Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approach. Our code and pre-trained models are made available at https://github.com/tdchaitanya/MMTODComment: Accepted at Perception Beyond Visible Spectrum Workshop, CVPR 201

Initiation of antidepressant medication in people with type 2 diabetes living in the UK – a retrospective cohort study

INTRODUCTION: Depression is a common comorbidity in people with type 2 diabetes and it is associated with poorer outcomes. There is limited data on the treatments used for depression in this population. The aim of this study was to explore the rates of initiation of antidepressant prescriptions in people with type 2 diabetes in the UK and identify those most at risk of needing such treatment. RESEARCH DESIGN AND METHODS: This was a retrospective cohort study using data from IQVIA Medical Research Data (IMRD)-UK data. Data from general practices in IMRD-UK between January 2008 and December 2017 were used for this study. RESULTS: The overall rates of antidepressant prescribing were stable over the study period. The rate of initiation of antidepressant medication in people with type 2 diabetes was 22.93 per 1000 person years at risk (PYAR) with a 95%CI 22.48 to 23.39 compared to 16.89 per 1000 PYAR (95%CI 16.77 to 17.01) in an age and gender matched cohort. The risk of being prescribed anti-depressant medication with age had a U-shaped distribution with the lowest risk in the 65-69 age group. The peak age for antidepressant initiation in men and women was 40-44, with a rate in men of 32.78 per 1000 PYAR (95% CI 29.57 to 36.34) and a rate in women of 46.80 per 1000 PYAR (95% CI 41.90 to 52.26). People with type 2 diabetes with in the least deprived quintile had an initiation rate of 19.66 per 1000 PYAR (95%CI 18.67 to 20.70) compared to 27.19 per 1000 PYAR (95%CI 25.50 to 28.93) in the most deprived quintile, with a 32% increase in the risk of starting antidepressant medication (95%CI 1.22 to 1.43). CONCLUSIONS: People with type 2 diabetes were 30% more likely to be started on antidepressant medication than people without type 2 diabetes. Women with type 2 diabetes were 35% more likely than men to be prescribed antidepressants and the risks increased with deprivation and in younger or older adults, with the lowest rates in the 65-69 year age band. The rates of antidepressant prescribing were broadly stable over the 10-year period in this study. The anti-depressant medications prescribed changed slightly over time with sertraline becoming more widely used and fewer prescriptions of citalopram

Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a ‘pseudo-multimodal’ object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approac

Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis

The purpose of this study is twofold: first, to establish the current themes on the topic of manufacturing and supply chain flexibility (MSCF), assess their level of maturity in relation to each other, identify the emerging ones and reflect on how they can inform each other, and second, to develop a conceptual model of MSCF that links different themes connect and highlight future research opportunities. The study builds on a sample of 222 articles published from 1996 to 2018 in international, peer-reviewed journals. The analysis of the sample involves two complementary approaches: the co-word technique to identify the thematic clusters as well as their relative standing and a critical reflection on the papers to explain the intellectual content of these thematic clusters. The results of the co-word analysis show that MSCF is a dynamic topic with a rich and complex structure that comprises five thematic clusters. The value chain, capability and volatility clusters showed research topics that were taking a central role in the discussion on MSCF but were not mature yet. The SC purchasing practices and SC planning clusters involved work that was more focused and could be considered more mature. These clusters were then integrated in a framework that built on the competence–capability perspective and identified the major structural and infrastructural elements of MSCF as well as its antecedents and consequences. This paper proposes an integrative framework helping managers keep track the various decisions they need to make to increase flexibility from the viewpoint of the entire value chain

Harnessing the Therapeutic Potential of the Nrf2/Bach1 Signaling Pathway in Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative movement disorder characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Although a complex interplay of multiple environmental and genetic factors has been implicated, the etiology of neuronal death in PD remains unresolved. Various mechanisms of neuronal degeneration in PD have been proposed, including oxidative stress, mitochondrial dysfunction, neuroinflammation, α-synuclein proteostasis, disruption of calcium homeostasis, and other cell death pathways. While many drugs individually targeting these pathways have shown promise in preclinical PD models, this promise has not yet translated into neuroprotective therapies in human PD. This has consequently spurred efforts to identify alternative targets with multipronged therapeutic approaches. A promising therapeutic target that could modulate multiple etiological pathways involves drug-induced activation of a coordinated genetic program regulated by the transcription factor, nuclear factor E2-related factor 2 (Nrf2). Nrf2 regulates the transcription of over 250 genes, creating a multifaceted network that integrates cellular activities by expressing cytoprotective genes, promoting the resolution of inflammation, restoring redox and protein homeostasis, stimulating energy metabolism, and facilitating repair. However, FDA-approved electrophilic Nrf2 activators cause irreversible alkylation of cysteine residues in various cellular proteins resulting in side effects. We propose that the transcriptional repressor of BTB and CNC homology 1 (Bach1), which antagonizes Nrf2, could serve as a promising complementary target for the activation of both Nrf2-dependent and Nrf2-independent neuroprotective pathways. This review presents the current knowledge on the Nrf2/Bach1 signaling pathway, its role in various cellular processes, and the benefits of simultaneously inhibiting Bach1 and stabilizing Nrf2 using non-electrophilic small molecules as a novel therapeutic approach for PD

Logistics Dynamics and Demographic Change

Change and dynamics in logistics are interestingly driven at the same time by external as well as internal forces. This contribution outlines a big data literature review methodology to overview recognizable external changes and analyzes the interaction of one major trend—demographic change—further in order to allow for change management and adaption concepts for successful logistics. Therefore, this may be a first blueprint of how to analyze and react to specific trends in a holistic manner embedded into a context and environment of trends and changes. This may allow logistics dynamics concepts also to be possibly more sustainable in terms of applicable for a longer period of time—and not to be overcome by other trends