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The anatomy and function of the respiratory systems of penguins are reviewed in relation to gas exchange and minimization of the risks of pulmonary barotrauma, decompression sickness and nitrogen narcosis during dives. Topics include available lung morphology and morphometry, respiratory air volumes determined with different techniques, review of possible physiological and biomechanical mechanisms of baroprotection, calculations of baroprotection limits and review of air sac and arterial partial pressure of oxygen (P_O2) profiles in relation to movement of air during breathing and during dives. Limits for baroprotection to 200, 400 and 600 m in Adélie, king and emperor penguins, respectively, would require complete transfer of air sac air and reductions in the combined tracheobronchial tree—parabronchial volume of 24% in Adélie, 53% in king penguins and 76% in emperor penguins. Air sac and arterial P_O2profiles at rest and during surface activity were consistent with unidirectional air flow through the lungs. During dives, P_O2profiles were more complex, but were consistent with compression of air sac air into the parabronchi and air capillaries with or without additional air mixing induced by potential differential air sac pressures generated by wing movements. This article is part of the theme issue ‘The biology of the avian respiratory system’. 

Cationic water-soluble deep cavitands enable hierarchical assembly-based recognition, optical detection and extraction of perfluoroalkyl substances (PFAS) in aqueous solution. Recognition of the PFAS occurs at the lower rim crown of the cavitand, which triggers self-aggregation of a PFAS-cavitand complex, allowing extraction from water. In addition, when paired with an indicator dye that can be bound in the cavity of the host molecule, the PFAS-cavitand association causes a significant (>20-fold at micromolar [PFAS]) enhancement of dye fluorescence due to conformational rearrangement of the fluxional cavitand AMI, allowing optical sensing of PFAS. The cavitands are water-soluble, and the detection and recognition occur in purely aqueous solution. The association is most effective for long chain sulfonate PFAS, and as such, selective optical detection of perfluorooctanesulfonate is possible, with a LOD = 130 nM in buffered water, and 500 nM in real-world samples such as polluted canal water. By pairing the AMI host with multiple dyes in an array-based format, full discrimination of five other PFAS can be achieved at micromolar concentration via differential sensing. In addition, the aggregation process allows extraction of PFAS from solution, and a 99% reduction of PFOS concentration in water is possible with a single treatment of an equimolar concentration of AMI cavitand. The hierarchical nature of the cavitand recognition system allows both selective, sensitive optical detection and extraction of PFAS from water with a single scaffold. 

We present nuclear magnetic resonance data in BaFe2As2 in the presence of pulsed strain fields that are interleaved in time with the radio frequency excitation pulses. In this approach, the preceding nuclear magnetization acquires a phase shift that is proportional to the strain and pulse time. The sensitivity of this approach is limited by the homogeneous decoherence time, T2, rather than the inhomogeneous linewidth. We measure the nematic susceptibility as a function of temperature and demonstrate a three orders of magnitude improvement in sensitivity. This approach will enable studies of the strain response in a broad range of materials that previously were inaccessible due to inhomogeneous broadening. 

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learn- ing by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classifi- cation and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the con- creteness of words, which are in turn a predictor of children’s word learning, suggesting that these models are capturing the relationship between words and visual phenomena. 

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized the promised potential due to a lack of insight into pathology and HCI considerations for pathologists’ navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists’ domain knowledge, we designed NaviPath — a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that participants’ navigation was more consistent with NaviPath, which can improve the examination quality. 

We present a new prospective analysis of deep multi-band imaging with the James Webb Space Telescope (JWST). In this work, we investigate the recovery of high-redshift 5 <   z  <  12 galaxies through extensive image simulations of accepted JWST programs, including the Early Release Science in the EGS field and the Guaranteed Time Observations in the HUDF. We introduced complete samples of ∼300 000 galaxies with stellar masses of log( M * / M ⊙ ) > 6 and redshifts of 0 <   z  <  15, as well as galactic stars, into realistic mock NIRCam, MIRI, and HST images to properly describe the impact of source blending. We extracted the photometry of the detected sources, as in real images, and estimated the physical properties of galaxies through spectral energy distribution fitting. We find that the photometric redshifts are primarily limited by the availability of blue-band and near-infrared medium-band imaging. The stellar masses and star formation rates are recovered within 0.25 and 0.3 dex, respectively, for galaxies with accurate photometric redshifts. Brown dwarfs contaminating the z  >  5 galaxy samples can be reduced to < 0.01 arcmin −2 with a limited impact on galaxy completeness. We investigate multiple high-redshift galaxy selection techniques and find that the best compromise between completeness and purity at 5 <   z  <  10 using the full redshift posterior probability distributions. In the EGS field, the galaxy completeness remains higher than 50% at magnitudes m UV  <  27.5 and at all redshifts, and the purity is maintained above 80 and 60% at z  ≤ 7 and 10, respectively. The faint-end slope of the galaxy UV luminosity function is recovered with a precision of 0.1–0.25, and the cosmic star formation rate density within 0.1 dex. We argue in favor of additional observing programs covering larger areas to better constrain the bright end. 

Drug development suffers from a lack of predictive and human-relevant in vitro models. Organ-on-chip (OOC) technology provides advanced culture capabilities to generate physiologically appropriate, human-based tissue in vitro , therefore providing a route to a predictive in vitro model. However, OOC technologies are often created at the expense of throughput, industry-standard form factors, and compatibility with state-of-the-art data collection tools. Here we present an OOC platform with advanced culture capabilities supporting a variety of human tissue models including liver, vascular, gastrointestinal, and kidney. The platform has 96 devices per industry standard plate and compatibility with contemporary high-throughput data collection tools. Specifically, we demonstrate programmable flow control over two physiologically relevant flow regimes: perfusion flow that enhances hepatic tissue function and high-shear stress flow that aligns endothelial monolayers. In addition, we integrate electrical sensors, demonstrating quantification of barrier function of primary gut colon tissue in real-time. We utilize optical access to the tissues to directly quantify renal active transport and oxygen consumption via integrated oxygen sensors. Finally, we leverage the compatibility and throughput of the platform to screen all 96 devices using high content screening (HCS) and evaluate gene expression using RNA sequencing (RNA-seq). By combining these capabilities in one platform, physiologically-relevant tissues can be generated and measured, accelerating optimization of an in vitro model, and ultimately increasing predictive accuracy of in vitro drug screening.