Production Workers’ Perception of Standardisation – The Relations Between Standardisation and Stress

As a part of their transformation to lean production companies are increasing the proportion of standardised work. Standardisation is commonly put forward as a means to reduce variability and improve quality. It is also expected to embody a base-line for improvement. A great deal of the criticism of lean production is associated with standardisation. Main concerns are that it could lead to monotony and intensification of work. The main question of the paper is: how does an increasing level of standardisation affect the psychosocial work environment of production workers? A total of 48 production workers from three Swedish production companies participated in a survey. In a questionnaire the respondents rated their perception of a number of aspects related to how standardised work is implemented in their company. In addition they rated their psychosocial work environment. The main conclusion of the study is that it is not possible to declare that increasing the level of standardisation is plain good or bad from a work environment point of view. The results show that good quality standards are important from an efficiency point of view as well as a stress perspective

The key Concepts of Lean Production and Anticipated Effects on Worker Well-being of Lean Production Concepts

The Lean Production concept was first heard of in 1988. The concept builds upon the notion of the Toyota Production System (TPS). Despite the vast amount of material that has been published on the topic there still seems to be no common definition of the term. Such a definition is not provided in this paper but four key concepts of Lean Production and a proposition of a conceptual model are presented. In addition an account of the expected main effects in respect to worker well-being of a number of concepts associated with Lean Production is given

Data from: High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer

Background: The progression of colorectal cancer (CRC) involves recurrent amplifications/mutations in the epidermal growth factor receptor (EGFR) and downstream signal transducers of the Ras pathway, KRAS and BRAF. Whether genetic events predicted to result in increased and constitutive signaling indeed lead to enhanced biological activity is often unclear and, due to technical challenges, unexplored. Here, we investigated proliferative signaling in CRC using a highly sensitive method for protein detection. The aim of the study was to determine whether multiple changes in proliferative signaling in CRC could be combined and exploited as a “complex biomarker” for diagnostic purposes. Methods: We used robotized capillary isoelectric focusing as well as conventional immunoblotting for the comprehensive analysis of epidermal growth factor receptor signaling pathways converging on extracellular regulated kinase 1/2 (ERK1/2), AKT, phospholipase Cγ1 (PLCγ1) and c-SRC in normal mucosa compared with CRC stage II and IV. Computational analyses were used to test different activity patterns for the analyzed signal transducers. Results: Signaling pathways implicated in cell proliferation were differently dysregulated in CRC and, unexpectedly, several were downregulated in disease. Thus, levels of activated ERK1 (pERK1), but not pERK2, decreased in stage II and IV while total ERK1/2 expression remained unaffected. In addition, c-SRC expression was lower in CRC compared with normal tissues and phosphorylation on the activating residue Y418 was not detected. In contrast, PLCγ1 and AKT expression levels were elevated in disease. Immunoblotting of the different signal transducers, run in parallel to capillary isoelectric focusing, showed higher variability and lower sensitivity and resolution. Computational analyses showed that, while individual signaling changes lacked predictive power, using the combination of changes in three signaling components to create a “complex biomarker” allowed with very high accuracy, the correct diagnosis of tissues as either normal or cancerous. Conclusions: We present techniques that allow rapid and sensitive determination of cancer signaling that can be used to differentiate colorectal cancer from normal tissue

Padhan BMC Cancer 2016 Supplemental Results

File contains the test statistic (T) for each possible combination of 1-3 features, including the constructed features (column 5 and 6). One combination is shown per row with the name of the feature combination in the first column and the header explaining the value in each column in the first row

Raw Protein RPAs

The relative peak area (RPA), i.e. peak area value of the 23 different activity levels of the 7 signal transducers after normalization to the HSP70 level analyzed in parallel in each sample (columns 2-24). This file contains one sample per row and one protein per column with the sample name in the first column and the protein name in the first row. Columns 25-27 contain the result of the mutation analysis of KRAS and BRAF. One in column 25 (MutationKRAS) indicate that KRAS is mutated in the sample, one in column 26 (MutationBRAF) indicates that BRAF is mutated, while one in column 27 (Wildtype) indicate that neither KRAS nor BRAF is mutated. A one in the binary variables in column 28-31 indicate the classification of each sample as normal mucosa, colorectal cancer (CRC) stage II, CRC stage IV, or metastasis. NaN is used to indicate that no measurement was done

Raw Protein RPAs Constructed features replicate corrected

File shows the relative peak area (RPA), i.e. peak area value of the measured protein after normalization to the HSP70 level analyzed in parallel in each sample (columns 2-24, 28-42). This file contains one sample per row and one protein per column with the sample name in the first column and the protein name in the first row. Column 25-27 contain the result of the mutation analysis of KRAS and BRAF. One in column 25 (MutationKRAS) indicate that KRAS is mutated in the sample, one in column 26 (MutationBRAF) indicates that BRAF is mutated, while one in column 27 (Wildtype) indicate that neither KRAS nor BRAF is mutated. Columns 28 to 42 contain the RPA values of the constructed features, i.e. features that are calculated based on the 23 different activity levels of the 7 signal transducers in column 2-24. The four replicates of each constructed feature contains the minimum, maximum, mean, and median value based on all possible ways to combine the replicates of the proteins used to construct the feature. A one in the binary variables in column 43-46 indicate the classification of each sample as normal mucosa, colorectal cancer (CRC) stage II, CRC stage IV, or metastasis. In the last column the classification is 1 = normal mucosa, 2 = colorectal cancer (CRC) stage II, 3 = CRC stage IV, or 5 = metastasis. NaN is used to indicate that no measurement was done

Additional file 1: of High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer

Figure S1. Validation of antibodies used in the study by conventional immunoblotting. All antibodies showed immunoreactivity with the expected molecular species, in conventional immunoblotting on endothelial lysates. Figure S2. Detection of MEK1/2 protein by isoelectric focusing. There was no significant difference in MEK protein expression between normal, CRCII and CRCIV tissues. Detailed description of computational analyses; “Characterization of the data set and errors”. Figure S3. Distribution function for data subsets by Monte Carlo simulation. (DOCX 1215 kb