Digital otoscopy in remote consultations.

Introduction: Otoscopes and otomicroscopes are the most commonly used instruments for visualizing the ear. Digital otoscopy (DO) could be used to improve diagnostics in primary health care by utilizing image enlargement. The aim of the study was to explore the possibilities of DO in remote consultations.

The implications of management practices on life cycle greenhouse gas emissions in biogas production.

Biogas production is seen as one of the key measures in circular economy providing several benefits for the environment. In practice, however, these benefits may not be achieved if the production is not implemented and managed in ways that reduce gaseous emissions. Thus, this study aimed at highlighting how different management practices impact the climate during the life cycle of biogas production in comparison to management without biogas production (reference).

Physical and mental health predicts better adherence to exercise intervention in older women: A post-hoc analysis.

Background: Adherence to exercise is crucial for promoting health and maintaining functioning.

Aims: To investigate predictors of adherence to exercise in the initially free supervised fall prevention RCT and its low-cost, self-sustained continuation among elderly women.

Methods: In the 2-year Kuopio Fall Prevention Study RCT, 457 women (aged 71-84) were offered a free initial 6-month supervised weekly training program (gym, Tai Chi) in the municipal facilities.

Effect of manure co-digestion on methane production, carbon retention, and fertilizer value of digestate.

Anaerobic digestion can provide benefits not only from the perspective of renewable energy production but also in the form of fertilization effect and increased retention of C in soils after digestate application. This study consisted of two phases, where the first phase assessed the suitability of carbon-rich co-feedstocks for methane production via laboratory testing. The second phase assessed the balance and stability of C before and after anaerobic digestion by systematic digestate characterization, and by evaluating its carbon retention potential using a modeling approach.

Machine-learning accelerated structure search for ligand-protected clusters.

Finding low-energy structures of ligand-protected clusters is challenging due to the enormous conformational space and the high computational cost of accurate quantum chemical methods for determining the structures and energies of conformers. Here, we adopted and utilized a kernel rigid regression based machine learning method to accelerate the search for low-energy structures of ligand-protected clusters. We chose the Au25(Cys)18 (Cys: cysteine) cluster as a model system to test and demonstrate our method.