Pre-exposure prophylaxis for South African adolescents: What evidence?

Adolescents and young women, particularly in South Africa, are at increased risk of HIV acquisition. To date, we have had limited primary prevention options to offer. Oral pre-exposure prophylaxis (PrEP) is an additional prevention modality that has now been proven to reduce HIV acquisition in those who take it consistently during periods of HIV infection exposure. We review the PrEP evidence in adolescents and highlight some of the research gaps. Our recommendation is to increase the number of demonstration projects and other scale-up opportunities to offer oral PrEP to at-risk adolescents, and monitor carefully to answer the outstanding questions

Cannabidiol interactions with voltage-gated sodium channels

Voltage-gated sodium channels are targets for a range of pharmaceutical drugs developed for treatment of neurological diseases. Cannabidiol (CBD), the non-psychoactive compound isolated from cannabis plants, was recently approved for treatment of two types of epilepsy associated with sodium channel mutations. This study used high resolution X-ray crystallography to demonstrate the detailed nature of the interactions between CBD and the NavMs voltage-gated sodium channel, and electrophysiology to show the functional effects of binding CBD to these channels. CBD binds at a novel site at the interface of the fenestrations and the central hydrophobic cavity of the channel. Binding at this site blocks the transmembrane-spanning sodium ion translocation pathway, providing a molecular mechanism for channel inhibition. Modelling studies suggest why the closely-related psychoactive compound tetrahydrocannabinol may not have the same effects on these channels. Finally, comparisons are made with the TRPV2 channel, also recently proposed as a target site for CBD. In summary, this study provides novel insight into a possible mechanism for CBD interactions with sodium channels

Improvement in medication education in a pediatric subspecialty practice

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to measure the impact of an educational intervention on parents of children taking methotrexate (MTX) for juvenile idiopathic arthritis (JIA).</p> <p>Methods</p> <p>This study was conducted using a pre- and postsurvey design. The parents of 100 children with JIA taking MTX for at least 2 months were surveyed during a routine office visit. The parents completed an initial questionnaire regarding the safe use, adverse effects, and guidelines for monitoring the toxicity of MTX. An educational intervention was then administered, and an identical follow-up questionnaire was given during the next office visit. Statistical analysis using a paired <it>t</it>-test (critical <it>P </it>value < 0.05) was performed on individuals who answered both questionnaires.</p> <p>Results</p> <p>There were 100 responses to the initial questionnaire and 67 responses to the follow-up questionnaire. The mean length of time between surveys was 2.9 ± 0.9 months. In those who completed both questionnaires, the overall correct score increased significantly from 75.8% to 93.4%, respectively (<it>P </it>< 0.0001). Individuals scored the lowest (49%) on the question that addressed MTX's impact on pregnancy and fertility.</p> <p>Conclusions</p> <p>MTX knowledge may be less than expected in the parents of children with JIA. Brief educational interventions in the pediatric subspecialty practice can significantly affect a family's understanding of their child's medications.</p

