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Terminology is the basis for communication among medical professionals. For anatomists, their daily work is based on the Terminologia Anatomica (TA), while pathologists cite the Tumor Node Metastasis (TNM) classification when referring to the anatomical boundaries and regions of malignant tumors. Terminologia Anatomica and clinical-based classifications, including the TMN classification of tumors, use a world-wide standardized nomenclature, which has been revised regularly to incorporate new anatomical discoveries and clinically relevant structures. In medical education, students are familiarized with medical nomenclatures in anatomy textbooks and online learning platforms. Sometime, descriptions and illustrations in anatomy teaching materials put a different focus on the importance of anatomical subdivisions and their borders than is found in cancer classifications. This discrepancy contrasts with the efforts of medical societies to define and implement clinically relevant anatomical structures, including organ subdivisions and their boundaries, in daily clinical practice. Here, we illustrate this problem using the larynx and pancreas as examples. Anatomy education should coordinate teaching content with the requirements of the clinical disciplines.

Textbooks covering normal human histology illustrate an allegedly normal gastric mucosa containing significant infiltrates of mononuclear cells in the lamina propria. This standard description seems to conflict with the pathologist's criterion for normality, which specifies only a few or a complete absence of inflammatory cells. Eventually, both anatomists and pathologists face the dilemma: how much infiltrate should their students and medical colleagues be told is acceptable for the gastric mucosa to be classified as normal? Summarizing the suggestions of experts in gastroenterology and our own experience, a simple approach could be to accept no more than five mononuclear and plasma cells per high power field as normal (400-fold magnification with a field area of 0.24 mm

The dramatic improvements in combinatorial optimization algorithms over the last decades have had a major impact in artificial intelligence, operations research, and beyond, but the output of current state-of-the-art solvers is often hard to verify and is sometimes wrong. For Boolean satisfiability (SAT) solvers proof logging has been introduced as a way to certify correctness, but the methods used seem hard to generalize to stronger paradigms. What is more, even for enhanced SAT techniques such as parity (XOR) reasoning, cardinality detection, and symmetry handling, it has remained beyond reach to design practically efficient proofs in the standard DRAT format. In this work, we show how to instead use pseudo-Boolean inequalities with extension variables to concisely justify XOR reasoning. Our experimental evaluation of a SAT solver integration shows a dramatic decrease in proof logging and verification time compared to existing DRAT methods. Since our method is a strict generalization of DRAT, and readily lends itself to expressing also 0-1 programming and even constraint programming problems, we hope this work points the way towards a unified approach for efficient machine-verifiable proofs for a rich class of combinatorial optimization paradigms.

Subsidizing polluting industries generally leads to increased pollution locally. However, given the diversity of production technologies across countries and international trade, the global impact of unilateral policies is not a priori clear. We use the agricultural sector model CAPRI to simulate the impact of removing the voluntary coupled support for ruminants, presently permitted under the EU Common Agricultural Policy. We find that this reduces greenhouse gas emissions in the EU. However, emissions leakage significantly diminishes the global mitigation effect since about 3/4 of the reduction in the EU is offset by increased emissions in the rest of the world.

In most cases, squamous cell carcinoma of the anus (SCCA) is treated with chemo-radiotherapy preserving sphincter function and offering good long-term survival and low recurrence rates. However, chemo-radiotherapy has several side effects: dyspareunia, impotence, fecal incontinence, pain, and skin symptoms. Small/T1 tumors, without metastatic disease, can be treated with local excision alone. We aimed to systematically review the literature regarding outcome following local excision of T1 SCCA. PubMed and Embase databases were searched for studies that investigated outcome following local excision of SCCA. Twenty-three studies were included. Twenty of the studies were retrospective, and three studies included more than 100 patients. Most of the studies were published before the 1980s. Overall there was great heterogeneity and missing data across the included studies when comparing patient demographics, resection margins, definitions on tumor location, and outcome. Overall 5-year survival was 69% (95% CI 66–72) following local excision. Overall 5-year recurrence was 37% (95% CI 30–45) following local excision. No complications were reported following local excision. The current literature on outcome following local excision of T1 anal cancers consists predominantly of smaller, retrospective, and heterogenous studies. Overall 5-year survival is acceptable, but worse than following chemo-radiation therapy. However, local excision seems to have no or only few minor complications. Recurrence rates are high. Therefore, a thorough follow-up program is needed when performing local excision as primary treatment for T1 SCCA. There is an evident need for further studies.

Supplementalmaterial for the AAAI-21 paper "Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs".

While many ideas and proofs of concept for parallel-in-time integration methods exists, the number of large-scale, accessible time-parallel codes is rather small. This is often due to the apparent or subtle complexity of the algorithms and the many pitfalls awaiting developers of parallel numerical software. One example of such a time-parallel code is pySDC, which implements, among others, the parallel full approximation scheme in space and time (PFASST). Inspired by nonlinear multigrid ideas, PFASST allows to integrate multiple time steps simultaneously using a space-time hierarchy of spectral deferred corrections. In this paper, we demonstrate the application of performance analysis tools to the PFASST implementation pySDC. We trace the path we took for this work, show examples of how the tools can be applied, and explain the sometimes surprising findings we encountered. Although focusing only on a single implementation of a particular parallel-in-time integrator, we hope that our results and in particular the way we obtained them are a blueprint for other time-parallel codes.

Within the last years, Python became more prominent in the scientific community and is now used for simulations, machine learning, and data analysis. All these tasks profit from additional compute power offered by parallelism and offloading. In the domain of High Performance Computing (HPC), we can look back to decades of experience exploiting different levels of parallelism on the core, node or inter-node level, as well as utilising accelerators. By using performance analysis tools to investigate all these levels of parallelism, we can tune applications for unprecedented performance. Unfortunately, standard Python performance analysis tools cannot cope with highly parallel programs. Since the development of such software is complex and error-prone, we demonstrate an easy-to-use solution based on an existing tool infrastructure for performance analysis. In this paper, we describe how to apply the established instrumentation framework Score-P to trace Python applications. We finish with a study of the overhead that users can expect for instrumenting their applications.

The intensity of land use and management in permanent grasslands affects both biodiversity and important ecosystem services. Comprehensive knowledge about these intensities is a crucial factor for sustainable decision-making in landscape policy. For meadows, the management intensity can be described by proxies such as the mowing frequency, usually, a higher number of cuts indicate higher intensities. Dense time series of medium resolution (10–30 m) remote sensing data are suitable for the detection of mowing events. However, existing studies revealed a general lack of consensus about the most appropriate input data set for a consistent and reliable mowing detection. We systematically evaluated the synergistic use of acquisitions from Sentinel-1, Sentinel-2, and Landsat 8 to detect the occurrence, frequency, and date of mowing events as an indicator of grassland management intensity. Dense time series of NDVI (Sentinel-2 and Landsat 8), γ0 backscatter, backscatter cross-ratio, backscatter second-order texture metrics as well as 6-day interferometric coherence (Sentinel-1) were used as input features. All possible combinations of input features were tested to train a one-dimensional convolutional neural network, which enables enhanced exploitation of the temporal domain of the data. The evaluation was conducted on 64 meadows for an overall of 257 mowing events from 2017 to 2019 in Germany. Our results revealed that the combination of input features improves the detection performance. The highest overall accuracy was reached by a combination of NDVI, backscatter cross-ratio, and interferometric coherence with an F1-Score of 0.84. The mowing frequency was predicted with a mean absolute error of 0.38 events per year, while the date of the events was missed by 3.79 days on average. NDVI time series alone mostly underperformed in comparison to optical/SAR combinations but clearly outperformed input-sets that were solely based on SAR features. The proposed model performed well for meadows with low to medium management intensities but further testing is recommended for highly intensive managed parcels. The results clearly demonstrate the additional value of fusing time series of the three present Earth observation systems that deliver a freely available global coverage of the land surface at medium resolution.

In this paper, we analyse the drivers of farm structural change in the EU-27, applying a novel analytical framework in the field of agricultural economics known as the multiplicative competitive interaction (MCI) model. MCI offers a more parsimonious specification for estimating models of regional farm group shares compared to the often-applied Markov approach. The MCI framework enables farm group-specific equations, which are used to account for drivers specific to certain farm groups. The MCI framework explains farm group shares at the regional level taken from the Farm Structure Survey (FSS) using socio-economic variables from the Farm Accountancy Data Network (FADN) and other databases for the period 1989–2013. We consider eight production specialisations and two size classes at the NUTS 2 regional level. The results indicate that the past farm structure explains approximately 36 per cent of the EU farm structure variation across regions and time, followed by natural conditions (16 per cent), agricultural prices (14 per cent), macroeconomic variables (9 per cent), subsidies (7 per cent), population (6 per cent) and agricultural income (6 per cent). Further, we have run a simulation experiment where we derived elasticities of structural change with respect to time-varying variables. The structural change appears to be the most elastic with respect to income and macroeconomic variables.

Background Cardiac allograft vasculopathy (CAV) is the main obstacle for long-term survival after heart transplantation. Alloimmune mediated chronic vascular rejection results in several mechanisms like platelet activation, immigration of inflammatory cells through the endothelial layer and proliferation and migration of smooth muscle cells (SMCs). Serotonin (5-HT) promotes these processes via activation of 5-HT2 receptors. We hypothesized that inhibiting 5-HT2 receptors ameliorates the development of CAV. Methods CBA/JRj mice recieved aortic grafts from C57BL/6 mice. After transplantation until recovery of organs, recipients were treated with serotonin receptor antagonists: sarpogrelate (5-HT2A), SB 204741 (5-HT2B) or terguride (5-HT2A+B). Mice were sacrificed after 14 days for qRT-PCR analysis or after 30 days for histological evaluation. Serum serotonin ELISA was done at both time points. Results Elevated serum serotonin levels were significantly reduced after 5-HT2A antagonist treatment as was 5-HT2A receptor expression. This went along with reduced inflammation characterized by significantly fewer infiltrating macrophages and pro-inflammatory intragraft cytokines and with reduced tissue remodeling evident as significantly less neointima formation. Conclusion Inhibition of the 5HT/5-HT2A receptor axis leads to significantly reduced neointima proliferation after aortic transplantation associated with reduced transendothelial migration of macrophages and decreased expression of inflammatory cytokines. These findings have translational implications as inhibitors of 5HT2A like sarpogrelate are already approved for clinical use.

The gold standard for the treatment of terminal heart failure and irreversible lung diseases includes thoracic organ transplantation. The major obstacle for long-term survival after successful transplantation is chronic rejection, an ongoing immunomodulatory disease so far without effective therapy. Therefore, the aim of this review is to elucidate scientific efforts targeting different new mechanisms of cardiac allograft vasculopathy (CAV) and chronic lung allograft dysfunction (CLAD). For this purpose, we performed a systematic review of the literature to assess recent strategies in transplant immunology research. We searched MEDLINE from 2015 up to date for articles addressing the following keywords: CAV, transplant vasculopathy, transplant arteriosclerosis, CLAD, bronchiolitis obliterans transplant, and obliterative bronchiolitis transplant. All articles including experimental models in the field of transplant immunology addressing new aspects for the prevention of chronic rejection after heart and lung transplantation were included in this review. The prevention of chronic rejection would clearly improve the survival of patients after heart and lung transplantation. Interesting targets were addressed in recent research, but further research is necessary to effectively treat this life-threatening disease in transplant recipients.

Efficient water management needs hydrological information provided by hydrometric networks. In arid and mountainous regions, hydrologic models for water resources management and forecasting require a large amount of data due to the temporal and spatial heterogeneity of hydrometeorological variables. The interaction of complex oceanic and atmospheric circulations makes North Chile one of the world's most arid areas. Since the onset of large mining projects in the nineties, constant population and economic growth generates high pressure on water resources. The existing regional scale hydrometric network in Tarapacá allows for the description of general characteristics and trends at national, but not at water basin level and therefore does not meet actual demands. Methods for hydrometric network design were designed for temperate areas in general. Based on a review of existing methodologies, the paper identifies multi-criteria analysis (MCA) as best adaptable to the context. It develops a methodology for hyper-arid areas, complementing MCA with stakeholder and geographic information system (GIS) analysis, as well as optimization. The paper optimizes the existing hydrometric network in the Tarapacá region, characterized by strong constraints regarding access and topography. Three MCA techniques are compared. The result is an optimized network consisting of 36 rainfall and 21 streamflow stations.

One of the most successful approaches to automated planning is the translation to propositional satisfiability (SAT). We employ incremental SAT solving to increase the capabilities of several modern encodings for SAT based planning. Experiments based on benchmarks from the 2014 International Planning Competition show that an incremental approach significantly outperforms non incremental solving. Although we are using sequential scheduling of makespans, we can outperform the state-of-the-art SAT based planning system Madagascar in the number of solved instances.

