A sketch of Theodore R. Sarbin's life

The article of record as published may be found at http://dx.doi.org/10.1075/ni.25.2.10schTed Sarbin was born on May 8, 1911 in Cleveland, Ohio. He died on August 31, 2005, in Carmel, California. He was born into a poor Jewish family from eastern Europe, and died at his home — beloved by his friends and family, and acclaimed by his professional colleagues as a psychologist of distinction. This article traces the course of his life — with special attention to the formative influences in his education as a psychologist. As a psychologist, he became a significant critical voice — arguing for a psychology that would embrace narrative as a principle of understanding human life, and contextualism, as opposed to mechanism, as a world view

Insights into the value of the market for cocaine, heroin and methamphetamine in South Africa

The illicit drug trade generates billions of dollars and sustains transnational criminal organisations. Drug markets can destabilise governance and undermine development. Data indicate increasing drug use in South Africa. However, information on the size and value of the drug market is limited. This is the first study to estimate the market value of cocaine, heroin and methamphetamine in South Africa. People who use drugs were meaningfully involved in all aspects of implementation. We used focus group discussions, ethnographic mapping, brief interviews, and the Delphi method to estimate the number of users, volumes consumed, and price for each drug in South Africa in 2020. Nationally, we estimated there to be: 400,000 people who use heroin (probability range (PR) 215,000–425,000) consuming 146.00 tonnes (PR 78.48–155.13) with a value of US$1,898.00 million (PR US$1,020.18–US$2,016.63); 350,000 people who use cocaine (PR 250,000–475,000) consuming 18.77 tonnes (PR 13.41–25.47) with a market value of US$1,219.86 million (PR 871.33–1,655.52) and 290,000 people who use methamphetamine (PR 225,000–365,000) consuming 60.19 tonnes (PR 6.58–10.68) and a market value of US$782.51 million (PR 607.12–984.88). The combined value was calculated at US$3.5 billion. Findings can be used to stimulate engagement to reform drug policy and approaches to mitigate the impact of the illicit drug trade. Additional studies that include people who use drugs in research design and implementation are needed to improve our understanding of drug markets

A SWOT analysis of health service access by men who have sex with men in South Africa: lessons for higher education institutions

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Selection of dominant multi-exciton transitions in disordered linear J-aggregates

We show that the third-order optical response of disordered linear J-aggregates can be calculated by considering only a limited number of transitions between (multi-) exciton states. We calculate the pump-probe absorption spectrum resulting from the truncated set of transitions and show that, apart from the blue wing of the induced absorption peak, it agrees well with the exact spectrum.Comment: 8 pages, 2 figures, accepted to Journal of Luminescenc

Accumulation and nuclear import of HIF1 alpha during high and low oxygen concentration in skeletal muscle cells in primary culture

AbstractThe hypoxia-inducible-factor-1 (HIF1) mediates the transcriptional upregulation of several target genes during hypoxia. HIF1 itself is known to be regulated essentially by ubiquitinylation and proteolytic degradation of the subunit HIF1α of the dimeric transcription factor HIF1. In contrast to other tissues, skeletal muscle expresses high amounts of HIF1α in normoxia as well as in hypoxia. In view of this, we aimed to investigate HIF1α accumulation and subcellular localization as well as the transcriptional activity of the HIF1α-regulated gene of glyceraldehyde dehydrogenase (GAPDH) in skeletal muscle cells exposed to low oxygen concentration (3% O2), normoxia (20% O2) or high oxygen concentration (42% O2). Immunofluorescence analysis reveals that under normoxic and high oxygen conditions, significant amounts of HIF1α can be found exclusively in the cytoplasm of the myotubes. Muscle cells treated with CoCl2, a known inhibitor of HIF1α degradation, show even higher levels of HIF1α, again exclusively in the cytoplasm. Under conditions of low oxygen, HIF1α in controls as well as in CoCl2-treated cells is found in the nuclei. CdCl2 inhibits nuclear import of HIF1α at low oxygen concentration and leads to a transcriptional downregulation of the marker enzyme of anaerobic glycolysis GAPDH. Immunoprecipitation with anti-HIF1α antibody co-precipitates HSP90 in an oxygen-dependent manner, more at high pO2 than at low pO2. Cadmium-treated samples also show high amounts of co-immunoprecipitated HSP90, independent of oxygen concentration. We conclude that in skeletal muscle cells, HIF1α, in contrast to other tissues, may, in addition to its regulation by degradation, also be regulated by binding to HSP90 and subsequent inhibition of its import into the nuclei

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn