Showing papers by "University of Bonn published in 2019"

GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions.

Abstract: An extended semiempirical tight-binding model is presented, which is primarily designed for the fast calculation of structures and noncovalent interaction energies for molecular systems with roughly 1000 atoms. The essential novelty in this so-called GFN2-xTB method is the inclusion of anisotropic second order density fluctuation effects via short-range damped interactions of cumulative atomic multipole moments. Without noticeable increase in the computational demands, this results in a less empirical and overall more physically sound method, which does not require any classical halogen or hydrogen bonding corrections and which relies solely on global and element-specific parameters (available up to radon, Z = 86). Moreover, the atomic partial charge dependent D4 London dispersion model is incorporated self-consistently, which can be naturally obtained in a tight-binding picture from second order density fluctuations. Fully analytical and numerically precise gradients (nuclear forces) are implemented. The...

Genome-wide association study identifies 30 loci associated with bipolar disorder

Abstract: Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

Abstract: Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-dominantly solved using high-resolution cameras. As a result of that, other sensor modalities potentially useful for this task are often ignored. In this paper, we push the state of the art in LiDAR-only semantic segmentation forward in order to provide another independent source of semantic information to the vehicle. Our approach can accurately perform full semantic segmentation of LiDAR point clouds at sensor frame rate. We exploit range images as an intermediate representation in combination with a Convolutional Neural Network (CNN) exploiting the rotating LiDAR sensor model. To obtain accurate results, we propose a novel post-processing algorithm that deals with problems arising from this intermediate representation such as discretization errors and blurry CNN outputs. We implemented and thoroughly evaluated our approach including several comparisons to the state of the art. Our experiments show that our approach outperforms state-of-the-art approaches, while still running online on a single embedded GPU. The code can be accessed at https://github.com/PRBonn/lidar-bonnetal.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Abstract: Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete 360-degree field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight.

Abstract: INTRODUCTION To date, 559 humans have been flown into space, but long-duration (>300 days) missions are rare (n = 8 total). Long-duration missions that will take humans to Mars and beyond are planned by public and private entities for the 2020s and 2030s; therefore, comprehensive studies are needed now to assess the impact of long-duration spaceflight on the human body, brain, and overall physiology. The space environment is made harsh and challenging by multiple factors, including confinement, isolation, and exposure to environmental stressors such as microgravity, radiation, and noise. The selection of one of a pair of monozygotic (identical) twin astronauts for NASA’s first 1-year mission enabled us to compare the impact of the spaceflight environment on one twin to the simultaneous impact of the Earth environment on a genetically matched subject. RATIONALE The known impacts of the spaceflight environment on human health and performance, physiology, and cellular and molecular processes are numerous and include bone density loss, effects on cognitive performance, microbial shifts, and alterations in gene regulation. However, previous studies collected very limited data, did not integrate simultaneous effects on multiple systems and data types in the same subject, or were restricted to 6-month missions. Measurement of the same variables in an astronaut on a year-long mission and in his Earth-bound twin indicated the biological measures that might be used to determine the effects of spaceflight. Presented here is an integrated longitudinal, multidimensional description of the effects of a 340-day mission onboard the International Space Station. RESULTS Physiological, telomeric, transcriptomic, epigenetic, proteomic, metabolomic, immune, microbiomic, cardiovascular, vision-related, and cognitive data were collected over 25 months. Some biological functions were not significantly affected by spaceflight, including the immune response (T cell receptor repertoire) to the first test of a vaccination in flight. However, significant changes in multiple data types were observed in association with the spaceflight period; the majority of these eventually returned to a preflight state within the time period of the study. These included changes in telomere length, gene regulation measured in both epigenetic and transcriptional data, gut microbiome composition, body weight, carotid artery dimensions, subfoveal choroidal thickness and peripapillary total retinal thickness, and serum metabolites. In addition, some factors were significantly affected by the stress of returning to Earth, including inflammation cytokines and immune response gene networks, as well as cognitive performance. For a few measures, persistent changes were observed even after 6 months on Earth, including some genes’ expression levels, increased DNA damage from chromosomal inversions, increased numbers of short telomeres, and attenuated cognitive function. CONCLUSION Given that the majority of the biological and human health variables remained stable, or returned to baseline, after a 340-day space mission, these data suggest that human health can be mostly sustained over this duration of spaceflight. The persistence of the molecular changes (e.g., gene expression) and the extrapolation of the identified risk factors for longer missions (>1 year) remain estimates and should be demonstrated with these measures in future astronauts. Finally, changes described in this study highlight pathways and mechanisms that may be vulnerable to spaceflight and may require safeguards for longer space missions; thus, they serve as a guide for targeted countermeasures or monitoring during future missions.

