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BackgroundText-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention. ObjectiveThis systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention. MethodsA systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar. ResultsOverall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being. ConclusionsOur findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.

Introduction Despite the evidence supporting the value of digital supports for enhancing youth mental health services, there is a lack of guidance on how best to engage with young people in coproduction processes during the design and evaluation of these technologies. User input is crucial in digital mental health, especially for disadvantaged, vulnerable and marginalised young people as they are often excluded from coproduction. A scoping review of international literature written in English will explore the coproduction processes with marginalised young people in digital mental health supports, from mental health promotion to targeted interventions. The review is guided by the research question: what are the most appropriate coproduction processes for engaging young people, especially marginalised young people, in the different stages of designing and evaluating digital mental health supports? The review aims to map and summarise the evidence, inform the overall research project and address the knowledge gaps.Methods and analysis The scoping review uses Arksey and O’Malley’s framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols Extension for Scoping Reviews. From 22–24 October 2023, PubMed, Scopus, EBSCO, ASSIA, Web of Science, Ovid MEDLINE, Cochrane database, Embase, Google Scholar, ProQuest, OAIster and BASE will be systematically searched. Papers from 2021 onwards with a range of study designs and evidence that illustrate engagement with marginalised young people (aged 16–25) in the design, implementation and evaluation of digital technologies for young people’s mental health will be considered for inclusion. At least two reviewers will screen full texts and chart data. The results of this review will be summarised quantitatively through numerical counts of included literature and qualitatively through a narrative synthesis.Ethics and dissemination Ethical approval is not required. Results will be disseminated through publications in peer-reviewed journals.Trial registration number This scoping review protocol has been registered with the Open Science Framework (https://osf.io/9xhgv).

Abstract The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.

Abstract This paper makes a case for digital mental health and provides insights into how digital technologies can enhance (but not replace) existing mental health services. We describe digital mental health by presenting a suite of digital technologies (from digital interventions to the application of artificial intelligence). We discuss the benefits of digital mental health, for example, a digital intervention can be an accessible stepping-stone to receiving support. The paper does, however, present less-discussed benefits with new concepts such as ‘poly-digital’, where many different apps/features (e.g. a sleep app, mood logging app and a mindfulness app, etc.) can each address different factors of wellbeing, perhaps resulting in an aggregation of marginal gains. Another benefit is that digital mental health offers the ability to collect high-resolution real-world client data and provide client monitoring outside of therapy sessions. These data can be collected using digital phenotyping and ecological momentary assessment techniques (i.e. repeated mood or scale measures via an app). This allows digital mental health tools and real-world data to inform therapists and enrich face-to-face sessions. This can be referred to as blended care/adjunctive therapy where service users can engage in ‘channel switching’ between digital and non-digital (face-to-face) interventions providing a more integrated service. This digital integration can be referred to as a kind of ‘digital glue’ that helps join up the in-person sessions with the real world. The paper presents the challenges, for example, the majority of mental health apps are maybe of inadequate quality and there is a lack of user retention. There are also ethical challenges, for example, with the perceived ‘over-promotion’ of screen-time and the perceived reduction in care when replacing humans with ‘computers’, and the trap of ‘technological solutionism’ whereby technology can be naively presumed to solve all problems. Finally, we argue for the need to take an evidence-based, systems thinking and co-production approach in the form of stakeholder-centred design when developing digital mental health services based on technologies. The main contribution of this paper is the integration of ideas from many different disciplines as well as the framework for blended care using ‘channel switching’ to showcase how digital data and technology can enrich physical services. Another contribution is the emergence of ‘poly-digital’ and a discussion on the challenges of digital mental health, specifically ‘digital ethics’.

Signet ring cell lymphoma is an exceedingly rare subtype of non-Hodgkin lymphoma that was originally thought to be a morphologic variant of follicular lymphoma. To date, 56 cases have been reported, with the majority occurring in lymph nodes and bone marrow. Herein, we report a case of a 37-year-old female who presented with a left inguinal mass and high suspicion of lymphoma that was rendered on MRI. A successful ultrasound-guided core biopsy was performed. Pathologic examination revealed a diffuse large B-cell lymphoma (DLBCL) with unique signet ring histology and post-germinal center phenotype. In-situ hybridization showed an isolated BCL-6 gene rearrangement and confirmed the absence of a double-hit phenotype. This case would be situated as the second case of activated B-cell (ABC) signet ring lymphoma and the first case to arise in this anatomic location. The patient recently initiated therapy with a standard six-cycle regimen of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone).

