Estimation of Commodity Specific Production Costs Using German Farm Accountancy Data

A central problem in estimating per unit costs of production originates from the fact that most farms produce multiple outputs and standard farm-accounting data are only available at the whole-farm level. The seemingly unrelated regression (SUR) approach is used to estimate per unit production costs based on German farm accountancy data. Special emphasis is put on outlier detection prior to the estimation of production costs to increase the robustness of the results. Outlier observations are identified based on the Mahalanobis distance for each observation on the data set. It was observed that less negative cost coefficients are estimated after the exclusion of the outliers. The time series analysis of cost estimation based on SUR regression shows the costs of arable crops after 2004, affected by rising prices of fertilizer, seeds and energy, while the increase of livestock production costs after 2006 is attributed to feed costs.Multi-output, outlier detection, production costs, Seemingly Unrelated Regression, Agricultural Finance,

Organic farming: implications for costs of production and provisioning of environmental services

The report is part of the project 'Farm Accountancy Cost Estimation and Policy Analysis of European Agriculture' (FACEPA). The overall aim of this report is to contrast organic and conventional forms of commodity production in terms of costs and environmental performance. Specific objectives are to apply the General Cost of Production Model (GECOM) developed in the FACEPA project to organic farms, to compare GECOM results for organic farming to data from other national studies as part of a (quasi-)validation, to discuss production costs in organic farming in the light of the structure of the organic farming sector and the respective policy environment in selected EU Member States, and to explore the potential of FADN systems for deriving environmental impacts at farm level, calculating and comparing selected indicators for organic farms. The report is structured as follows: First, a short overview is given of the structure of the organic farming sector and the respective policy environment in selected study countries (Chapter 2). Chapter 3 provides a description and discussion of production costs in organic farming collected from various other national sources, paying specific attention to the impact of different methodological approaches used in the available studies. In Chapter 4, the GECOM estimations for fully organic farms of EU FADN are illustrated and compared to other national production cost data to provide a quasi-validation of the GECOM estimates. Chapter 5 presents a comparison of the GECOM estimates for production costs in organic and conventional farming. The final Chapter 6 then illustrates the potential of identifying environmental impacts based on FADN data

Orbital inflammation and colitis in pediatric IgG4-related disease: A case report and review of the literature.

IgG4-related disease (IgG4-RD) is an inflammatory disorder characterized by tumor-like swelling in one or more organs, elevated serum IgG4 levels, and histological alterations with infiltration of IgG4-positive plasma cells. IgG4-RD is rare and likely underdiagnosed in children. We report a case of a 16-year-old girl with IgG4-positive colitis that developed weeks after IgG4-related ophthalmic disease and discuss diagnosis and treatment in the context of the literature available. Since the pathophysiology of IgG4-RD is unknown, treatment options are empiric and, for the most part, untargeted. Systemic corticosteroid treatment is the basis of anti-inflammatory treatment in IgG4-RD and induced early remission in our patient. During corticosteroid taper, the patient developed weight loss and intestinal inflammation. Histopathological assessment of the intestinal walls confirmed IgG4-positive colitis. Immune-modulating treatment with non-biologic (e.g., methotrexate (MTX) and mycophenolate mofetil) or biologic (rituximab) disease-modifying antirheumatic drugs has been reported in treatment refractory or corticosteroid-dependent patients. The patient responded to treatment with anti-inflammatory therapy with food rich in TGF-β2 (modulen) and MTX. This is one of the first pediatric patients reported with IgG4-related colitis extending the phenotype of pediatric IgG4-RD. International collaboration to prospectively document clinical presentation and treatment responses may help to further establish the phenotype and treatment options and to raise awareness for IgG4-RD

Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors

Background: Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. Methods: In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. Findings: When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2 weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67+ regulatory T cells (Tregs) and Ki-67+ CD8+ T cells is also likely to be associated with increased risk of irAEs. Conclusions: We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. Funding: This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen

Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors.

BACKGROUND Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. METHODS In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. FINDINGS When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2 weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67+ regulatory T cells (Tregs) and Ki-67+ CD8+ T cells is also likely to be associated with increased risk of irAEs. CONCLUSIONS We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. FUNDING This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives