TMEM45A Is Dispensable for Epidermal Morphogenesis,Keratinization and Barrier Formation

TMEM45A gene encodes an initially uncharacterized predicted transmembrane protein. We previously showed that this gene is highly expressed in keratinocytes where its expression correlates with keratinization, suggesting a role in normal epidermal physiology. To test this hypothesis, we generated TMEM45A knockout mice and found that these mice develop without any evident phenotype. The morphology of the epidermis assessed by histology and by labelling differentiation markers in immunofluorescence was not altered. Toluidine blue permeability assay showed that the epidermal barrier develops normally during embryonic development. We also showed that depletion of TMEM45A in human keratinocytes does not alter their potential to form in vitro 3D-reconstructed epidermis. Indeed, epidermis with normal morphogenesis were generated from TMEM45A-silenced keratinocytes. Their expression of differentiation markers quantified by RT-qPCR and evidenced by immunofluorescence labelling as well as their barrier function estimated by Lucifer yellow permeability were similar to the control epidermis. In summary, TMEM45A gene expression is dispensable for epidermal morphogenesis, keratinization and barrier formation. If this protein plays a role in the epidermis, its experimental depletion can possibly be compensated by other proteins in the two experimental models analyzed in this study.     

Reasons for Missing Antiretroviral Therapy: Results from a Multi-Country Study in Tanzania,Uganda, and Zambia

Objectives

To identify the reasons patients miss taking their antiretroviral therapy (ART) and the proportion who miss their ART because of symptoms; and to explore the association between symptoms and incomplete adherence.

Methods

Secondary analysis of data collected during a cross-sectional study that examined ART adherence among adults from 18 purposefully selected sites in Tanzania, Uganda, and Zambia. We interviewed 250 systematically selected patients per facility (≥18 years) on reasons for missing ART and symptoms they had experienced (using the HIV Symptom Index). We abstracted clinical data from the patients’ medical, pharmacy, and laboratory records. Incomplete adherence was defined as having missed ART for at least 48 consecutive hours during the past 3 months.

Results

Twenty-nine percent of participants reported at least one reason for having ever missed ART (1278/4425). The most frequent reason was simply forgetting (681/1278 or 53%), followed by ART-related hunger or not having enough food (30%), and symptoms (12%). The median number of symptoms reported by participants was 4 (IQR: 2–7). Every additional symptom increased the odds of incomplete adherence by 12% (OR: 1.1, 95% CI: 1.1–1.2). Female participants and participants initiated on a regimen containing stavudine were more likely to report greater numbers of symptoms.

Conclusions

Symptoms were a common reason for missing ART, together with simply forgetting and food insecurity. A combination of ART regimens with fewer side effects, use of mobile phone text message reminders, and integration of food supplementation and livelihood programmes into HIV programmes, have the potential to decrease missed ART and hence to improve adherence and the outcomes of ART programmes.     

Decreased Bone Formation Explains Osteoporosis in a Genetic Mouse Model of Hemochromatosiss

Osteoporosis may complicate iron overload diseases such as genetic hemochromatosis. However, molecular mechanisms involved in the iron-related osteoporosis remains poorly understood. Recent in vitro studies support a role of osteoblast impairment in iron-related osteoporosis. Our aim was to analyse the impact of excess iron in Hfe^-/- mice on osteoblast activity and on bone microarchitecture. We studied the bone formation rate, a dynamic parameter reflecting osteoblast activity, and the bone phenotype of Hfe^−/− male mice, a mouse model of human hemochromatosis, by using histomorphometry. Hfe^−/− animals were sacrificed at 6 months and compared to controls. We found that bone contains excess iron associated with increased hepatic iron concentration in Hfe^−/− mice. We have shown that animals with iron overload have decreased bone formation rate, suggesting a direct impact of iron excess on active osteoblasts number. For bone mass parameters, we showed that iron deposition was associated with bone loss by producing microarchitectural impairment with a decreased tendency in bone trabecular volume and trabecular number. A disorganization of trabecular network was found with marrow spaces increased, which was confirmed by enhanced trabecular separation and star volume of marrow spaces. These microarchitectural changes led to a loss of connectivity and complexity in the trabecular network, which was confirmed by decreased interconnectivity index and increased Minkowski’s fractal dimension. Our results suggest for the first time in a genetic hemochromatosis mouse model, that iron overload decreases bone formation and leads to alterations in bone mass and microarchitecture. These observations support a negative effect of iron on osteoblast recruitment and/or function, which may contribute to iron-related osteoporosis.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.