It Worked There, So It Should Work Here: Sustaining Change while Improving Product Development Processes

Organizations operate under ongoing pressure to conduct product development (PD) in ways that reduce errors, improve product designs, and increase speed and efficiency. Often, managers are expected to respond to this pressure by implementing process improvement programs (PIPs) based on best practices elsewhere (e.g., in another part of their organization or in another industrial context). Successful PIP implementation depends on two criteria: (a) demonstrating (symbolic) success by meeting externally imposed deadlines and producing mandated artifacts and (b) sustaining the expected (substantive) changes in their employees' underlying beliefs and practices. Given the mixed success of PIPs in nonmanufacturing contexts, identifying factors that contribute to both symbolic and substantive implementation is important to both researchers and practitioners. We explore this challenge through an in‐depth field study at a PD company (DevCo) that implemented a PIP across its 11 PD projects. We examine DevCo's change message to implement the PIP, how DevCo's engineers experienced it, factors that impeded implementation, and factors that could improve substantive success. Along with this empirical evidence, we leverage organizational change concepts to facilitate effective PIP implementation in new contexts such as PD. We distill our findings into eight propositions that expand theory about effectively transferring PIPs across contexts

Elevated TCA cycle function in the pathology of diet-induced hepatic insulin resistance and fatty liver

The manner in which insulin resistance impinges on hepatic mitochondrial function is complex. Although liver insulin resistance is associated with respiratory dysfunction, the effect on fat oxidation remains controversial, and biosynthetic pathways that traverse mitochondria are actually increased. The tricarboxylic acid (TCA) cycle is the site of terminal fat oxidation, chief source of electrons for respiration, and a metabolic progenitor of gluconeogenesis. Therefore, we tested whether insulin resistance promotes hepatic TCA cycle flux in mice progressing to insulin resistance and fatty liver on a high-fat diet (HFD) for 32 weeks using standard biomolecular and in vivo (2)H/(13)C tracer methods. Relative mitochondrial content increased, but respiratory efficiency declined by 32 weeks of HFD. Fasting ketogenesis became unresponsive to feeding or insulin clamp, indicating blunted but constitutively active mitochondrial β-oxidation. Impaired insulin signaling was marked by elevated in vivo gluconeogenesis and anaplerotic and oxidative TCA cycle flux. The induction of TCA cycle function corresponded to the development of mitochondrial respiratory dysfunction, hepatic oxidative stress, and inflammation. Thus, the hepatic TCA cycle appears to enable mitochondrial dysfunction during insulin resistance by increasing electron deposition into an inefficient respiratory chain prone to reactive oxygen species production and by providing mitochondria-derived substrate for elevated gluconeogenesis

TL1A–DR3 interaction regulates Th17 cell function and Th17-mediated autoimmune disease

T helper type 17 (Th17) cells play an important pathogenic function in autoimmune diseases; their regulation, however, is not well understood. We show that the expression of a tumor necrosis factor receptor family member, death receptor 3 (DR3; also known as TNFRSF25), is selectively elevated in Th17 cells, and that TL1A, its cognate ligand, can promote the proliferation of effector Th17 cells. To further investigate the role of the TL1A–DR3 pathway in Th17 regulation, we generated a TL1A-deficient mouse and found that TL1A−/− dendritic cells exhibited a reduced capacity in supporting Th17 differentiation and proliferation. Consistent with these data, TL1A−/− animals displayed decreased clinical severity in experimental autoimmune encephalomyelitis (EAE). Finally, we demonstrated that during EAE disease progression, TL1A was required for the optimal differentiation as well as effector function of Th17 cells. These observations thus establish an important role of the TL1A–DR3 pathway in promoting Th17 cell function and Th17-mediated autoimmune disease

Epigenetic dominance of prion conformers

Although they share certain biological properties with nucleic acid based infectious agents, prions, the causative agents of invariably fatal, transmissible neurodegenerative disorders such as bovine spongiform encephalopathy, sheep scrapie, and human Creutzfeldt Jakob disease, propagate by conformational templating of host encoded proteins. Once thought to be unique to these diseases, this mechanism is now recognized as a ubiquitous means of information transfer in biological systems, including other protein misfolding disorders such as those causing Alzheimer's and Parkinson's diseases. To address the poorly understood mechanism by which host prion protein (PrP) primary structures interact with distinct prion conformations to influence pathogenesis, we produced transgenic (Tg) mice expressing different sheep scrapie susceptibility alleles, varying only at a single amino acid at PrP residue 136. Tg mice expressing ovine PrP with alanine (A) at (OvPrP-A136) infected with SSBP/1 scrapie prions propagated a relatively stable (S) prion conformation, which accumulated as punctate aggregates in the brain, and produced prolonged incubation times. In contrast, Tg mice expressing OvPrP with valine (V) at 136 (OvPrP-V136) infected with the same prions developed disease rapidly, and the converted prion was comprised of an unstable (U), diffusely distributed conformer. Infected Tg mice co-expressing both alleles manifested properties consistent with the U conformer, suggesting a dominant effect resulting from exclusive conversion of OvPrP-V136 but not OvPrP-A136. Surprisingly, however, studies with monoclonal antibody (mAb) PRC5, which discriminates OvPrP-A136 from OvPrP-V136, revealed substantial conversion of OvPrP-A136. Moreover, the resulting OvPrP-A136 prion acquired the characteristics of the U conformer. These results, substantiated by in vitro analyses, indicated that co-expression of OvPrP-V136 altered the conversion potential of OvPrP-A136 from the S to the otherwise unfavorable U conformer. This epigenetic mechanism thus expands the range of selectable conformations that can be adopted by PrP, and therefore the variety of options for strain propagation

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models

Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research

Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models

Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research