Abstract: The rational design of metal–organic frameworks (MOFs) with hollow features and tunable porosity at the nanoscale can enhance their intrinsic properties and stimulates increasing attentions. In this Communication, we demonstrate that methanol can affect the coordination mode of ZIF-67 in the presence of Co2+ and induces a mild phase transformation under solvothermal conditions. By applying this transformation process to the ZIF-67@ZIF-8 core–shell structures, a well-defined hollow Zn/Co ZIF rhombic dodecahedron can be obtained. The manufacturing of hollow MOFs enables us to prepare a noble metal@MOF yolk-shell composite with controlled spatial distribution and morphology. The enhanced gas storage and porous confinement that originate from the hollow interior and coating of ZIF-8 confers this unique catalyst with superior activity and selectivity toward the semi-hydrogenation of acetylene.

Abstract: At the time of writing this commentary (February 2020), the coronavirus COVID-19 epidemic has already resulted in more fatalities compared with the SARS and MERS coronavirus epidemics combined. Therapeutics that may assist to contain its rapid spread and reduce its high mortality rates are urgently needed. Developing vaccines against the SARS-CoV-2 virus may take many months. Moreover, vaccines based on viral-encoded peptides may not be effective against future coronavirus epidemics, as virus mutations could make them futile. Indeed, new Influenza virus strains emerge every year, requiring new immunizations. A tentative suggestion based on existing therapeutics, which would likely be resistant to new coronavirus mutations, is to use available angiotensin receptor 1 (AT1R) blockers, such as losartan, as therapeutics for reducing the aggressiveness and mortality from SARS-CoV-2 virus infections. This idea is based on observations that the angiotensin-converting enzyme 2 (ACE2) very likely serves as the binding site for SARS-CoV-2, the strain implicated in the current COVID-19 epidemic, similarly to strain SARS-CoV implicated in the 2002-2003 SARS epidemic. This commentary elaborates on the idea of considering AT1R blockers as tentative treatment for SARS-CoV-2 infections, and proposes a research direction based on datamining of clinical patient records for assessing its feasibility.

Abstract: Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.

Abstract: Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.

Abstract: We report a measurement of the branching fraction ratios R(D)(()*()) of (B) over bar -> D-(*())tau(-)(nu) over bar (tau) relative to (B) over bar -> D-(*())l(-)(nu) over barl (where l = e or mu) using the full Belle data sample of 772 x 10(6)B (B) over bar pairs collected at the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric-energy e(+)e(-) collider. The measured values are R(D) = 0.375 +/- 0.064(stat) +/- 0.026(syst) and R(D*) = 0.293 +/- 0.038 (stat) +/- 0.015 (syst). The analysis uses hadronic reconstruction of the tag-side B meson and purely leptonic t decays. The results are consistent with earlier measurements and do not show a significant deviation from the standard model prediction.

Abstract: Integrins, and integrin-mediated adhesions, have long been recognized to provide the main molecular link attaching cells to the extracellular matrix (ECM) and to serve as bidirectional hubs transmitting signals between cells and their environment. Recent evidence has shown that their combined biochemical and mechanical properties also allow integrins to sense, respond to and interact with ECM of differing properties with exquisite specificity. Here, we review this work first by providing an overview of how integrin function is regulated from both a biochemical and a mechanical perspective, affecting integrin cell-surface availability, binding properties, activation or clustering. Then, we address how this biomechanical regulation allows integrins to respond to different ECM physicochemical properties and signals, such as rigidity, composition and spatial distribution. Finally, we discuss the importance of this sensing for major cell functions by taking cell migration and cancer as examples.

