Supply chain vulnerability assessment: A network based visualization and clustering analysis approach

Supply chains are large, complex, and often unpredictable. Purchasing and supply managers and supply chain risk managers need methods and tools to enable them to quickly understand how unexpected disruptions in the supply chain start and grow and to what extent will they negatively impact the flow of goods and services. This paper introduces a methodological approach that can be used by both researchers and managers to quickly visualize a supply chain, map out the propagation path of disruptive events from the supply side to the end customer and understand potential weaknesses in the supply chain design; taking into account the structure, connectivity, and dependence within the supply chain. The approach incorporates a Petri net and Triangularization Clustering Algorithm to offer insights into a supply chain network\u27s vulnerabilities and can be used to efficiently assess supply chain disruption mitigation strategies, especially in complex and difficulty to analyze supply chain systems

Recently fixed carbon fuels microbial activity several meters below the soil surface

This data file (Scheibe_2022.xlsx) contains radiocarbon data of bulk soil carbon and CO2 respired in incubations from soil profiles in three climate zones (arid, mediterranean, and humid) of the Costal Cordillera of Chile down to a depth of six meters. Variable descriptions are provided in Template Info File. The data are part of a study, which investigates how soil microbial carbon cycling affects soil formation especially in the critical zone by understanding the carbon source of microbial activity in deep soil. The study was conducted within the framework of the Deep EarthShape priority program funded by the German Science Foundation (DFG-SPP 1803)

Quantitative mass spectrometry and PAR-CLIP to identify RNA-protein interactions

Systematic analysis of the RNA-protein interactome requires robust and scalable methods. We here show the combination of two completely orthogonal, generic techniques to identify RNA-protein interactions: PAR-CLIP reveals a collection of RNAs bound to a protein whereas SILAC-based RNA pull-downs identify a group of proteins bound to an RNA. We investigated binding sites for five different proteins (IGF2BP1-3, QKI and PUM2) exhibiting different binding patterns. We report near perfect agreement between the two approaches. Nevertheless, they are non-redundant, and ideally complement each other to map the RNA-protein interaction network

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

Training, Self-Efficacy, and Performance; a Replication Study

A conceptual replication of multiple prior IS studies was conducted with the aim of providing stronger empirical support for those results. Conducting six separate longitudinal studies, the effect of professional training on improving one’s application-specific computer self-efficacy (AS-CSE) was shown. Also in line with some prior IS studies it was shown that an application-specific measure of self-efficacy is better able to predict one’s performance in accomplishing tasks in the corresponding domain than a general computer self-efficacy (GCSE) measure. Moreover, it is shown that, regardless of the type and characteristics of the training method, individuals’ perceptions of quality of training significantly affects their AS-CSE after the training course

Factors predicting 6-month retention among people with opioid use disorders accessing outpatient methadone maintenance therapy in Tshwane, South Africa

Background. In South Africa (SA), increasing illicit opioid use and associated health challenges can be managed with opioid substitution therapy (OST), such as methadone, if the recommended dose and duration of use are prescribed. The Community Oriented Substance Use Programme (COSUP) provides free methadone to patients with opioid use disorder in Tshwane, SA, on need-based criteria.Objectives. To determine selected sociodemographic and substance use treatment factors associated with retention for at least 6 months among participants receiving methadone as part of OST in COSUP.Methods. This was a retrospective cohort study using secondary data of patients treated with methadone at 8 COSUP sites. The factors associated with at least 6 months’ retention for 575 participants from December 2016 to September 2018 were analysed.Results. There were 91.3% males, 86.4% South Africans and 85.9% black Africans, with a median age of 30 years. At baseline, the majority were injecting heroin (55.5%) and were provided with free methadone (59.3%). The median dose of methadone at 6 months or on leaving the programme was 20 mg; 38.4% of participants were retained for at least 6 months. Of those not retained, the median duration on methadone was 56 days, whereas for those retained for at least 6 months, the median number of days on methadone were 254. After adjusting for sex and age, participants receiving methadone doses <50 mg had lower odds of being retained (0 - 20 mg: adjusted odds ratio (aOR) 0.25; p=0.002; 95% confidence interval (CI) 0.10 - 0.61; >20 - 40 mg: aOR 0.20; p<0.001; 95% CI 0.08 - 0.49) than those administered ≥50 mg. Participants who received free methadone had 3.75 the odds of being retained than those buying it themselves (p<0.001; 95% CI 2.47 - 5.70). Participants treated in the inner city had 5.19 the odds of being retained than those in a suburban setting (p<0.001; 95% CI 2.99 - 9.03). Compared with black African participants, white participants had 3.39 the odds of being retained (p=0.001; 95% CI 1.64 - 7.00). Injecting heroin users had 0.63 the odds of being retained (p=0.032; 95% CI 0.41 - 0.96).Conclusions. To maximise retention on OST, methadone should be free, with maintenance doses >50 mg. Reasons for lower retention among participants from periurban settings, those who inject and those from previously disadvantaged racial groups need to be explored, and findings used to inform programming

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community’s traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators