiting matter accumulations, so-called accretion disks. The predicted abundance of the formed elements provides insight into which heavy elements need to be studied in future laboratories — such as the Facility for Antiproton and Ion Research (FAIR), which is currently under construction — to unravel the origin of heavy elements. The results are published in the journal Monthly Notices of the Royal Astronomical Society.
All heavy elements on Earth today were formed under extreme conditions in astrophysical environments: inside stars, in stellar explosions, and during the collision of neutron stars. Researchers are intrigued with the question in which of these astrophysical events the appropriate conditions for the formation of the heaviest elements, such as gold or uranium, exist. The spectacular first observation of gravitational waves and electromagnetic radiation originating from a neutron star merger in 2017 suggested that many heavy elements can be produced and released in these cosmic collisions. However, the question remains open as to when and why the material is ejected and whether there may be other scenarios in which heavy elements can be produced.
Promising candidates for heavy element production are black holes orbited by an accretion disk of dense and hot matter. Such a system is formed both after the merger of two massive neutron stars and during a so-called collapsar, the collapse and subsequent explosion of a rotating star. The internal composition of such accretion disks has so far not been well understood, particularly with respect to the conditions under which an excess of neutrons forms. A high number of neutrons is a basic requirement for the synthesis of heavy elements, as it enables the rapid neutron-capture process or r-process. Nearly massless neutrinos play a key role in this process, as they enable conversion between protons and neutrons.
It seems evaporated glass, chains of ions, and quantum stability go hand in hand.
IonQ has replaced the typical silicon with a fused glass-based chip, allowing for unprecedented levels of scaling for the company’s trapped-ion approach to quantum computing.
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The company announced a deal that could help significantly expand access to the Covid-19 treatment, but the agreement excludes a number of countries hit hard by the pandemic.
In quantum mechanics, counterfactual behaviours are generally associated with particles being affected by events taking place where they can’t be found. Here, the authors consider extended quantum Cheshire cat scenarios where a particle can be influenced in regions where only its disembodied property has entered.
Laughter is a ubiquitous social signal. Recent work has highlighted distinctions between spontaneous and volitional laughter, which differ in terms of both production mechanisms and perceptual features. Here, we test listeners’ ability to infer group identity from volitional and spontaneous laughter, as well as the perceived positivity of these laughs across cultures. Dutch (n = 273) and Japanese (n = 131) participants listened to decontextualized laughter clips and judged (i) whether the laughing person was from their cultural in-group or an out-group; and (ii) whether they thought the laughter was produced spontaneously or volitionally. They also rated the positivity of each laughter clip. Using frequentist and Bayesian analyses, we show that listeners were able to infer group membership from both spontaneous and volitional laughter, and that performance was equivalent for both types of laughter. Spontaneous laughter was rated as more positive than volitional laughter across the two cultures, and in-group laughs were perceived as more positive than out-group laughs by Dutch but not Japanese listeners. Our results demonstrate that both spontaneous and volitional laughter can be used by listeners to infer laughers’ cultural group identity.
This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part II)’.
Laughter is a frequently occurring and socially potent nonverbal vocalization, which is frequently used to signal affiliation, reward or cooperative intent, and often helps to maintain and strengthen social bonds [1,2]. A key distinction is whether laughs are spontaneous or volitional [3,4]. Spontaneous and volitional laughs are thought to be generated by different vocal production mechanisms. We often laugh spontaneously with little volitional control, which is thought to typically reflect an internal emotional state. Yet laughter can also be produced with volitional modulation of vocal output, which is more likely to express polite agreement in conversation [5,6]. Recent research has shown that listeners’ ability to differentiate individual speakers is impaired for spontaneous, as compared to volitional, laughter [7,8].
A new model explains the current density of dark matter by proposing that conventional matter converted to dark matter in the early Universe.
Mechanical metamaterials can be engineered with properties not possible in ordinary materials. Here the authors demonstrate and study an active metamaterial with self-sensing characteristics that enables odd elastic properties not observed in passive media.
Machine learning (ML) models are powerful tools to study multivariate correlations that exist within large datasets but are hard for humans to identify16,23. Our aim is to build a model that captures the chemical interactions between the element combinations that afford reported crystalline inorganic materials, noting that the aim of such models is efficacy rather than interpretability, and that as such they can be complementary guides to human experts. The model should assist expert prioritization between the promising element combinations by ranking them quantitatively. Researchers have practically understood how to identify new chemistries based on element combinations for phase-field exploration, but not at significant scale. However, the prioritization of these attractive knowledge-based choices for experimental and computational investigation is critical as it determines substantial resource commitment. The collaborative ML workflow24,25 developed here includes a ML tool trained across all available data at a scale beyond that, which humans can assimilate simultaneously to provide numerical ranking of the likelihood of identifying new phases in the selected chemistries. We illustrate the predictive power of ML in this workflow in the discovery of a new solid-state Li-ion conductor from unexplored quaternary phase fields with two anions. To train a model to assist prioritization of these candidate phase fields, we extracted 2021 MxM ′yAzA ′t phases reported in ICSD (Fig. 1, Step 1), and associated each phase with the phase fields M-M ′-A-A′ where M, M ′ span all cations, A, A ′ are anions {N3−, P3−, As3−, O2−, S2−, Se2−, Te2−, F−, Cl−, Br−, and I−} and x, y, z, t denote concentrations (Fig. 1, Step 2). Data were augmented by 24-fold elemental permutations to enhance learning and prevent overfitting (Supplementary Fig. 2).
ML models rely on using appropriate features (often called descriptors)26 to describe the data presented, so feature selection is critical to the quality of the model. The challenge of selecting the best set of features among the multitude available for the chemical elements (e.g., atomic weight, valence, ionic radius, etc.)26 lies in balancing competing considerations: a small number of features usually makes learning more robust, while limiting the predictive power of resulting models, large numbers of features tend to make models more descriptive and discriminating while increasing the risk of overfitting. We evaluated 40 individual features26,27 (Supplementary Fig. 4, 5) that have reported values for all elements and identify a set of 37 elemental features that best balance these considerations. We thus describe each phase field of four elements as a vector in a 148-dimensional feature space (37 features × 4 elements = 148 dimensions).
To infer relationships between entries in such a high-dimensional feature space in which the training data are necessarily sparsely distributed28, we employ the variational autoencoder (VAE), an unsupervised neural network-based dimensionality reduction method (Fig. 1, Step 3), which quantifies nonlinear similarities in high-dimensional unlabelled data29 and, in addition to the conventional autoencoder, pays close attention to the distribution of the data features in multidimensional space. A VAE is a two-part neural network, where one part is used to compress (encode) the input vectors into a lower-dimensional (latent) space, and the other to decode vectors in latent space back into the original high-dimensional space. Here we choose to encode the 148-dimensional input feature space into a four-dimensional latent feature space (Supplementary Methods).