Identifying the Machine Learning Family from Black-Box Models

[EN] We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models exhibit similar vulnerabilities and strengths. In our method, we first consider how an adversary can systematically query a given black-box model (oracle) to label an artificially-generated dataset. This labelled dataset is then used for training different surrogate models (each one trying to imitate the oracle¿s behaviour). The method has two different approaches. First, we assume that the family of the surrogate model that achieves the maximum Kappa metric against the oracle labels corresponds to the family of the oracle model. The other approach, based on machine learning, consists in learning a meta-model that is able to predict the model family of a new black-box model. We compare these two approaches experimentally, giving us insight about how explanatory and predictable our concept of family is.This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0287, the EU (FEDER), and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, the Generalitat Valenciana PROMETEOII/2015/013. F. Martinez-Plumed was also supported by INCIBE under grant INCIBEI-2015-27345 (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad). J. H-Orallo also received a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the CFI, Cambridge, and a BEST grant (BEST/2017/045) from the GVA for another research stay at the CFI.Fabra-Boluda, R.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2018). Identifying the Machine Learning Family from Black-Box Models. Lecture Notes in Computer Science. 11160:55-65. https://doi.org/10.1007/978-3-030-00374-6_6S556511160Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1988)Benedek, G.M., Itai, A.: Learnability with respect to fixed distributions. Theor. Comput. Sci. 86(2), 377–389 (1991)Biggio, B., et al.: Security Evaluation of support vector machines in adversarial environments. In: Ma, Y., Guo, G. (eds.) Support Vector Machines Applications, pp. 105–153. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02300-7_4Blanco-Vega, R., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Analysing the trade-off between comprehensibility and accuracy in mimetic models. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 338–346. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30214-8_29Dalvi, N., Domingos, P., Sanghai, S., Verma, D., et al.: Adversarial classification. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 99–108. ACM (2004)Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/mlDomingos, P.: Knowledge discovery via multiple models. Intell. Data Anal. 2(3), 187–202 (1998)Duin, R.P.W., Loog, M., Pȩkalska, E., Tax, D.M.J.: Feature-based dissimilarity space classification. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 46–55. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17711-8_5Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. J. Mach. Learn. Res. 15(1), 3133–3181 (2014)Ferri, C., Hernández-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recognit. Lett. 30(1), 27–38 (2009)Giacinto, G., Perdisci, R., Del Rio, M., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fusion 9(1), 69–82 (2008)Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43–58 (2011)Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)Landis, J.R., Koch, G.G.: An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33, 363–374 (1977)Lowd, D., Meek, C.: Adversarial learning. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data mining, pp. 641–647. ACM (2005)Martınez-Plumed, F., Prudêncio, R.B., Martınez-Usó, A., Hernández-Orallo, J.: Making sense of item response theory in machine learning. In: Proceedings of 22nd European Conference on Artificial Intelligence (ECAI). Frontiers in Artificial Intelligence and Applications, vol. 285, pp. 1140–1148 (2016)Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372–387. IEEE (2016)Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 582–597. IEEE (2016)Sesmero, M.P., Ledezma, A.I., Sanchis, A.: Generating ensembles of heterogeneous classifiers using stacked generalization. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 5(1), 21–34 (2015)Smith, M.R., Martinez, T., Giraud-Carrier, C.: An instance level analysis of data complexity. Mach. Learn. 95(2), 225–256 (2014)Tramèr, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: USENIX Security Symposium, pp. 601–618 (2016)Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)Wallace, C.S., Boulton, D.M.: An information measure for classification. Comput. J. 11(2), 185–194 (1968)Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992

Measuring Energy Expenditure in Sub-Adult and Hatchling Sea Turtles via Accelerometry

Measuring the metabolic of sea turtles is fundamental to understanding their ecology yet the presently available methods are limited. Accelerometry is a relatively new technique for estimating metabolic rate that has shown promise with a number of species but its utility with air-breathing divers is not yet established. The present study undertakes laboratory experiments to investigate whether rate of oxygen uptake (o2) at the surface in active sub-adult green turtles Chelonia mydas and hatchling loggerhead turtles Caretta caretta correlates with overall dynamic body acceleration (ODBA), a derivative of acceleration used as a proxy for metabolic rate. Six green turtles (25–44 kg) and two loggerhead turtles (20 g) were instrumented with tri-axial acceleration logging devices and placed singly into a respirometry chamber. The green turtles were able to submerge freely within a 1.5 m deep tank and the loggerhead turtles were tethered in water 16 cm deep so that they swam at the surface. A significant prediction equation for mean o2 over an hour in a green turtle from measures of ODBA and mean flipper length (R2 = 0.56) returned a mean estimate error across turtles of 8.0%. The range of temperatures used in the green turtle experiments (22–30°C) had only a small effect on o2. A o2-ODBA equation for the loggerhead hatchling data was also significant (R2 = 0.67). Together these data indicate the potential of the accelerometry technique for estimating energy expenditure in sea turtles, which may have important applications in sea turtle diving ecology, and also in conservation such as assessing turtle survival times when trapped underwater in fishing nets

Malaria rapid diagnostic kits: quality of packaging, design and labelling of boxes and components and readability and accuracy of information inserts

<p>Abstract</p> <p>Background</p> <p>The present study assessed malaria RDT kits for adequate and correct packaging, design and labelling of boxes and components. Information inserts were studied for readability and accuracy of information.</p> <p>Methods</p> <p>Criteria for packaging, design, labelling and information were compiled from Directive 98/79 of the European Community (EC), relevant World Health Organization (WHO) documents and studies on end-users' performance of RDTs. Typography and readability level (Flesch-Kincaid grade level) were assessed.</p> <p>Results</p> <p>Forty-two RDT kits from 22 manufacturers were assessed, 35 of which had evidence of good manufacturing practice according to available information (<it>i.e</it>. CE-label affixed or inclusion in the WHO list of ISO13485:2003 certified manufacturers). Shortcomings in devices were (i) insufficient place for writing sample identification (n = 40) and (ii) ambiguous labelling of the reading window (n = 6). Buffer vial labels were lacking essential information (n = 24) or were of poor quality (n = 16). Information inserts had elevated readability levels (median Flesch Kincaid grade 8.9, range 7.1 - 12.9) and user-unfriendly typography (median font size 8, range 5 - 10). Inadequacies included (i) no referral to biosafety (n = 18), (ii) critical differences between depicted and real devices (n = 8), (iii) figures with unrealistic colours (n = 4), (iv) incomplete information about RDT line interpretations (n = 31) and no data on test characteristics (n = 8). Other problems included (i) kit names that referred to <it>Plasmodium vivax </it>although targeting a pan-species <it>Plasmodium </it>antigen (n = 4), (ii) not stating the identity of the pan-species antigen (n = 2) and (iii) slight but numerous differences in names displayed on boxes, device packages and information inserts. Three CE labelled RDT kits produced outside the EC had no authorized representative affixed and the shape and relative dimensions of the CE symbol affixed did not comply with the Directive 98/79/EC. Overall, RDTs with evidence of GMP scored better compared to those without but inadequacies were observed in both groups.</p> <p>Conclusion</p> <p>Overall, malaria RDTs showed shortcomings in quality of construction, design and labelling of boxes, device packages, devices and buffers. Information inserts were difficult to read and lacked relevant information.</p

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

Anti-müllerian hormone is not associated with cardiometabolic risk factors in adolescent females

&lt;p&gt;Objectives: Epidemiological evidence for associations of Anti-Müllerian hormone (AMH) with cardiometabolic risk factors is lacking. Existing evidence comes from small studies in select adult populations, and findings are conflicting. We aimed to assess whether AMH is associated with cardiometabolic risk factors in a general population of adolescent females.&lt;/p&gt; &lt;p&gt;Methods: AMH, fasting insulin, glucose, HDLc, LDLc, triglycerides and C-reactive protein (CRP) were measured at a mean age 15.5 years in 1,308 female participants in the Avon Longitudinal Study of Parents and Children (ALSPAC). Multivariable linear regression was used to examine associations of AMH with these cardiometabolic outcomes.&lt;/p&gt; &lt;p&gt;Results: AMH values ranged from 0.16–35.84 ng/ml and median AMH was 3.57 ng/ml (IQR: 2.41, 5.49). For females classified as post-pubertal (n = 848) at the time of assessment median (IQR) AMH was 3.81 ng/ml (2.55, 5.82) compared with 3.25 ng/ml (2.23, 5.05) in those classed as early pubertal (n = 460, P≤0.001). After adjusting for birth weight, gestational age, pubertal stage, age, ethnicity, socioeconomic position, adiposity and use of hormonal contraceptives, there were no associations with any of the cardiometabolic outcomes. For example fasting insulin changed by 0% per doubling of AMH (95%CI: −3%,+2%) p = 0.70, with identical results if HOMA-IR was used. Results were similar after additional adjustment for smoking, physical activity and age at menarche, after exclusion of 3% of females with the highest AMH values, after excluding those that had not started menarche and after excluding those using hormonal contraceptives.&lt;/p&gt; &lt;p&gt;Conclusion: Our results suggest that in healthy adolescent females, AMH is not associated with cardiometabolic risk factors.&lt;/p&gt

Key Intervention Characteristics in e-Health: Steps Towards Standardized Communication

PURPOSE: This paper reports expert opinion on e-health intervention characteristics that enable effective communication of characteristics across the diverse field of e-health interventions. The paper presents a visualization tool to support communication of the defining characteristics. METHODS: An initial list of e-health intervention characteristics was developed through an iterative process of item generation and discussion among the 12 authors. The list was distributed to 123 experts in the field, who were emailed an invitation to assess and rank the items. Participants were asked to evaluate these characteristics in three separate ways. RESULTS: A total of 50 responses were received for a response rate of 40.7%. Six respondents who reported having little or no expertise in e-health research were removed from the dataset. Our results suggest that 10 specific intervention characteristics were consistently supported as of central importance by the panel of 44 e-intervention experts. The weight and perceived relevance of individual items differed between experts; oftentimes, this difference is a result of the individual theoretical perspective and/or behavioral target of interest. CONCLUSIONS: The first iteration of the visualization of salient characteristics represents an ambitious effort to develop a tool that will support communication of the defining characteristics for e-health interventions aimed to assist e-health developers and researchers to communicate the key characteristics of their interventions in a standardized manner that facilitates dialog

Graphene Photonics and Optoelectronics

The richness of optical and electronic properties of graphene attracts enormous interest. Graphene has high mobility and optical transparency, in addition to flexibility, robustness and environmental stability. So far, the main focus has been on fundamental physics and electronic devices. However, we believe its true potential to be in photonics and optoelectronics, where the combination of its unique optical and electronic properties can be fully exploited, even in the absence of a bandgap, and the linear dispersion of the Dirac electrons enables ultra-wide-band tunability. The rise of graphene in photonics and optoelectronics is shown by several recent results, ranging from solar cells and light emitting devices, to touch screens, photodetectors and ultrafast lasers. Here we review the state of the art in this emerging field.Comment: Review Nature Photonics, in pres