Sampling and Mass Detection of a Countable Number of Microparticles Using on-Cantilever Imprinting.

Liquid-borne particles sampling and cantilever-based mass detection are widely applied in many industrial and scientific fields e.g., in the detection of physical, chemical, and biological particles, and disease diagnostics, etc. Microscopic analysis of particles-adsorbed cantilever-samples can provide a good basis for measurement comparison. However, when a particles-laden droplet on a solid surface is vaporized, a cluster-ring deposit is often yielded which makes particles counting difficult or impractical. Nevertheless, in this study, we present an approach, i.e., on-cantilever particles imprinting, which effectively defies such odds to sample and deposit countable single particles on a sensing surface. Initially, we designed and fabricated a triangular microcantilever sensor whose mass m0, total beam-length L, and clamped-end beam-width w are equivalent to that of a rectangular/normal cantilever but with a higher resonant frequency (271 kHz), enhanced sensitivity (0.13 Hz/pg), and quality factor (~3000). To imprint particles on these cantilever sensors, various calibrated stainless steel dispensing tips were utilized to pioneer this study by dipping and retracting each tip from a small particle-laden droplet (resting on a hydrophobic n-type silicon substrate), followed by tip-sensor-contact (at a target point on the sensing area) to detach the solution (from the tip) and adsorb the particles, and ultimately determine the particles mass concentration. Upon imprinting/adsorbing the particles on the sensor, resonant frequency response measurements were made to determine the mass (or number of particles). A minimum detectable mass of ~0.05 pg was demonstrated. To further validate and compare such results, cantilever samples (containing adsorbed particles) were imaged by scanning electron microscopy (SEM) to determine the number of particles through counting (from which, the lowest count of about 11 magnetic polystyrene particles was obtained). The practicality of particle counting was essentially due to monolayer particle arrangement on the sensing surface. Moreover, in this work, the main measurement process influences are also explicitly examined

Progress of the European Metrology Network for Advanced Manufacturing

The European Metrology Network (EMN) for Advanced Manufacturing has been established in June 2021. Currently nine EMNs focussing on different important topics of strategic importance for Europe exist and form an integral part of EURAMET, the European Association of National Metrology Institutes (NMI). All EMNs are tasked to develop a high-level coordination of the metrology community in Europe in a close dialogue with the respective stakeholders. The development of a Strategic Research Agenda (SRA) is a key task for all EMNs in their thematic areas as important input for the European Partnership on Metrology programme in alignment with other relevant European Partnerships. This task will be based on an analysis of the existing metrology infrastructure and capabilities of the NMIs, the metrology research needs for advanced manufacturing identified in close cooperation with industrial stakeholders and a resulting gap analysis. Here we report on the progress of the EMN for Advanced Manufacturing

‘Help for trauma from the app stores?’ A systematic review and standardised rating of apps for Post-Traumatic Stress Disorder (PTSD)

Background: Mobile health applications (apps) are considered to complement traditional psychological treatments for Post-Traumatic Stress Disorder (PTSD). However, the use for clinical practice and quality of available apps is unknown. Objective: To assess the general characteristics, therapeutic background, content, and quality of apps for PTSD and to examine their concordance with established PTSD treatment and self-help methods. Method: A web crawler systematically searched for apps targeting PTSD in the British Google Play and Apple iTunes stores. Two independent researchers rated the apps using the Mobile App Rating Scale (MARS). The content of high-quality apps was checked for concordance with psychological treatment and self-help methods extracted from current literature on PTSD treatment. Results: Out of 555 identified apps, 69 met the inclusion criteria. The overall app quality based on the MARS was medium (M = 3.36, SD = 0.65). Most apps (50.7%) were based on cognitive behavioural therapy and offered a wide range of content, including established psychological PTSD treatment methods such as processing of trauma-related emotions and beliefs, relaxation exercises, and psychoeducation. Notably, data protection and privacy standards were poor in most apps and only one app (1.4%) was scientifically evaluated in a randomized controlled trial. Conclusions: High-quality apps based on established psychological treatment techniques for PTSD are available in commercial app stores. However, users are confronted with great difficulties in identifying useful high-quality apps and most apps lack an evidence-base. Commercial distribution channels do not exploit the potential of apps to complement the psychological treatment of PTSD

A GPS-referenced wavelength standard for high-precision displacement interferometry at λ = 633 nm

Since the turn of the millennium, the development and commercial availability of optical frequency combs has led to a steadily increase of worldwide installed frequency combs and a growing interest in using them for industrial-related metrology applications. Especially, GPS-referenced frequency combs often serve as a "self-calibrating" length standard for laser wavelength calibration in many national metrology institutes with uncertainties better than u = 1 × 10^-11. In this contribution, the application of a He-Ne laser source permanently disciplined to a GPS-referenced frequency comb for the interferometric measurements in a nanopositioning machine with a measuring volume of 200 mm × 200 mm × 25 mm (NPMM-200) is discussed. For this purpose, the frequency stability of the GPS-referenced comb is characterized by heterodyning with a diode laser referenced to an ultrastable cavity. Based on this comparison, an uncertainty of u = 9.2 × 10^-12 (τ = 8 s, k = 2) for the GPS-referenced comb has been obtained. By stabilizing a tunable He-Ne source to a single comb line, the long-term frequency stability of the comb is transferred onto our gas lasers increasing their long-term stability by three orders of magnitude. Second, short-term fluctuations-related length measurement errors were reduced to a value that falls below the nominal resolving capabilities of our interferometers (ΔL/L = 2.9 × 10^-11). Both measures make the influence of frequency distortions on the interferometric length measurement within the NPMM-200 negligible. Furthermore, this approach establishes a permanent link of interferometric length measurements to an atomic clock

A systematic quality rating of available mobile health apps for borderline personality disorder

Background Mobile health apps (MHAs) may offer a mean to overcome treatment barriers in Borderline Personality Disorder (BPD) mental health care. However, MHAs for BPD on the market lack transparency and quality assessment. Methods European app stores were systematically searched, and two independent trained reviewers extracted relevant MHAs. Employed methods and privacy and security details documentation of included MHAs were extracted. MHAs were then assessed and rated using the German version of the standardized Mobile Application Rating Scale (MARS-G). Mean values and standard deviations of all subscales (engagement, functionality, aesthetics, information, and therapeutic gain) and correlations with user ratings were calculated. Results Of 2,977 identified MHAs, 16 were included, showing average quality across the four main subscales (M=3.25, SD=0.68). Shortcomings were observed with regard to engagement (M=2.87, SD = 0.99), potential therapeutic gain (M=2.67, SD=0.83), existing evidence base (25.0% of included MHAs were tested empirically), and documented privacy and security details. No significant correlations were found between user ratings and the overall total score of the MARS-G or MARS-G main subscales. Conclusions Available MHAs for BPD vary in quality and evidence on their efficacy, effectiveness, and possible adverse events is scarce. More substantial efforts to ensure the quality of MHAs available for patients and a focus on transparency, particularly regarding privacy and security documentation, are necessary

Contributions of precision engineering to the revision of the SI

All measurements performed in science and industry are based on the International System of Units, the SI. It has been proposed to revise the SI following an approach which was implemented for the redefinition of the unit of length, the metre, namely to define the SI units by fixing the numerical values of so-called defining constants, including c, h, e, k and NA. We will discuss the reasoning behind the revision, which will likely be put into force in 2018. Precision engineering was crucial to achieve the required small measurement uncertainties and agreement of measurement results for the defining constants

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24-68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = -0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.Peer reviewe

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth