Deindustrialization in cities of the global south

Recent research by economists has shown that deindustrialization is more severe in Sub-Saharan Africa and Latin America than it ever was in the Organisation for Economic Co-operation and Development (OECD). Nevertheless, most research on deindustrialization is focused on the former centres of Fordist manufacturing in the industrial heartlands of the North Atlantic. In short, there is a mismatch between where deindustrialization is researched and where it is occurring, and the objective of this paper is to shift the geographical focus of research on deindustrialization to the Global South. Case studies from Argentina, India, Tanzania and Turkey demonstrate the variegated nature of deindustrialization beyond the North Atlantic. In the process, it is demonstrated that cities in the Global South can inform wider theoretical discussions on the impacts of deindustrialization at the urban scale

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. Results: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). Conclusions: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm

Understanding Managerial Work Values in Turkey

values, managerial values, organization, Turkey, cultural values, proper values, improper values,

The WHO global alliance against chronic respiratory diseases in Turkey (GARD Turkey)

In order to prevent and control non-communicable diseases (NCDs), the 61st World Health Assembly has endorsed an NCD action plan (WHA resolution 61.14). A package for essential NCDs including chronic respiratory diseases (CRDs) has also been developed. The Global Alliance against Chronic Respiratory Diseases (GARD) is a new but rapidly developing voluntary alliance that is assisting World Health Organization (WHO) in the task of addressing NCDs at country level. The GARD approach was initiated in 2006. GARD Turkey is the first comprehensive programme developed by a government with all stakeholders of the country. This paper provides a summary of indicators of the prevalence and severity of chronic respiratory diseases in Turkey and the formation of GARD Turkey