Correlated spin canting in ordered core-shell Fe3O4/MnxFe3-XO4 nanoparticle assemblies

Polarization-analyzed small-angle neutron-scattering methods are used to determine the spin arrangements and experimental length scales of magnetic correlations in ordered three-dimensional assemblies of ∼7.4-nm-diam core-shell Fe3O4/MnxFe3−xO4 nanoparticles. In moderate to high magnetic fields, the assemblies display a canted magnetic structure where the canting direction is coherent from nanoparticle to nanoparticle, in contrast to the less extended, more single-particle-like behavior for similar ferrite assemblies. The observed magnetic scattering is modeled by assuming that the interparticle dipolar coupling combined with Zeeman effects in a field leads to nanoparticle domains with preferred net spin alignments relative to packing symmetry axes. Over a range of fields and temperatures, the model qualitatively explains the observed scattering anomalies in terms of clusters that vary in area and thickness, highlighting the complex structures adopted in real, dense nanoparticle systems. The clusters often have a strong two-dimensional magnetic character which is attributed to structural stacking faults and the resulting influence of interparticle dipolar interactions for these magnetically soft nanoparticles

Notch Signaling Activates Yorkie Non-Cell Autonomously in Drosophila

In Drosophila imaginal epithelia, cells mutant for the endocytic neoplastic tumor suppressor gene vps25 stimulate nearby untransformed cells to express Drosophila Inhibitor-of-Apoptosis-Protein-1 (DIAP-1), conferring resistance to apoptosis non-cell autonomously. Here, we show that the non-cell autonomous induction of DIAP-1 is mediated by Yorkie, the conserved downstream effector of Hippo signaling. The non-cell autonomous induction of Yorkie is due to Notch signaling from vps25 mutant cells. Moreover, activated Notch in normal cells is sufficient to induce non-cell autonomous Yorkie activity in wing imaginal discs. Our data identify a novel mechanism by which Notch promotes cell survival non-cell autonomously and by which neoplastic tumor cells generate a supportive microenvironment for tumor growth

Xanthine oxidoreductase promotes the inflammatory state of mononuclear phagocytes through effects on chemokine expression, peroxisome proliferator-activated receptor-gamma sumoylation, and HIF-₁ alpha

The protective effects of pharmacological inhibitors of xanthine oxidoreductase (XOR) have implicated XOR in many inflammatory diseases. Nonetheless, the role played by XOR during inflammation is poorly understood. We previously observed that inhibition of XOR within the inflammatory mononuclear phagocytes (MNP) prevented neutrophil recruitment during adoptive transfer demonstrating the role of XOR in MNP-mediated neutrophil recruitment. To further explore the role of XOR in the inflammatory state of MNP, we studied MNP isolated from inflammatory lungs combined with analyses of MNP cell lines. We demonstrated that XOR activity was increased in inflammatory MNP following insufflation of Th-1 cytokines in vivo and that activity was specifically increased by MNP differentiation. Inhibition of XOR reduced levels of CINC-1 secreted by MNP. Expression of peroxisome proliferator-activated receptor γ (PPARγ) in purified rat lung MNP and MNP cell lines reflected both the presence of PPARγ isoforms and PPARγ SUMOylation, and XOR inhibitors increased levels of SUMO-PPARγ in MNP cell lines. Both ectopic overexpression of XOR cDNA and uric acid supplementation reduced SUMO-PPARγ in MNP cells. Levels of the M2 markers CD36, CD206, and arginase-1 were modulated by uric acid and oxonic acid, whereas siRNA to SUMO-1 or PIAS-1 also reduced arginase-1 in RAW264.7 cells. We also observed that HIF-1α was increased by XOR inhibitors in inflammatory MNP and in MNP cell lines. These data demonstrate that XOR promotes the inflammatory state of MNP through effects on chemokine expression, PPARγ SUMOylation, and HIF-1α and suggest that strategies for inhibiting XOR may be valuable in modulating lung inflammatory disorders

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

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

Discovery of New Crystal Structures Using a Highly Tunable Interaction Potential

78 pagesSoft matter systems can achieve structural variety beyond that of their atomic and molecular building blocks. In past experimental work, soft materials, e.g., nanoparticles and colloids, have been shown to spontaneously self-assemble into familiar crystallographic arrangements, ranging from simple, close-packed structures to some of the most complex known crystal structures such as clathrates. Simulations using coarse-grained molecular dynamics simulations can also yield similar structures, as well as structures with no known atomic equivalents. Particles controlled by relatively simple, short-range isotropic pair potentials have been shown to generate such complex assemblies as a one-component icosahedral quasicrystal---a highly ordered but aperiodic type of structure, which had previously only been observed in multi-component intermetallics. The search for new, self-assembling crystal structures hinges on the exact interaction potential, yet how the shape of the interaction potential influences the resulting structure is still poorly understood. In this work, we design a new functional form for the interaction potential, in which its features can be tuned independently and intuitively. We use our interaction potential to screen small regions of phase space by sampling two-dimensional cuts, targeting regions of low-coordinated crystal structures. We report the discovery and characterization of eight new crystal structures---cI16-X, cP8-X, hP3-X, hP14-Y, hR3-X, mC32-X, tI16-X, and tI32-Y---as well as six more structures that require more advanced characterization techniques.2023-03-1

Targeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions

Particles interacting via isotropic, multiwell pair potentials have been shown to self-assemble into a range of crystal structures, yet how the characteristics of the underlying interaction potential give rise to the resultant structure remains largely unknown. We have thus developed a functional form for the interaction potential in which all features can be tuned independently. We perform continuous parameter space searches by systematically changing pairs of parameters, controlling the various features of the interaction potential. By enforcing a repulsive first well (controlling particle interactions of the first neighbor shell), we stimulate the formation of low-coordinated assemblies. We report the self-assembly of 20 previously unknown crystal structure types, 14 of which have low coordination numbers. Despite limiting the search to a small region of the vast parameter space of possible particle interactions, a wealth of complexity and symmetry is apparent within these crystal structures, which include clathrates with empty cages and low-symmetry structures. Our findings suggest that an unknown number of previously undiscovered crystal structure configurations are possible through self-assembly, which can serve as interesting design targets for soft condensed matter synthesis

Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures

Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally-derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against 7 existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity towards small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for machine learning and other applications