Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Two meta-analyses of noncontact healing studies

Reviews of empirical work on the efficacy of noncontact healing have found that interceding on behalf of patients through prayer or by adopting various practices that incorporate an intention to heal can have some positive effect upon their wellbeing. However, reviewers have also raised concerns about study quality and the diversity of healing approaches adopted, which makes the findings difficult to interpret. Some of these concerns can be addressed by adopting a standardised approach based on the double-blind randomised controlled clinical trial, and a recent review restricted to such studies has reported a combined effect size of .40 (p < .001). However, the studies in this review involve human participants for whom there can be no guarantee that control patients are not beneficiaries of healing intentions from friends, family or their own religious groups. We proposed to address this by reviewing healing studies that involved biological systems other than ‘whole’ humans (i.e. to include animal and plant work but also work involving human biological matter such as blood samples or cell cultures), which are less susceptible to placebo and expectancy effects and also allow for more circumscribed outcome measures. Secondly, doubts have been cast concerning the legitimacy of some of the work included in previous reviews so we planned to conduct an updated review that excluded that work. For phase 1, 49 non-whole human studies from 34 papers were eligible for review. The combined effect size weighted by sample size yielded a highly significant r of .258. However the effect sizes in the database were heterogeneous, and outcomes correlated with blind ratings of study quality. When restricted to studies that met minimum quality thresholds, the remaining 22 studies gave a reduced but still significant weighted r of .115. For phase 2, 57 whole human studies across 56 papers were eligible for review. When combined, these studies yielded a small effect size of r = .203 that was also significant. This database was also heterogeneous, and outcomes were correlated with methodological quality ratings. However, when restricted to studies that met threshold quality levels the weighted effect size for the 27 surviving studies increased to r = .224. Taken together these results suggest that subjects in the active condition exhibit a significant improvement in wellbeing relative to control subjects under circumstances that do not seem to be susceptible to placebo and expectancy effects. Findings with the whole human database gave a smaller mean effect size but this was still significant and suggests that the effect is not dependent upon the previous inclusion of suspect studies and is robust enough to accommodate some high profile failures to replicate. Both databases show problems with heterogeneity and with study quality and recommendations are made for necessary standards for future replication attempts

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future