Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Prediction of effective genome size in metagenomic samples

We introduce a novel computational approach to predict effective genome size (EGS; a measure that includes multiple plasmid copies, inserted sequences, and associated phages and viruses) from short sequencing reads of environmental genomics (or metagenomics) projects. We observe considerable EGS differences between environments and link this with ecologic complexity as well as species composition (for instance, the presence of eukaryotes). For example, we estimate EGS in a complex, organism-dense farm soil sample at about 6.3 megabases (Mb) whereas that of the bacteria therein is only 4.7 Mb; for bacteria in a nutrient-poor, organism-sparse ocean surface water sample, EGS is as low as 1.6 Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects

X-ray dark-field radiography for in situ gout diagnosis by means of an ex vivo animal study

Gout is the most common form of inflammatory arthritis, caused by the deposition of monosodium urate (MSU) crystals in peripheral joints and tissue. Detection of MSU crystals is essential for definitive diagnosis, however the gold standard is an invasive process which is rarely utilized. In fact, most patients are diagnosed or even misdiagnosed based on manifested clinical signs, as indicated by the unchanged premature mortality among gout patients over the past decade, although effective treatment is now available. An alternative, non-invasive approach for the detection of MSU crystals is X-ray dark-field radiography. In our work, we demonstrate that dark-field X-ray radiography can detect naturally developed gout in animals with high diagnostic sensitivity and specificity based on the in situ measurement of MSU crystals. With the results of this study as a potential basis for further research, we believe that X-ray dark-field radiography has the potential to substantially improve gout diagnostics

Transitions between superstatistical regimes: Validity, breakdown and applications

It is pleasure to acknowledge helpful conversations with D. Xu and H. Kleinert. P.J., J.K., M.P. and V.S. were supported by the Czech Science Foundation (GAČR) Grant No. 17–33812L. C.B. was supported by EPSRC via the grant EP/N013492/1. J.K. also acknowledges support from the Austrian Science Fund, Grant No. I 3073-N32

Groundwater is a hidden global keystone ecosystem

Groundwater is a vital ecosystem of the global water cycle, hosting unique biodiversity and providing essential services to societies. Despite being the largest unfrozen freshwater resource, in a period of depletion by extraction and pollution, groundwater environments have been repeatedly overlooked in global biodiversity conservation agendas. Disregarding the importance of groundwater as an ecosystem ignores its critical role in preserving surface biomes. To foster timely global conservation of groundwater, we propose elevating the concept of keystone species into the realm of ecosystems, claiming groundwater as a keystone ecosystem that influences the integrity of many dependent ecosystems. Our global analysis shows that over half of land surface areas (52.6%) has a medium‐to‐high interaction with groundwater, reaching up to 74.9% when deserts and high mountains are excluded. We postulate that the intrinsic transboundary features of groundwater are critical for shifting perspectives towards more holistic approaches in aquatic ecology and beyond. Furthermore, we propose eight key themes to develop a science‐policy integrated groundwater conservation agenda. Given ecosystems above and below the ground intersect at many levels, considering groundwater as an essential component of planetary health is pivotal to reduce biodiversity loss and buffer against climate change

Forward pi^0 Production and Associated Transverse Energy Flow in Deep-Inelastic Scattering at HERA

Deep-inelastic positron-proton interactions at low values of Bjorken-x down to x \approx 4.10^-5 which give rise to high transverse momentum pi^0 mesons are studied with the H1 experiment at HERA. The inclusive cross section for pi^0 mesons produced at small angles with respect to the proton remnant (the forward region) is presented as a function of the transverse momentum and energy of the pi^0 and of the four-momentum transfer Q^2 and Bjorken-x. Measurements are also presented of the transverse energy flow in events containing a forward pi^0 meson. Hadronic final state calculations based on QCD models implementing different parton evolution schemes are confronted with the data.Comment: 27 pages, 8 figures and 3 table

Multi-Jet Event Rates in Deep Inelastic Scattering and Determination of the Strong Coupling Constant

Jet event rates in deep inelastic ep scattering at HERA are investigated applying the modified JADE jet algorithm. The analysis uses data taken with the H1 detector in 1994 and 1995. The data are corrected for detector and hadronization effects and then compared with perturbative QCD predictions using next-to-leading order calculations. The strong coupling constant alpha_S(M_Z^2) is determined evaluating the jet event rates. Values of alpha_S(Q^2) are extracted in four different bins of the negative squared momentum transfer~\qq in the range from 40 GeV2 to 4000 GeV2. A combined fit of the renormalization group equation to these several alpha_S(Q^2) values results in alpha_S(M_Z^2) = 0.117+-0.003(stat)+0.009-0.013(syst)+0.006(jet algorithm).Comment: 17 pages, 4 figures, 3 tables, this version to appear in Eur. Phys. J.; it replaces first posted hep-ex/9807019 which had incorrect figure 4

Measurement of Leading Proton and Neutron Production in Deep Inelastic Scattering at HERA

Deep--inelastic scattering events with a leading baryon have been detected by the H1 experiment at HERA using a forward proton spectrometer and a forward neutron calorimeter. Semi--inclusive cross sections have been measured in the kinematic region 2 <= Q^2 <= 50 GeV^2, 6.10^-5 <= x <= 6.10^-3 and baryon p_T <= MeV, for events with a final state proton with energy 580 <= E' <= 740 GeV, or a neutron with energy E' >= 160 GeV. The measurements are used to test production models and factorization hypotheses. A Regge model of leading baryon production which consists of pion, pomeron and secondary reggeon exchanges gives an acceptable description of both semi-inclusive cross sections in the region 0.7 <= E'/E_p <= 0.9, where E_p is the proton beam energy. The leading neutron data are used to estimate for the first time the structure function of the pion at small Bjorken--x.Comment: 30 pages, 9 figures, 2 tables, submitted to Eur. Phys.

A −436C>A Polymorphism in the Human FAS Gene Promoter Associated with Severe Childhood Malaria

Human genetics and immune responses are considered to critically influence the outcome of malaria infections including life-threatening syndromes caused by Plasmodium falciparum. An important role in immune regulation is assigned to the apoptosis-signaling cell surface receptor CD95 (Fas, APO-1), encoded by the gene FAS. Here, a candidate-gene association study including variant discovery at the FAS gene locus was carried out in a case-control group comprising 1,195 pediatric cases of severe falciparum malaria and 769 unaffected controls from a region highly endemic for malaria in Ghana, West Africa. We found the A allele of c.−436C>A (rs9658676) located in the promoter region of FAS to be significantly associated with protection from severe childhood malaria (odds ratio 0.71, 95% confidence interval 0.58–0.88, pempirical = 0.02) and confirmed this finding in a replication group of 1,412 additional severe malaria cases and 2,659 community controls from the same geographic area. The combined analysis resulted in an odds ratio of 0.71 (95% confidence interval 0.62–0.80, p = 1.8×10−7, n = 6035). The association applied to c.−436AA homozygotes (odds ratio 0.47, 95% confidence interval 0.36–0.60) and to a lesser extent to c.−436AC heterozygotes (odds ratio 0.73, 95% confidence interval 0.63–0.84), and also to all phenotypic subgroups studied, including severe malaria anemia, cerebral malaria, and other malaria complications. Quantitative FACS analyses assessing CD95 surface expression of peripheral blood mononuclear cells of naïve donors showed a significantly higher proportion of CD69+CD95+ cells among persons homozygous for the protective A allele compared to AC heterozygotes and CC homozygotes, indicating a functional role of the associated CD95 variant, possibly in supporting lymphocyte apoptosis

Towards a European Health Research and Innovation Cloud (HRIC)

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe