Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining : Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings.publishedVersionPeer reviewe

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings

Excessive Unbalanced Meat Consumption in the First Year of Life Increases Asthma Risk in the PASTURE and LUKAS2 Birth Cohorts

A higher diversity of food items introduced in the first year of life has been inversely related to subsequent development of asthma. In the current analysis, we applied latent class analysis (LCA) to systematically assess feeding patterns and to relate them to asthma risk at school age. PASTURE (N=1133) and LUKAS2 (N=228) are prospective birth cohort studies designed to evaluate protective and risk factors for atopic diseases, including dietary patterns. Feeding practices were reported by parents in monthly diaries between the 4(th) and 12(th) month of life. For 17 common food items parents indicated frequency of feeding during the last 4 weeks in 4 categories. The resulting 153 ordinal variables were entered in a LCA. The intestinal microbiome was assessed at the age of 12 months by 16S rRNA sequencing. Data on feeding practice with at least one reported time point was available in 1042 of the 1133 recruited children. Best LCA model fit was achieved by the 4-class solution. One class showed an elevated risk of asthma at age 6 as compared to the other classes (adjusted odds ratio (aOR): 8.47, 95% CI 2.52-28.56, p = 0.001) and was characterized by daily meat consumption and rare consumption of milk and yoghurt. A refined LCA restricted to meat, milk, and yoghurt confirmed the asthma risk effect of a particular class in PASTURE and independently in LUKAS2, which we thus termed unbalanced meat consumption (UMC). The effect of UMC was particularly strong for non-atopic asthma and asthma irrespectively of early bronchitis (aOR: 17.0, 95% CI 5.2-56.1, p < 0.001). UMC fostered growth of iron scavenging bacteria such as Acinetobacter (aOR: 1.28, 95% CI 1.00-1.63, p = 0.048), which was also related to asthma (aOR: 1.55, 95% CI 1.18-2.03, p = 0.001). When reconstructing bacterial metabolic pathways from 16S rRNA sequencing data, biosynthesis of siderophore group nonribosomal peptides emerged as top hit (aOR: 1.58, 95% CI 1.13-2.19, p = 0.007). By a data-driven approach we found a pattern of overly meat consumption at the expense of other protein sources to confer risk of asthma. Microbiome analysis of fecal samples pointed towards overgrowth of iron-dependent bacteria and bacterial iron metabolism as a potential explanation.Peer reviewe

Using mergers and acquisitions to prepare for disruption

Industry incumbents often encounter significant troubles in the face of disruptive innovations. These types of innovations erode the existing capabilities and resources of the firm, forcing them to seek out new capabilities outside their own organization in order to remain competitive and survive. Exploitation and exploration, organizational learning strategies utilized to develop incremental and radical innovations, respectively, are considered common drivers for mergers and acquisitions (MA) among firms. MA's enable the firm to obtain new capabilities and competencies in order to respond to the threat of substitution of their current ones by disruptive innovations and new entrants employing them. According to the research, firms' operative actions are more strongly linked to preparing for disruption than strategic ones in the motives for acquisitions.acceptedVersionPeer reviewe

Early age exposure to moisture damage and systemic inflammation at the age of 6 years

Cross-sectional studies have shown that exposure to indoor moisture damage and mold may be associated with subclinical inflammation. Our aim was to determine whether early age exposure to moisture damage or mold is prospectively associated with subclinical systemic inflammation or with immune responsiveness in later childhood. Home inspections were performed in children's homes in the first year of life. At age 6 years, subclinical systemic inflammation was measured by serum C-reactive protein(CRP) and blood leucocytes and immune responsiveness by ex vivo production of interleukin 1-beta(IL-1beta), IL-6 and tumor necrosis factor-alpha(TNF-alpha) in whole blood cultures without stimulation or after 24h stimulation with phorbol 12-myristate 13-acetate and ionomycin(PI), lipopolysaccharide(LPS) or peptidoglycan(PPG) in 251 to 270 children. Moisture damage in child's main living areas in infancy was not significantly associated with elevated levels of CRP or leucocytes at 6 years. In contrast, there was some suggestion for an effect on immune responsiveness, as moisture damage with visible mold was positively associated with LPS-stimulated production of TNF-alpha and minor moisture damage was inversely associated with PI-stimulated IL-1beta. While early life exposure to mold damage may have some influence on later immune responsiveness, it does not seem to increase subclinical systemic inflammation in later life. This article is protected by copyright. All rights reserved

The role of Probiotics in allergic diseases

Allergic disorders are very common in the pediatric age group. While the exact etiology is unclear, evidence is mounting to incriminate environmental factors and an aberrant gut microbiota with a shift of the Th1/Th2 balance towards a Th2 response. Probiotics have been shown to modulate the immune system back to a Th1 response. Several in vitro studies suggest a role for probiotics in treating allergic disorders. Human trials demonstrate a limited benefit for the use of probiotics in atopic dermatitis in a preventive as well as a therapeutic capacity. Data supporting their use in allergic rhinitis are less robust. Currently, there is no role for probiotic therapy in the treatment of bronchial asthma. Future studies will be critical in determining the exact role of probiotics in allergic disorders

Towards practical typology of energy communities : main differentiating elements and examples of promising implementations

publishedVersionPeer reviewe

Excessive Unbalanced Meat Consumption in the First Year of Life Increases Asthma Risk in the PASTURE and LUKAS2 Birth Cohorts.

A higher diversity of food items introduced in the first year of life has been inversely related to subsequent development of asthma. In the current analysis, we applied latent class analysis (LCA) to systematically assess feeding patterns and to relate them to asthma risk at school age. PASTURE (N=1133) and LUKAS2 (N=228) are prospective birth cohort studies designed to evaluate protective and risk factors for atopic diseases, including dietary patterns. Feeding practices were reported by parents in monthly diaries between the 4th and 12th month of life. For 17 common food items parents indicated frequency of feeding during the last 4 weeks in 4 categories. The resulting 153 ordinal variables were entered in a LCA. The intestinal microbiome was assessed at the age of 12 months by 16S rRNA sequencing. Data on feeding practice with at least one reported time point was available in 1042 of the 1133 recruited children. Best LCA model fit was achieved by the 4-class solution. One class showed an elevated risk of asthma at age 6 as compared to the other classes (adjusted odds ratio (aOR): 8.47, 95% CI 2.52-28.56, p = 0.001) and was characterized by daily meat consumption and rare consumption of milk and yoghurt. A refined LCA restricted to meat, milk, and yoghurt confirmed the asthma risk effect of a particular class in PASTURE and independently in LUKAS2, which we thus termed unbalanced meat consumption (UMC). The effect of UMC was particularly strong for non-atopic asthma and asthma irrespectively of early bronchitis (aOR: 17.0, 95% CI 5.2-56.1, p < 0.001). UMC fostered growth of iron scavenging bacteria such as Acinetobacter (aOR: 1.28, 95% CI 1.00-1.63, p = 0.048), which was also related to asthma (aOR: 1.55, 95% CI 1.18-2.03, p = 0.001). When reconstructing bacterial metabolic pathways from 16S rRNA sequencing data, biosynthesis of siderophore group nonribosomal peptides emerged as top hit (aOR: 1.58, 95% CI 1.13-2.19, p = 0.007). By a data-driven approach we found a pattern of overly meat consumption at the expense of other protein sources to confer risk of asthma. Microbiome analysis of fecal samples pointed towards overgrowth of iron-dependent bacteria and bacterial iron metabolism as a potential explanation

Microbial secondary metabolites in homes in association with moisture damage and asthma

We aimed to characterize the presence of microbial secondary metabolites in homes and their association with moisture damage, mold, and asthma development. Living room floor dust was analyzed by LC-MS/MS for 333 secondary metabolites from 93 homes of 1-year-old children. Moisture damage was present in 15 living rooms. At 6 years, 8 children had active and 15 lifetime doctor-diagnosed asthma. The median number of different metabolites per house was 17 ( range 8-29) and median sum load 65 ( 4-865) ng/m(2). Overall 42 different metabolites were detected. The number of metabolites present tended to be higher in homes with mold odor or moisture damage. The higher sum loads and number of metabolites with loads over 10 ng/m(2) were associated with lower prevalence of active asthma at 6 years ( aOR 0.06 ( 95% CI <0.001-0.96) and 0.05 (<0.001-0.56), respectively). None of the individual metabolites, which presence tended ( P <0.2) to be increased by moisture damage or mold, were associated with increased risk of asthma. Microbial secondary metabolites are ubiquitously present in home floor dust. Moisture damage and mold tend to increase their numbers and amount. There was no evidence indicating that the secondary metabolites determined would explain the association between moisture damage, mold, and the development of asthma.Peer reviewe