In this paper we analyse the economic and environmental impacts of CAP greening introduced by the 2013 CAP reform using the CAPRI model. CAPRI captures the farm heterogeneity across the EU and it allows to depict the implementation of the greening measures in high detail while integrating the environmental effects and the market feedback of the simulated policy changes. The simulated results reveal that the economic impacts (land use, production, price and income) of CAP greening are rather small, although some farm types, crops (fallow land and pulses) and Member States may be affected more significantly. The CAP greening will lead simultaneously to a small increase in prices and a small decrease in production. Farm income slightly increases because the price effects offset the production decline. Similarly to economic effects, the environmental impacts (GHG emissions, N surplus, ammonia emissions, soil erosion, and biodiversity-friendly farming practices) of CAP greening are small, although some regions may see greater effects than others. In general, the environmental effects at EU level are positive on a per hectare basis, but the increase in UAA can reverse the sign for total impacts. Overall, simulated GHG and ammonia emissions decrease in the EU, while the total N surplus, soil erosion and biodiversity-friendly farming practices indicator slightly increase due to the CAP greening.

Additional file 1: Tables S1, S2. Table S1: Patient data. Table S2: Criteria for staging of human CPM lesions.

The chapter 11 defines projects in a macro-economic perspective as deliberate alterations of resource allocations (reallocations). It explains how it comes that public sector projects exist in market economies and introduces a rational treatment of the heavily debated equity topic.

In Kapitel 5 fuhrt eine Diskussion uber Opportunitatskosten, Subventionen und unterschiedliche Zeithorizonte zur Offentlich-Privaten Partnerschaft.

In chapter 14 a discussion of opportunity costs, subsidies and time horizons leads to public-private partnerships.

Given a Boolean formula, the problem of counting seeks to estimate the number of solutions of F while the problem of uniform sampling seeks to sample solutions uniformly at random. Counting and uniform sampling are fundamental problems in computer science with a wide range of applications ranging from constrained random simulation, probabilistic inference to network reliability and beyond. The past few years have witnessed the rise of hashing-based approaches that use XOR-based hashing and employ SAT solvers to solve the resulting CNF formulas conjuncted with XOR constraints. Since over 99% of the runtime of hashing-based techniques is spent inside the SAT queries, improving CNF-XOR solvers has emerged as a key challenge.

The conflict-driven clause learning (CDCL) paradigm has revolutionized SAT solving over the last two decades. Extending this approach to pseudo-Boolean (PB) solvers doing 0-1 linear programming holds the promise of further exponential improvements in theory, but intriguingly such gains have not materialized in practice. Also intriguingly, most PB extensions of CDCL use not the division rule in cutting planes as defined in [Cook et al., '87] but instead the so-called saturation rule. To the best of our knowledge, there has been no study comparing the strengths of division and saturation in the context of conflict-driven PB learning, when all linear combinations of inequalities are required to cancel variables. We show that PB solvers with division instead of saturation can be exponentially stronger. In the other direction, we prove that simulating a single saturation step can require an exponential number of divisions. We also perform some experiments to see whether these phenomena can be observed in actual solvers. Our conclusion is that a careful combination of division and saturation seems to be crucial to harness more of the power of cutting planes.

System self-tuning is a crucial task to lower the energy consumption of computers. Traditional approaches decrease the processor frequency in idle or synchronisation periods. However, in High-Performance Computing (HPC) this is not sufficient: if the executed code is load balanced, there are neither idle nor synchronisation phases that can be exploited. Therefore, alternative self-tuning approaches are needed, which allow exploiting different compute characteristics of HPC programs. The novel notion of application regions based on function call stacks, introduced in the Horizon 2020 Project READEX, allows us to define such a self-tuning approach. In this paper, we combine these regions with the Q-Learning typical state-action maps, which save information about available states, possible actions to take, and the expected rewards. By exploiting the existing processor power interface, we are able to provide direct feedback to the learning process. This approach allows us to save up to 15% energy, while only adding a minor runtime overhead., 4 pages short paper, HPCS 2019, AHPC 2019, READEX, HAEC, Horizon2020, H2020 grant agreement number 671657, DFG, CRC 912

Summary: Pro-inflammatory caspase-1 is a key player in innate immunity. Caspase-1 processes interleukin (IL)-1β and IL-18 to their mature forms and triggers pyroptosis. These caspase-1 functions are linked to its enzymatic activity. However, loss-of-function missense mutations in CASP1 do not prevent autoinflammation in patients, despite decreased IL-1β production. In vitro data suggest that enzymatically inactive caspase-1 drives inflammation via enhanced nuclear factor κB (NF-κB) activation, independent of IL-1β processing. Here, we report two mouse models of enzymatically inactive caspase-1-C284A, demonstrating the relevance of this pathway in vivo. In contrast to Casp1−/− mice, caspase-1-C284A mice show pronounced hypothermia and increased levels of the pro-inflammatory cytokines tumor necrosis factor alpha (TNF-α) and IL-6 when challenged with lipopolysaccharide (LPS). Caspase-1-C284A signaling is RIP2 dependent and mediated by TNF-α but independent of the NLRP3 inflammasome. LPS-stimulated whole blood from patients carrying loss-of-function missense mutations in CASP1 secretes higher amounts of TNF-α. Taken together, these results reveal non-canonical caspase-1 signaling in vivo. : Reinke et al. show that enzymatically inactive caspase-1-C284A mediates non-canonical caspase-1 signaling. This pathway is RIP2 dependent and mediated by TNF-α but independent from IL-1 cytokines. Keywords: caspase-1, Rip2, NF-κB, TNF-α, non-canonical caspase-1 signaling, enzymatic activity

Background Severe acute pancreatitis is associated with high morbidity and mortality. Melatonin is known as the activator of antioxidant enzymes. The main purpose of this study was to evaluate the clinical effect of melatonin treatment in a pig model with induced acute pancreatitis. Methods In this study, acute pancreatitis was induced in 38 German domestic pigs (German Hybrid). After induction of acute pancreatitis, 18 animals were treated with melatonin. Intraoperative clinical data, postoperative blood parameters, fitness, and Porcine Well-being (PWB) score, and post-mortal histopathological data were analyzed in both study groups. Results The matching procedure created two groups (melatonin group and control group) which were very similar. The fitness and PWB score were postoperative significantly enhanced in the melatonin group as compared to the control group (p = 0.005 and p = 0.003). Additionally, histological analysis revealed that acinar necrosis, fat tissue necrosis, and edema were significantly reduced in the melatonin group as compared to the non-melatonin group (p = 0.025, p = 0.003, and p = 0.028). Conclusions Pigs, which were treated with melatonin, were characterized by higher fitness and PWB scores than those of the control group. Moreover, melatonin treatment reduces the acinar necrosis, fat tissue necrosis, and edema of pancreatic tissue. Thus, melatonin might be a useful therapeutic option in severe acute pancreatitis. Electronic supplementary material The online version of this article (10.1186/s13017-019-0237-2) contains supplementary material, which is available to authorized users.

Accounting for more than half of the European Union's (EU) territory, agriculture ensures food production, manages important natural resources and supports socio-economic development of rural areas. Moreover, it is estimated that 50% of all plant and animal species (including some of that are listed in the EU Habitat Directive) depend on agricultural practices. The continuation of appropriate agricultural land management is essential to ensure these primary functions. Avoidance of farmland abandonment is therefore an important rationale for the EU's Common Agricultural Policy which requires improved knowledge of this phenomenon at the European level. This study assesses the risk of farmland abandonment in the 27 EU Member States. It summarizes the work performed by an expert panel of European scientists and national representatives which aimed to identify the main drivers of farmland abandonment in Europe, to define indicators for assessing its risk of occurrence and to test the value of European-wide data sources to achieve these aims. Drivers were identified under two rationales: low farm stability and viability, and negative regional context. Indicators were defined using recent socio-economic farm data and geospatial datasets. Some indicators were then combined to make a composite risk indicator. Regions with higher risk of farmland abandonment are located in Portugal, Spain, Italy, Greece, Latvia, Estonia, Finland, Sweden and Ireland. This paper demonstrates the challenges of performing a European-wide assessment of a phenomenon influenced by drivers whose effects vary at local levels. Other problems encountered are data heterogeneity in terms of spatial resolution and quality, as well as access to micro-data (local level data). High spatial resolution European datasets measuring farmland abandonment are needed to validate the defined indicators as well as to benchmark the methodology. Furthermore, such data could be used to establish a weighting system for the drivers.