Another selection of papers that rated highly during peer review, with a broad focus on biology and life sciences. We’ve thrown in one paper on single photon sources for good measure. Happy reading!

 

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Fermentation Quality of Round-Bale Silage as Affected by Additives and Ensiling Seasons in Dwarf Napiergrass (Pennisetum purpureum Schumach)

Satoru Fukagawa, Yasuyuki Ishii and Ikuo Hattori

Fermentation quality of dwarf napiergrass (Pennisetum purpureum Schumach) was estimated for additives lactic acid bacteria and Acremonium cellulase (LAB + AC), fermented juice of epiphytic lactic acid bacteria (FJLB), and a no-additive control in 2006 via two ensiling methods—round-bale and vinyl-bag methods in 2006—and via two ensiling seasons—summer and autumn of 2013. Fermentation quality of dwarf napiergrass ensiled in the summer season was improved by the input of additives, with the highest quality in LAB + AC, followed by FJLB; the lactic acid content was higher, and the pH and sum of the butyric, caproic, and valeric acid contents were lower, resulting in an increase in the V-score value by each additive. The ensiling method in autumn without additives affected fermentation quality, mainly due to the airtightness, which was higher for round-bale processing than in vinyl bags, even with the satisfactory V-score of 72. Fermentation in round bales without additives had a higher quality in autumn than in summer, possibly due to the higher concentration of mono- and oligo-saccharides. Thus, it was concluded that dwarf napiergrass can be produced to satisfactory-quality silage by adding LAB + AC or FJLB in summer and even in the absence of additives in autumn.

Views and Preferences for Nicotine Products as an Alternative to Smoking: A Focus Group Study of People Living with Mental Disorders

Carla Meurk, Pauline Ford, Ratika Sharma, Lisa Fitzgerald and Coral Gartner

Aims and Background: People living with mental disorders experience a disproportionately higher burden of tobacco-related disease than the general population. Long-term substitution with less harmful nicotine products could reduce the tobacco-related harm among this population. This study investigated the views and preferences of people with mental health disorders about different nicotine products and their use as long-term substitutes for cigarettes. Methods: Semi-structured focus group discussion followed by a brief questionnaire. The discussion transcripts were analysed for content and themes and quantitative data summarised with descriptive statistics. Results: Twenty-nine participants took part in four focus groups. Vaping devices were the most acceptable nicotine products discussed; however preferences for nicotine products were individual and varied along aesthetic, pragmatic, sensory and symbolic dimensions. The concept of tobacco harm reduction was unfamiliar to participants, however they generally agreed with the logic of replacing cigarettes with less harmful nicotine products. Barriers to activating tobacco harm reduction included the symbolism of smoking and quitting; the importance placed on health; the consumer appeal of alternatives; and cost implications. Discussion and Conclusions: Engaging this population in tobacco harm reduction options will require communication that challenges black and white thinking (a conceptual framework in which smoking cigarettes or quitting all nicotine are the only legitimate options) as in practice this serves to support the continuance of smoking. Consumers should be encouraged to trial a range of nicotine products to find the most acceptable alternative to smoking that reduces health harms. Providing incentives to switch to nicotine products could help overcome barriers to using less harmful nicotine products among mental health consumers.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Peter Christiansen, Lars N. Nielsen, Kim A. Steen, Rasmus N. Jørgensen and Henrik Karstoft

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

Integration of Single-Photon Sources and Detectors on GaAs

Giulia Enrica Digeronimo, Maurangelo Petruzzella, Simone Birindelli, Rosalinda Gaudio, Sartoon Fattah Poor, Frank W.M. van Otten and Andrea Fiore

Quantum photonic integrated circuits (QPICs) on a GaAs platform allow the generation, manipulation, routing, and detection of non-classical states of light, which could pave the way for quantum information processing based on photons. In this article, the prototype of a multi-functional QPIC is presented together with our recent achievements in terms of nanofabrication and integration of each component of the circuit. Photons are generated by excited InAs quantum dots (QDs) and routed through ridge waveguides towards photonic crystal cavities acting as filters. The filters with a transmission of 20% and free spectral range ≥66 nm are able to select a single excitonic line out of the complex emission spectra of the QDs. The QD luminescence can be measured by on-chip superconducting single photon detectors made of niobium nitride (NbN) nanowires patterned on top of a suspended nanobeam, reaching a device quantum efficiency up to 28%. Moreover, two electrically independent detectors are integrated on top of the same nanobeam, resulting in a very compact autocorrelator for on-chip g⁽²⁾(τ) measurements.

ODK Scan: Digitizing Data Collection and Impacting Data Management Processes in Pakistan’s Tuberculosis Control Program

Syed Mustafa Ali, Rachel Powers, Jeffrey Beorse, Arif Noor, Farah Naureen, Naveed Anjum, Muhammad Ishaq, Javariya Aamir and Richard Anderson

The present grievous tuberculosis situation can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology, this can be achieved through quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during a testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, the application and 3G-enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to determine the impact of ODK Scan. Additionally, at the end of the study, users’ feedback was collected regarding app usability, user interface design and workflow changes. A total of 122 patients’ records were retrieved from the server and analysed in terms of quality. It was found that ODK Scan recognized 99.2% of multiple choice fill-in bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, but data verification and form-filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, but they were more concerned about the time-consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.