Abstract: Objective: The National Central Cancer Registry (NCCR) collected population-based cancer registration data in 2011 from all cancer registries. National cancer incidence and mortality were compiled and cancer incident new cases and cancer deaths were estimated. Methods: In 2014, there were 234 cancer registries submitted cancer incidence and deaths occurred in 2011. All datasets were checked and evaluated based on the criteria of data quality from NCCR. Total 177 registries’ data were qualified and compiled for cancer statistics in 2011. The pooled data were stratified by area (urban/rural), gender, age group (0, 1-4, 5-9, 10-14…85+) and cancer type. Cancer incident cases and deaths were estimated using age-specific rates and national population in 2011. All incidence and death rates are age-standardized to the 2000 Chinese standard population and Segi’s population expressed per 100,000 persons. Results: All 177 cancer registries (77 in urban and 100 in rural areas) covered 175,310,169 populations (98,341,507 in urban and 76,968,662 in rural areas). The morphology verified cases (MV%) accounting for 70.14% and 2.44% of incident cases were identified through death certifications only (DCO%) with mortality to incidence ratio of 0.63. The estimates of new cancer incident cases and cancer deaths were 3,372,175 and 2,113,048 in 2011, respectively. The incidence rate was 250.28/100,000 (males 277.77/100,000, females 221.37/100,000), and the age-standardized incidence rates by Chinese standard population (ASIRC) and by world standard population (ASIRW) were 186.34/100,000 and 182.76/100,000 with the cumulative incidence rate (0-74 years old) of 21.20%. The cancer incidence and ASIRC in urban areas were 261.38/100,000 and 189.89/100,000 compared to 238.60/100,000 and 182.10/100,000 in rural areas, respectively. The cancer mortality was 156.83/100,000 (194.88/100,000 in males and 116.81/100,000 in females), the age-standardized mortality rates by Chinese standard population (ASMRC) and by world standard population (ASMRW) were 112.88/100,000 and 111.82/100,000, and the cumulative mortality rate (0-74 years old) was 12.69%. The cancer mortality and ASMRC were 154.37/100,000 and 108.20/100,000 in urban areas, and 159.42/100,000 and 117.97/100,000 in rural areas, respectively. Cancers of lung, female breast, stomach, liver, colon and rectum, esophageal, cervix, uterus, prostate and ovary were the most common cancers, accounting for about 75% of all cancer new cases. Lung cancer, liver cancer, stomach cancer, esophageal cancer, colorectal cancer, female breast cancer, pancreatic cancer, brain tumor, cervical cancer and leukemia were the leading causes of cancer death, accounting for about 80% of all cancer deaths. The cancer incidence, mortality and spectrum showed difference between urban and rural areas, males and females. Conclusions: The coverage of cancer registration population had a greater increase than that in the last year. The data quality and representativeness are gradually improved. As the basic work of cancer prevention and control, cancer registry is playing an irreplaceable role. The disease burden of cancer is increasing, and the health department has to take effective measures to contain the increased cancer burden in China.

Abstract: The development of an artificial photosynthetic system is a promising strategy to convert solar energy into chemical fuels. Here, a direct Z-scheme CdS-WO(3) photocatalyst without an electron mediator is fabricated by imitating natural photosynthesis of green plants. Photocatalytic activities of as-prepared samples are evaluated on the basis of photocatalytic CO(2) reduction to form CH(4) under visible light irradiation. These Z-scheme-heterostructured samples show a higher photocatalytic CO(2) reduction than single-phase photocatalysts. An optimized CdS-WO(3) heterostructure sample exhibits the highest CH(4) production rate of 1.02 μmol h(-1) g(-1) with 5 mol% CdS content, which exceeds the rates observed in single-phase WO(3) and CdS samples for approximately 100 and ten times under the same reaction condition, respectively. The enhanced photocatalytic activity could be attributed to the formation of a hierarchical direct Z-scheme CdS-WO(3) photocatalyst, resulting in an efficient spatial separation of photo-induced electron-hole pairs. Reduction and oxidation catalytic centers are maintained in two different regions to minimize undesirable back reactions of the photocatalytic products. The introduction of CdS can enhance CO(2) molecule adsorption, thereby accelerating photocatalytic CO(2) reduction to CH(4). This study provides novel insights into the design and fabrication of high-performance artificial Z-scheme photocatalysts to perform photocatalytic CO(2) reduction.

Abstract: The essential role of the gut microbiota for health has generated tremendous interest in modulating its composition and metabolic function. One of these strategies is prebiotics, which typically refer to selectively fermented nondigestible food ingredients or substances that specifically support the growth and/or activity of health-promoting bacteria that colonize the gastrointestinal tract. In this Perspective, we argue that advances in our understanding of diet-microbiome-host interactions challenge important aspects of the current concept of prebiotics, and especially the requirement for effects to be 'selective' or 'specific'. We propose to revise this concept in an effort to shift the focus towards ecological and functional features of the microbiota more likely to be relevant for host physiology. This revision would provide a more rational basis for the identification of prebiotic compounds, and a framework by which the therapeutic potential of modulating the gut microbiota could be more fully materialized.

Abstract: Mono- and di-quaternized 4,4'-bipyridine derivatives constitute a family of heterocyclic compounds, which in recent years have been employed in numerous applications. These applications correspond to various disciplines of research and technology. In their majority, two key features of these 4,4'-bipyridine-based derivatives are exploited: their redox activity and their electrochromic aptitude. Contemporary materials and compounds encompassing these skeletons as building blocks are often characterized as multifunctional, as their presence often gives rise to interesting phenomena, e.g., various types of chromism. This research trend is acknowledged, and, in this review article, recent examples of multifunctional chromic materials/compounds of this class are presented. Emphasis is placed on solvent-/medium- and environment-responsive 4,4'-bipyridine derivatives. Two important classes of 4,4'-bipyridine-based products with solvatochromic and/or environment-responsive character are reviewed: viologens (i.e., N,N'-disubstituted derivatives) and monoquats (i.e., monosubstituted 4,4'-bipyridine derivatives). The multifunctional nature of these derivatives is analyzed and structure-property relations are discussed in connection to the role of these derivatives in various novel applications.

Abstract: Purpose: BRCA1/2 -mutated and some sporadic triple-negative breast cancers (TNBC) have DNA repair defects and are sensitive to DNA-damaging therapeutics. Recently, three independent DNA-based measures of genomic instability were developed on the basis of loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST). Experimental Design: We assessed a combined homologous recombination deficiency (HRD) score, an unweighted sum of LOH, TAI, and LST scores, in three neoadjuvant TNBC trials of platinum-containing therapy. We then tested the association of HR deficiency, defined as HRD score ≥42 or BRCA1/2 mutation, with response to platinum-based therapy. Results: In a trial of neoadjuvant platinum, gemcitabine, and iniparib, HR deficiency predicted residual cancer burden score of 0 or I (RCB 0/I) and pathologic complete response (pCR; OR = 4.96, P = 0.0036; OR = 6.52, P = 0.0058). HR deficiency remained a significant predictor of RCB 0/I when adjusted for clinical variables (OR = 5.86, P = 0.012). In two other trials of neoadjuvant cisplatin therapy, HR deficiency predicted RCB 0/I and pCR (OR = 10.18, P = 0.0011; OR = 17.00, P = 0.0066). In a multivariable model of RCB 0/I, HR deficiency retained significance when clinical variables were included (OR = 12.08, P = 0.0017). When restricted to BRCA1/2 nonmutated tumors, response was higher in patients with high HRD scores: RCB 0/I P = 0.062, pCR P = 0.063 in the neoadjuvant platinum, gemcitabine, and iniparib trial; RCB 0/I P = 0.0039, pCR P = 0.018 in the neoadjuvant cisplatin trials. Conclusions: HR deficiency identifies TNBC tumors, including BRCA1/2 nonmutated tumors more likely to respond to platinum-containing therapy. Clin Cancer Res; 22(15); 3764–73. ©2016 AACR .

Abstract: After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource ( https://atlas.ctglab.nl ).

Abstract: Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets.

Abstract: Despite major advances in vaccination over the past century, resurgence of vaccine-preventable illnesses has led the World Health Organization to identify vaccine hesitancy as a major threat to global health. Vaccine hesitancy may be fueled by health information obtained from a variety of sources, including new media such as the Internet and social media platforms. As access to technology has improved, social media has attained global penetrance. In contrast to traditional media, social media allow individuals to rapidly create and share content globally without editorial oversight. Users may self-select content streams, contributing to ideological isolation. As such, there are considerable public health concerns raised by anti-vaccination messaging on such platforms and the consequent potential for downstream vaccine hesitancy, including the compromise of public confidence in future vaccine development for novel pathogens, such as SARS-CoV-2 for the prevention of COVID-19. In this review, we discuss the current position of social media platforms in propagating vaccine hesitancy and explore next steps in how social media may be used to improve health literacy and foster public trust in vaccination.

Abstract: Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

Abstract: A variety of monolayer crystals have been proposed to be two-dimensional topological insulators exhibiting the quantum spin Hall effect (QSHE), possibly even at high temperatures. Here we report the observation of the QSHE in monolayer tungsten ditelluride (WTe 2 ) at temperatures up to 100 kelvin. In the short-edge limit, the monolayer exhibits the hallmark transport conductance, ~ e 2 / h per edge, where e is the electron charge and h is Planck’s constant. Moreover, a magnetic field suppresses the conductance, and the observed Zeeman-type gap indicates the existence of a Kramers degenerate point and the importance of time-reversal symmetry for protection from elastic backscattering. Our results establish the QSHE at temperatures much higher than in semiconductor heterostructures and allow for exploring topological phases in atomically thin crystals.

Abstract: Background Nonalcoholic steatohepatitis (NASH) is a common disease that is associated with increased morbidity and mortality, but treatment options are limited. The efficacy and safety of ...