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Abstract: Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete $360^{o}$ field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.

Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches

Abstract: INTRODUCTION Resident tissue macrophages (RTMs) are a heterogeneous population of immune cells occupying multiple tissue niches and exhibiting microenvironment-specific phenotypes and functions. In certain tissues such as the brain, lung, and liver, embryonically derived RTMs maintain themselves by self-renewal, whereas others, including those in the gut, dermis, and pancreas, are replaced by monocytes, at levels that are tissue specific. Once they arrive in their tissue of residence, monocytes undergo extensive differentiation according to molecular cues provided by their distinct tissue-specific niches, enabling their development into specialized RTMs that support local tissue function. RATIONALE As a result of this ontogenetic and tissue niche heterogeneity, each tissue contains multiple populations of macrophages. For example, in the murine lung, alveolar macrophages are the major embryonically derived population in the alveolar spaces, whereas a minor population named interstitial macrophages (IMs) resides within the lung parenchyma. Previous results reported several phenotypically distinct IM subpopulations, whose relationship remained unknown. Do they represent independent populations or, rather, different points on the spectrum of maturation and activation states? How do these differences relate to their localization in tissue or roles in tissue function in health and disease? Does such macrophage heterogeneity also exist in other tissues? RESULTS Here, using single-cell mRNA sequencing, we unbiasedly identified two independent populations exhibiting distinct gene expression profiles and phenotypes: Lyve1loMHCIIhiCX3CR1hi (Lyve1loMHCIIhi) and Lyve1hiMHCIIloCX3CR1lo (Lyve1hiMHCIIlo) RTMs. We uncovered evidence of parallel populations in multiple others tissues, including the heart, fat, and dermis, as well as in human lung and omental and subcutaneous fat tissues, suggesting that a similar dichotomy is observed in human tissues. We further demonstrated that both populations are slowly replaced by Ly6Chi monocytes. Importantly, using complementary fate-mapping models, we showed that monocyte-derived RTMs (MRTMs) are two separate lineages, rather than representing points along a developmental or maturation continuum. Notably, these distinct MRTM populations preferentially reside within different, but conserved, subtissular niches, located either adjacent to nerve bundles and fibers (Lyve1loMHCIIhi) or blood vessels (Lyve1hiMHCIIlo) across tissues. Finally, by acutely depleting Lyve1hiMHCIIlo MRTMs using a mouse model of inducible macrophage depletion during the induction of fibrosis, we found that the absence of Lyve1hiMHCIIlo IMs exacerbated experimental lung and heart fibrosis, demonstrating their critical role in tissue inflammation. CONCLUSION Two independent MRTMs populations exist across tissues with specific niche-dependent phenotype and functional programming. Their different roles in homeostasis, immune regulation, and fibrosis renders them attractive and separate cellular targets for the therapeutic exploitation of RTM subsets.

