Consumption and food production in crisis time: Swiss consumption co-operations as farmers at the End of World War I

During the last two years of World War I food supply in Switzerland declined and caused shortcomings in consume, leading to social distress and conflict. Mainly two important factors caused these problems: First, Switzerland was highly dependent on food imports and during the war traditional supply lines faded. Second, weather extremes in the years 1916–1917 caused crop failure all over Europe and North America, which intensified the decline of food trade between the nations. In 1918 a conflict between classic urban consumers, such as workers, and famers erupted due to the food shortcomings and led to a lasting discord between urban and agrarian regions in Switzerland. But there was not only disharmony and conflict between the urban and agrarian regions. As a matter of fact several agents (urban and agrarian) interested in presenting adequate coping strategies to overcome the food shortages developed ideas of alternative ways of food production and supply since 1917. The aim of the paper is to outline these strategies that were undertaken to create a new era of food production that was not solely dependent on the agrarian sector or the import-trade. Actual growing of vegetables in estate areas is an important, but just one, factor of establishing a new system of food production, distribution and consume. The market-leading grocery stores in Switzerland nowadays (Coop and Migros) started their business during that time as co-operatives establishing new forms of distribution and food-production. So the interest of the paper is not only in actual «urban farming», but it wants to share some light on how swiss urban and agrarian spheres overlapped their functions in order to create a modern system of agro food-chains at the beginning of the interwar period.

ADDPLOT: Stata module to add twoway plot objects to an existing twoway graph

addplot adds twoway plot objects to an existing twoway graph. This is useful if you want to add additional objects such as titles or extra data points to a twoway graph after it has been created. Most of what addplot can do, can also be done by rerunning the original graph command including additional options or plot statements. addplot, however, might be useful if you have to modify a graph for which you cannot rerun the original command, for example, because you only have the graph file but not the data that were used to create the graph. Furthermore, addplot can do certain things that would be difficult to achieve in a single graph command (e.g. customizing individual subgraphs within a by-graph). addplot also provides a substitute for some of the functionality of the graph editor.

A note on adding objects to an existing twoway graph

In Stata, graphs are usually generated by one call to the graph command. Sometimes, however, it would be convenient to be able to add objects to a graph after the graph has been created. In this article, I provide a command called addplot that offers such functionality for twoway graphs, capitalizing on an undocumented feature of Stata's graphics system.

Risk Factors for Sternal Wound Infection After Open Heart Operations Vary According to Type of Operation

BACKGROUND This study evaluated whether risk factors for sternal wound infections vary with the type of surgical procedure in cardiac operations. METHODS This was a university hospital surveillance study of 3,249 consecutive patients (28% women) from 2006 to 2010 (median age, 69 years [interquartile range, 60 to 76]; median additive European System for Cardiac Operative Risk Evaluation score, 5 [interquartile range, 3 to 8]) after (1) isolated coronary artery bypass grafting (CABG), (2) isolated valve repair or replacement, or (3) combined valve procedures and CABG. All other operations were excluded. Univariate and multivariate binary logistic regression were conducted to identify independent predictors for development of sternal wound infections. RESULTS We detected 122 sternal wound infections (3.8%) in 3,249 patients: 74 of 1,857 patients (4.0%) after CABG, 19 of 799 (2.4%) after valve operations, and 29 of 593 (4.9%) after combined procedures. In CABG patients, bilateral internal thoracic artery harvest, procedural duration exceeding 300 minutes, diabetes, obesity, chronic obstructive pulmonary disease, and female sex (model 1) were independent predictors for sternal wound infection. A second model (model 2), using the European System for Cardiac Operative Risk Evaluation, revealed bilateral internal thoracic artery harvest, diabetes, obesity, and the second and third quartiles of the European System for Cardiac Operative Risk Evaluation were independent predictors. In valve patients, model 1 showed only revision for bleeding as an independent predictor for sternal infection, and model 2 yielded both revision for bleeding and diabetes. For combined valve and CABG operations, both regression models demonstrated revision for bleeding and duration of operation exceeding 300 minutes were independent predictors for sternal infection. CONCLUSIONS Risk factors for sternal wound infections after cardiac operations vary with the type of surgical procedure. In patients undergoing valve operations or combined operations, procedure-related risk factors (revision for bleeding, duration of operation) independently predict infection. In patients undergoing CABG, not only procedure-related risk factors but also bilateral internal thoracic artery harvest and patient characteristics (diabetes, chronic obstructive pulmonary disease, obesity, female sex) are predictive of sternal wound infection. Preventive interventions may be justified according to the type of operation.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

Effect of calf purchase and other herd-level risk factors on mortality, unwanted early slaughter, and use of antimicrobial group treatments in Swiss veal calf operations.

The objective of this survey was to determine herd level risk factors for mortality, unwanted early slaughter, and metaphylactic application of antimicrobial group therapy in Swiss veal calves in 2013. A questionnaire regarding farm structure, farm management, mortality and antimicrobial use was sent to all farmers registered in a Swiss label program setting requirements for improved animal welfare and sustainability. Risk factors were determined by multivariable logistic regression. A total of 619 veal producers returned a useable questionnaire (response rate=28.5%), of which 40.9% only fattened their own calves (group O), 56.9% their own calves and additional purchased calves (group O&P), and 2.3% only purchased calves for fattening (group P). A total number of 19,077 calves entered the fattening units in 2013, of which 21.7%, 66.7%, and 11.6% belonged to groups O, O&P, and P, respectively. Mortality was 0% in 322 herds (52.0%), between 0% and 3% in 47 herds (7.6%), and ≥3% in 250 herds (40.4%). Significant risk factors for mortality were purchasing calves, herd size, higher incidence of BRD, and access to an outside pen. Metaphylaxis was used on 13.4% of the farms (7.9% only upon arrival, 4.4% only later in the fattening period, 1.1% upon arrival and later), in 3.2% of the herds of group O, 17.9% of those in group O&P, and 92.9% of those of group P. Application of metaphylaxis upon arrival was positively associated with purchase (OR=8.9) and herd size (OR=1.2 per 10 calves). Metaphylaxis later in the production cycle was positively associated with group size (OR=2.9) and risk of respiratory disease (OR=1.2 per 10% higher risk) and negatively with the use of individual antimicrobial treatment (OR=0.3). In many countries, purchase and a large herd size are inherently connected to veal production. The Swiss situation with large commercial but also smaller herds with little or no purchase of calves made it possible to investigate the effect of these factors on mortality and antimicrobial drug use. The results of this study show that a system where small farms raise the calves from their own herds has a substantial potential to improve animal health and reduce antimicrobial drug use.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

Building the View Graph of a Category by Exploiting Image Realism

We propose a weakly supervised method to arrange images of a given category based on the relative pose between the camera and the object in the scene. Relative poses are points on a sphere centered at the object in a given canonical pose, which we call object viewpoints. Our method builds a graph on this sphere by assigning images with similar viewpoint to the same node and by connecting nodes if they are related by a small rotation. The key idea is to exploit a large unlabeled dataset to validate the likelihood of dominant 3D planes of the object geometry. A number of 3D plane hypotheses are evaluated by applying small 3D rotations to each hypothesis and by measuring how well the deformed images match other images in the dataset. Correct hypotheses will result in deformed images that correspond to plausible views of the object, and thus will likely match well other images in the same category. The identified 3D planes are then used to compute affinities between images related by a change of viewpoint. We then use the affinities to build a view graph via a greedy method and the maximum spanning tree.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

CPRPLOT2: Stata module to graph component-plus-residual plots for transformed regressors

cprplot2 is a variation of official Stata's cprplot and is used for graphing component-plus-residual plots (a.k.a. partial residual plots). Additional features (compared to cprplot): (1) cprplot2 can handle variables that enter the model repeatedly via different transformations (for example, polynomials). (2) cprplot2 can display component-plus-residual plots using the original units for transformed variables in the model. (3) A wrapper is provided to quickly display several component-plus-residual plots in a single image.