Abstract: Inorganic perovskites such as CsPbX3 (X=Cl, Br, I) have attracted attention due to their excellent thermal stability and high photoluminescence quantum efficiency. However, the electroluminescence quantum efficiency of their light-emitting diodes was <1%. We posited that this low efficiency was a result of high leakage current caused by poor perovskite morphology, high non-radiative recombination at interfaces and perovskite grain boundaries, and also charge injection imbalance. Here, we incorporated a small amount of methylammonium organic cation into the CsPbBr3 lattice and by depositing a hydrophilic and insulating polyvinyl pyrrolidine polymer atop the ZnO electron-injection layer to overcome these issues. As a result, we obtained light-emitting diodes exhibiting a high brightness of 91,000 cd m−2 and a high external quantum efficiency of 10.4% using a mixed-cation perovskite Cs0.87MA0.13PbBr3 as the emitting layer. To the best of our knowledge, this is the brightest and most-efficient green perovskite light-emitting diodes reported to date. Hybrid organic-inorganic perovskites are garnering attention for light emitting diode (LED) applications. Employing a thin hydrophilic insulating polymer, Zhanget al. report LEDs exhibiting a brightness of 91,000 cd m−2and external quantum efficiency of 10.4% using a mixed-cation perovskite.

Abstract: We define a new topological summary for data that we call the persistence landscape. Since this summary lies in a vector space, it is easy to combine with tools from statistics and machine learning, in contrast to the standard topological summaries. Viewed as a random variable with values in a Banach space, this summary obeys a strong law of large numbers and a central limit theorem. We show how a number of standard statistical tests can be used for statistical inference using this summary. We also prove that this summary is stable and that it can be used to provide lower bounds for the bottleneck and Wasserstein distances.

Abstract: Reconfigurable intelligent surfaces (RISs) have the potential of realizing the emerging concept of smart radio environments by leveraging the unique properties of metamaterials and large arrays of inexpensive antennas In this article, we discuss the potential applications of RISs in wireless networks that operate at high-frequency bands, eg, millimeter wave (30-100 GHz) and sub-millimeter wave (greater than 100 GHz) frequencies When used in wireless networks, RISs may operate in a manner similar to relays The present paper, therefore, elaborates on the key differences and similarities between RISs that are configured to operate as anomalous reflectors and relays In particular, we illustrate numerical results that highlight the spectral efficiency gains of RISs when their size is sufficiently large as compared with the wavelength of the radio waves In addition, we discuss key open issues that need to be addressed for unlocking the potential benefits of RISs for application to wireless communications and networks

Abstract: Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs. We design, implement, and evaluate DeepXplore, the first whitebox framework for systematically testing real-world DL systems. First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques. DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets including ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.

Abstract: Heat engines convert thermal energy into mechanical work and generally involve a large number of particles. We report the experimental realization of a single-atom heat engine. An ion is confined in a linear Paul trap with tapered geometry and driven thermally by coupling it alternately to hot and cold reservoirs. The output power of the engine is used to drive a harmonic oscillation. From direct measurements of the ion dynamics, we were able to determine the thermodynamic cycles for various temperature differences of the reservoirs. We then used these cycles to evaluate the power P and efficiency η of the engine, obtaining values up to P = 3.4 × 10(-22)joules per second and η = 0.28%, consistent with analytical estimations. Our results demonstrate that thermal machines can be reduced to the limit of single atoms.

Abstract: Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

Abstract: Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.

Abstract: Preface Acknowledgments Introduction Part I THE EIGHTFOLD PATH Step One: Define the Problem Step Two: Assemble Some Evidence Step Three: Construct the Alternatives Step Four: Select the Criteria Step Five: Project the Outcomes Step Six: Confront the Trade-Offs Step Seven: Stop, Focus, Narrow, Deepen, Decide! Step Eight: Tell Your Story PART II ASSEMBLING EVIDENCE Getting Started Locating Relevant Sources Gaining Access and Engaging Assistance Conducting a Policy Research Interview Using Language to Characterize and Calibrate Protecting Credibility Strategic Dilemmas of Policy Research PART III HANDLING A DESIGN PROBLEM It's a Production System Crosswalks to the Eightfold Path Define the Problem-Focus on a Primary Outcome Construct the Alternatives-Configure the System's Organizational Structure and Its Operating Processes Select the Criteria-Define the Objectives to Be Achieved Project the Outcomes-Test Whether It Will Work Confront the Trade-Offs-Examine the System from Multiple Perspectives Design a Transition Strategy PART IV "SMART (BEST) PRACTICES" RESEARCH: UNDERSTANDING AND MAKING USE OF WHAT LOOK LIKE GOOD IDEAS FROM SOMEWHERE ELSE Develop Realistic Expectations Analyze Smart Practices Observe the Practice Describe Generic Vulnerabilities But Will It Work Here? Back to the Eightfold Path APPENDIX A SPECIMEN OF A REAL-WORLD POLICY ANALYSIS Preface Summary Reducing Consumption: More Enforcement against Typical Dealers Reducing Consumption: More Enforcement against Higher-Level Dealers Reducing Cocaine-Related Crime Conclusion Appendix B THINGS GOVERNMENTS DO Taxes Regulation Subsidies and Grants Service Provision Agency Budgets Information The Structure of Private Rights The Framework of Economic Activity Education and Consultation Financing and Contracting Bureaucratic and Political Reforms APPENDIX C UNDERSTANDING PUBLIC AND NONPROFIT INSTITUTIONS: ASKING THE RIGHT QUESTIONS Mission Environment Performance Measurement Technology Production/Delivery Processes Frontline Workers and Co-Producers Partners and Other Outsiders Centralization/Decentralization Culture and Communications Politics Leadership Change APPENDIX D STRATEGIC ADVICE ON THE DYNAMICS OF GATHERING POLITICAL SUPPORT Sequencing Timing APPENDIX E TIPS FOR WORKING WITH CLIENTS References Index

Abstract: As in many countries worldwide, as part of the consequences of the COVID-19 pandemic lockdown schools in Germany closed in March 2020 and only partially re-opened in May. Teachers were confronted with the need to adapt to online teaching. This paper presents the results of a survey of early career teachers conducted in May and June 2020. First, we analysed the extent to which they maintained social contact with students and mastered core teaching challenges. Second, we analysed potential factors (school computer technology, teacher competence such as their technological pedagogical knowledge, and teacher education learning opportunities pertaining to digital teaching and learning). Findings from regression analyses show that information and communication technologies (ICT) tools, particularly digital teacher competence and teacher education opportunities to learn digital competence, are instrumental in adapting to online teaching during COVID-19 school closures. Implications are discussed for the field of teacher education and the adoption of ICT by teachers.

Abstract: Since the colonization of land by ancestral plant lineages 450 million years ago, plants and their associated microbes have been interacting with each other, forming an assemblage of species that is often referred to as a “holobiont.” Selective pressure acting on holobiont components has likely shaped plant-associated microbial communities and selected for host-adapted microorganisms that impact plant fitness. However, the high microbial densities detected on plant tissues, together with the fast generation time of microbes and their more ancient origin compared to their host, suggest that microbe-microbe interactions are also important selective forces sculpting complex microbial assemblages in the phyllosphere, rhizosphere, and plant endosphere compartments. Reductionist approaches conducted under laboratory conditions have been critical to decipher the strategies used by specific microbes to cooperate and compete within or outside plant tissues. Nonetheless, our understanding of these microbial interactions in shaping more complex plant-associated microbial communities, along with their relevance for host health in a more natural context, remains sparse. Using examples obtained from reductionist and community-level approaches, we discuss the fundamental role of microbe-microbe interactions (prokaryotes and micro-eukaryotes) for microbial community structure and plant health. We provide a conceptual framework illustrating that interactions among microbiota members are critical for the establishment and the maintenance of host-microbial homeostasis.

Abstract: Widespread tree mortality associated with drought has been observed on all forested continents and global change is expected to exacerbate vegetation vulnerability. Forest mortality has implications for future biosphere-atmosphere interactions of carbon, water and energy balance, and is poorly represented in dynamic vegetation models. Reducing uncertainty requires improved mortality projections founded on robust physiological processes. However, the proposed mechanisms of drought-induced mortality, including hydraulic failure and carbon starvation, are unresolved. A growing number of empirical studies have investigated these mechanisms, but data have not been consistently analysed across species and biomes using a standardized physiological framework. Here, we show that xylem hydraulic failure was ubiquitous across multiple tree taxa at drought-induced mortality. All species assessed had 60% or higher loss of xylem hydraulic conductivity, consistent with proposed theoretical and modelled survival thresholds. We found diverse responses in non-structural carbohydrate reserves at mortality, indicating that evidence supporting carbon starvation was not universal. Reduced non-structural carbohydrates were more common for gymnosperms than angiosperms, associated with xylem hydraulic vulnerability, and may have a role in reducing hydraulic function. Our finding that hydraulic failure at drought-induced mortality was persistent across species indicates that substantial improvement in vegetation modelling can be achieved using thresholds in hydraulic function.