A generally applicable atomic-charge dependent London dispersion correction

Abstract: The so-called D4 model is presented for the accurate computation of London dispersion interactions in density functional theory approximations (DFT-D4) and generally for atomistic modeling methods. In this successor to the DFT-D3 model, the atomic coordination-dependent dipole polarizabilities are scaled based on atomic partial charges which can be taken from various sources. For this purpose, a new charge-dependent parameter-economic scaling function is designed. Classical charges are obtained from an atomic electronegativity equilibration procedure for which efficient analytical derivatives with respect to nuclear positions are developed. A numerical Casimir-Polder integration of the atom-in-molecule dynamic polarizabilities then yields charge- and geometry-dependent dipole-dipole dispersion coefficients. Similar to the D3 model, the dynamic polarizabilities are precomputed by time-dependent DFT and all elements up to radon (Z = 86) are covered. The two-body dispersion energy expression has the usual sum-over-atom-pairs form and includes dipole-dipole as well as dipole-quadrupole interactions. For a benchmark set of 1225 molecular dipole-dipole dispersion coefficients, the D4 model achieves an unprecedented accuracy with a mean relative deviation of 3.8% compared to 4.7% for D3. In addition to the two-body part, three-body effects are described by an Axilrod-Teller-Muto term. A common many-body dispersion expansion was extensively tested, and an energy correction based on D4 polarizabilities is found to be advantageous for larger systems. Becke-Johnson-type damping parameters for DFT-D4 are determined for more than 60 common density functionals. For various standard energy benchmark sets, DFT-D4 slightly but consistently outperforms DFT-D3. Especially for metal containing systems, the introduced charge dependence of the dispersion coefficients improves thermochemical properties. We suggest (DFT-)D4 as a physically improved and more sophisticated dispersion model in place of DFT-D3 for DFT calculations as well as other low-cost approaches like semi-empirical models.

Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa

Abstract: Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness1, affecting 0.9-4% of women and 0.3% of men2-4, with twin-based heritability estimates of 50-60%5. Mortality rates are higher than those in other psychiatric disorders6, and outcomes are unacceptably poor7. Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI)8,9 and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.

Quantitative assessment of microbial necromass contribution to soil organic matter.

Abstract: Soil carbon transformation and sequestration have received significant interest in recent years due to a growing need for quantitating its role in mitigating climate change. Even though our understanding of the nature of soil organic matter has recently been substantially revised, fundamental uncertainty remains about the quantitative importance of microbial necromass as part of persistent organic matter. Addressing this uncertainty has been hampered by the absence of quantitative assessments whether microbial matter makes up the majority of the persistent carbon in soil. Direct quantitation of microbial necromass in soil is very challenging because of an overlapping molecular signature with nonmicrobial organic carbon. Here, we use a comprehensive analysis of existing biomarker amino sugar data published between 1996 and 2018, combined with novel appropriation using an ecological systems approach, elemental carbon-nitrogen stoichiometry, and biomarker scaling, to demonstrate a suit of strategies for quantitating the contribution of microbe-derived carbon to the topsoil organic carbon reservoir in global temperate agricultural, grassland, and forest ecosystems. We show that microbial necromass can make up more than half of soil organic carbon. Hence, we suggest that next-generation field management requires promoting microbial biomass formation and necromass preservation to maintain healthy soils, ecosystems, and climate. Our analyses have important implications for improving current climate and carbon models, and helping develop management practices and policies.

Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN

Abstract: We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (-1.0_{-1.1}^{+0.9}) eV^{2}. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation.

Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations.

Abstract: The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the framework of meta-dynamics (MTD) to globally explore chemical compound, conformer, and reaction space. The biasing potential given as a sum of Gaussian functions is expressed with the root-mean-square-deviation (RMSD) in Cartesian space as a metric for the collective variables. This choice makes the approach robust and generally applicable to three common problems (i.e., conformer search, chemical reaction space exploration in a virtual nanoreactor, and for guessing reaction paths). Because of the inherent locality of the atomic RMSD, functional group or fragment selective treatments are possible facilitating the investigation of catalytic processes where, for example, only the substrate is thermally activated. Due to the approximate character of the GFN2-xTB method, the resulting structure ensembles require further refinement with more sophisticated, for example, density functional or wave function theory methods. However, the approach is extremely efficient running routinely on common laptop computers in minutes to hours of computation time even for realistically sized molecules with a few hundred atoms. Furthermore, the underlying potential energy surface for molecules containing almost all elements ( Z = 1-86) is globally consistent including the covalent dissociation process and electronically complicated situations in, for example, transition metal systems. As examples, thermal decomposition, ethyne oligomerization, the oxidation of hydrocarbons (by oxygen and a P450 enzyme model), a Miller-Urey model system, a thermally forbidden dimerization, and a multistep intramolecular cyclization reaction are shown. For typical conformational search problems of organic drug molecules, the new MTD(RMSD) algorithm yields lower energy structures and more complete conformer ensembles at reduced computational effort compared with its already well performing predecessor.

Vascular dysfunction-The disregarded partner of Alzheimer's disease

Abstract: Increasing evidence recognizes Alzheimer's disease (AD) as a multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. The recently updated AD Research Framework put forth by the National Institute on Aging-Alzheimer's Association describes a biomarker-based pathologic definition of AD focused on amyloid, tau, and neuronal injury. In response to this article, here we first discussed evidence that vascular dysfunction is an important early event in AD pathophysiology. Next, we examined various imaging sequences that could be easily implemented to evaluate different types of vascular dysfunction associated with, and/or contributing to, AD pathophysiology, including changes in blood-brain barrier integrity and cerebral blood flow. Vascular imaging biomarkers of small vessel disease of the brain, which is responsible for >50% of dementia worldwide, including AD, are already established, well characterized, and easy to recognize. We suggest that these vascular biomarkers should be incorporated into the AD Research Framework to gain a better understanding of AD pathophysiology and aid in treatment efforts.

Three-pion contribution to hadronic vacuum polarization

Abstract: We address the contribution of the $3\pi$ channel to hadronic vacuum polarization (HVP) using a dispersive representation of the $e^+e^-\to 3\pi$ amplitude. This channel gives the second-largest individual contribution to the total HVP integral in the anomalous magnetic moment of the muon $(g-2)_\mu$, both to its absolute value and uncertainty. It is largely dominated by the narrow resonances $\omega$ and $\phi$, but not to the extent that the off-peak regions were negligible, so that at the level of accuracy relevant for $(g-2)_\mu$ an analysis of the available data as model independent as possible becomes critical. Here, we provide such an analysis based on a global fit function using analyticity and unitarity of the underlying $\gamma^*\to3\pi$ amplitude and its normalization from a chiral low-energy theorem, which, in particular, allows us to check the internal consistency of the various $e^+e^-\to 3\pi$ data sets. Overall, we obtain $a_\mu^{3\pi}|_{\leq 1.8\,\text{GeV}}=46.2(6)(6)\times 10^{-10}$ as our best estimate for the total $3\pi$ contribution consistent with all (low-energy) constraints from QCD. In combination with a recent dispersive analysis imposing the same constraints on the $2\pi$ channel below $1\,\text{GeV}$, this covers nearly $80\%$ of the total HVP contribution, leading to $a_\mu^\text{HVP}=692.3(3.3)\times 10^{-10}$ when the remainder is taken from the literature, and thus reaffirming the $(g-2)_\mu$ anomaly at the level of at least $3.4\sigma$. As side products, we find for the vacuum-polarization-subtracted masses $M_\omega=782.63(3)(1)\,\text{MeV}$ and $M_\phi=1019.20(2)(1)\,\text{MeV}$, confirming the tension to the $\omega$ mass as extracted from the $2\pi$ channel.

Black holes, gravitational waves and fundamental physics: a roadmap

Abstract: The grand challenges of contemporary fundamental physics-dark matter, dark energy, vacuum energy, inflation and early universe cosmology, singularities and the hierarchy problem-all involve gravity as a key component. And of all gravitational phenomena, black holes stand out in their elegant simplicity, while harbouring some of the most remarkable predictions of General Relativity: event horizons, singularities and ergoregions. The hitherto invisible landscape of the gravitational Universe is being unveiled before our eyes: the historical direct detection of gravitational waves by the LIGO-Virgo collaboration marks the dawn of a new era of scientific exploration. Gravitational-wave astronomy will allow us to test models of black hole formation, growth and evolution, as well as models of gravitational-wave generation and propagation. It will provide evidence for event horizons and ergoregions, test the theory of General Relativity itself, and may reveal the existence of new fundamental fields. The synthesis of these results has the potential to radically reshape our understanding of the cosmos and of the laws of Nature. The purpose of this work is to present a concise, yet comprehensive overview of the state of the art in the relevant fields of research, summarize important open problems, and lay out a roadmap for future progress. This write-up is an initiative taken within the framework of the European Action on 'Black holes, Gravitational waves and Fundamental Physics'. © 2019 IOP Publishing Ltd.

Oxidative Stress in Neurodegenerative Diseases: From a Mitochondrial Point of View

Abstract: Age is the main risk factor for a number of human diseases, including neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis, which increasing numbers of elderly individuals suffer. These pathological conditions are characterized by progressive loss of neuron cells, compromised motor or cognitive functions, and accumulation of abnormally aggregated proteins. Mitochondrial dysfunction is one of the main features of the aging process, particularly in organs requiring a high-energy source such as the heart, muscles, brain, or liver. Neurons rely almost exclusively on the mitochondria, which produce the energy required for most of the cellular processes, including synaptic plasticity and neurotransmitter synthesis. The brain is particularly vulnerable to oxidative stress and damage, because of its high oxygen consumption, low antioxidant defenses, and high content of polyunsaturated fats very prone to be oxidized. Thus, it is not surprising the importance of protecting systems, including antioxidant defenses, to maintain neuronal integrity and survival. Here, we review the role of mitochondrial oxidative stress in the aging process, with a specific focus on neurodegenerative diseases. Understanding the molecular mechanisms involving mitochondria and oxidative stress in the aging and neurodegeneration may help to identify new strategies for improving the health and extending lifespan.

Climate change impact and adaptation for wheat protein

Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Perioperative Quality Initiative consensus statement on preoperative blood pressure, risk and outcomes for elective surgery

Abstract: Background A multidisciplinary international working subgroup of the third Perioperative Quality Initiative consensus meeting appraised the evidence on the influence of preoperative arterial blood pressure and community cardiovascular medications on perioperative risk. Methods A modified Delphi technique was used, evaluating papers published in MEDLINE on associations between preoperative numerical arterial pressure values or cardiovascular medications and perioperative outcomes. The strength of the recommendations was graded by National Institute for Health and Care Excellence guidelines. Results Significant heterogeneity in study design, including arterial pressure measures and perioperative outcomes, hampered the comparison of studies. Nonetheless, consensus recommendations were that (i) preoperative arterial pressure measures may be used to define targets for perioperative management; (ii) elective surgery should not be cancelled based solely upon a preoperative arterial pressure value; (iii) there is insufficient evidence to support lowering arterial pressure in the immediate preoperative period to minimise perioperative risk; and (iv) there is insufficient evidence that any one measure of arterial pressure (systolic, diastolic, mean, or pulse) is better than any other for risk prediction of adverse perioperative events. Conclusions Future research should define which preoperative arterial pressure values best correlate with adverse outcomes, and whether modifying arterial pressure in the preoperative setting will change the perioperative morbidity or mortality. Additional research should define optimum strategies for continuation or discontinuation of preoperative cardiovascular medications.

MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation

Abstract: Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics. While traditional approaches follow a two-step pipeline, by generating frame-wise probabilities and then feeding them to high-level temporal models, recent approaches use temporal convolutions to directly classify the video frames. In this paper, we introduce a multi-stage architecture for the temporal action segmentation task. Each stage features a set of dilated temporal convolutions to generate an initial prediction that is refined by the next one. This architecture is trained using a combination of a classification loss and a proposed smoothing loss that penalizes over-segmentation errors. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our model achieves state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.

Developmental origin, functional maintenance and genetic rescue of osteoclasts

Abstract: Osteoclasts are multinucleated giant cells that resorb bone, ensuring development and continuous remodelling of the skeleton and the bone marrow haematopoietic niche. Defective osteoclast activity leads to osteopetrosis and bone marrow failure1–9, whereas excess activity can contribute to bone loss and osteoporosis10. Osteopetrosis can be partially treated by bone marrow transplantation in humans and mice11–18, consistent with a haematopoietic origin of osteoclasts13,16,19 and studies that suggest that they develop by fusion of monocytic precursors derived from haematopoietic stem cells in the presence of CSF1 and RANK ligand1,20. However, the developmental origin and lifespan of osteoclasts, and the mechanisms that ensure maintenance of osteoclast function throughout life in vivo remain largely unexplored. Here we report that osteoclasts that colonize fetal ossification centres originate from embryonic erythro-myeloid progenitors21,22. These erythro-myeloid progenitor-derived osteoclasts are required for normal bone development and tooth eruption. Yet, timely transfusion of haematopoietic-stem-cell-derived monocytic cells in newborn mice is sufficient to rescue bone development in early-onset autosomal recessive osteopetrosis. We also found that the postnatal maintenance of osteoclasts, bone mass and the bone marrow cavity involve iterative fusion of circulating blood monocytic cells with long-lived osteoclast syncytia. As a consequence, parabiosis or transfusion of monocytic cells results in long-term gene transfer in osteoclasts in the absence of haematopoietic-stem-cell chimerism, and can rescue an adult-onset osteopetrotic phenotype caused by cathepsin K deficiency23,24. In sum, our results identify the developmental origin of osteoclasts and a mechanism that controls their maintenance in bones after birth. These data suggest strategies to rescue osteoclast deficiency in osteopetrosis and to modulate osteoclast activity in vivo. Multinucleated osteoclasts required for normal bone development and tooth eruption in the mouse originate from embryonic erythro-myeloid progenitors and are maintained after birth by fusion with circulating monocytes.

The Belle II Physics Book

Abstract: The Belle II detector will provide a major step forward in precision heavy flavor physics, quarkonium and exotic states, searches for dark sectors, and many other areas. The sensitivity to a large number of key observables can be improved by about an order of magnitude compared to the current measurements, and up to two orders in very clean search measurements. This increase in statistical precision arises not only due to the increased luminosity, but also from improved detector efficiency and precision for many channels. Many of the most interesting observables tend to have very small theoretical uncertainties that will therefore not limit the physics reach. This book has presented many new ideas for measurements, both to elucidate the nature of current anomalies seen in flavor, and to search for new phenomena in a plethora of observables that will become accessible with the Belle II dataset. The simulation used for the studiesinthis book was state ofthe artat the time, though weare learning a lot more about the experiment during the commissioning period. The detector is in operation, and working spectacularly well.

ICTV Virus Taxonomy Profile: Parvoviridae.

Abstract: Members of the family Parvoviridae are small, resilient, non-enveloped viruses with linear, single-stranded DNA genomes of 4–6 kb. Viruses in two subfamilies, the Parvovirinae and Densovirinae, are distinguished primarily by their respective ability to infect vertebrates (including humans) versus invertebrates. Being genetically limited, most parvoviruses require actively dividing host cells and are host and/or tissue specific. Some cause diseases, which range from subclinical to lethal. A few require co-infection with helper viruses from other families. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the Parvoviridae, which is available at www.ictv.global/report/parvoviridae.