Abstract Traditionally General Practitioner (GP) practices have been labelled as being in Rural, Urban or Semi-Rural areas with no statistical method of identifying which practices fall into each category. The main aim of this study is to investigate whether location and other characteristics can provide a tautology to identify different types of GP practice and compare the prescribing behaviours associated with the different practice types. To achieve this monthly open source prescription data were analysed by practice considering location, practice size, population density and deprivation rankings. One year’s data was subjected to k-means clustering with the results showing that only two different types of GP practice can be classified that are dependent on location characteristics in Northern Ireland. Traditional labels did not describe the two classifications fully and new classifications of Metropolitan and Non-Metropolitan were used. Whilst prescribing patterns were generally similar, it was found that Metropolitan practices generally had higher prescribing rates than Non-Metropolitan practices. Examining prescribing behaviours in accordance with British National Formulary (BNF) categories (known as chapters) showed that Chapter 4 (Central Nervous System) was responsible for most of the difference in prescribing levels. Within Chapter 4 higher prescribing levels were attributable to Analgesic and Antidepressant prescribing. The clusters were finally examined regarding the level of deprivation experienced in the area in which the practice was located. This showed that the Metropolitan cluster, having higher prescription rates, also had a higher proportion of practices located in highly deprived areas making deprivation a contributing factor.

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

Abstract Background Tuberculosis is a deadly disease caused by Mycobacterium species. The use of medicinal plants is an ancient global practice for the treatment and prevention of diverse ailments including tuberculosis. The aim of this study was to isolate and characterize antimycobacterial compounds by bioassay-guided fractionation of the acetone leaf extract of Oxyanthus speciosus. Methods A two-fold serial microdilution method was used to determine the minimum inhibitory concentration (MIC) against mycobacteria. Cytotoxicity and nitric oxide inhibitory activity of the isolated compounds was determined to evaluate in vitro safety and potential anti-inflammatory activity. Intracellular efficacy of the crude extract against Mycobacterium-infected macrophages was also determined. Results Two compounds were isolated and identified as lutein (1) and rotundic acid (2). These had good antimycobacterial activity against the four mycobacteria tested with MIC values ranging from 0.013 to 0.1 mg/mL. Rotundic acid had some cytotoxicity against C3A human liver cells. Lutein was not cytotoxic at the highest tested concentration (200 μg/mL) and inhibited nitric oxide production in RAW 264.7 macrophages by 94% at a concentration of 25 μg/mL. The acetone crude extract (120 μg/mL) of O. speciosus had intracellular antimycobacterial activity, reducing colony forming units by more than 90%, displaying bactericidal efficacy in a dose and time-dependent manner. Conclusion This study provides good proof of the presence of synergism between different compounds in extracts and fractions. It is also the first report of the antimycobacterial activity of lutein and rotundic acid isolated from Oxyanthus speciosus. The promising activity of the crude extract of O. speciosus both in vitro and intracellularly in an in vitro macrophage model suggests its potential for development as an anti- tuberculosis (TB) herbal medicine.

Surface to hand transfer of viruses represents a potential mechanism for human exposure. An experimental process for evaluating the touch transfer of aerosol-deposited material is described based on controlling surface, tribological, and soft matter components of the transfer process. A range of high-touch surfaces were evaluated. Under standardized touch parameters (15 N, 1 s), relative humidity (RH) of the atmosphere around the contact transfer event significantly influenced transfer of material to the finger-pad. At RH < 40%, transfer from all surfaces was ® plastics. Pooled human saliva was transferred at a lower rate compared to artificial saliva, indicating the role of rheological properties. The artificial saliva data were modeled by non-linear regression and the impact of environmental humidity and temperature were evaluated within a Quantitative Microbial Risk Assessment model using SARS-CoV-2 as an example. This illustrated that the trade-off between transfer efficiency and virus survival may lead to the highest risks of fomite transmissions in indoor environments with higher humidity.

The aim of the present work is to evaluate the rate and recovery speed of stump splinters of five species of Brachiaria (B. brizantha, B. decumbens, B. humidicola, B. mutica and B. ruziziensis) in the ecological conditions of Kisangani using four randomized complete blocks experiment. The results revealed that the best recovery rate is recorded with stump splinters of B. mutica (96%) followed by B. humidicola (85%), B. decumbens and B. ruziziensis (82 and 81% respectively), while B. brizantha recovers quite difficult (62%). - B. mutica recovers faster (average 11 days after planting) followed by B. decumbens, B. humidicola and B. ruziziensis (average 14, 16 and 15 days, respectively). B. brizantha recovers more slowly (on average 20 days after planting). These results indicate that under the ecological conditions of Kisangani city, B. mutica is well indicated to serve as alternative to livestock fodder especially in case of shortage due to rains late season. However, the assessment of the productivity of these different species is in progress.

The aim of the present study was to evaluate the productivity of five species of Brachiaria genus (B. brizantha, B. decumbens, B. humidicola, B. mutica and B. ruziziensis) under the 30-day interval mowing regime. After field experiment in four randomized complete block, the results revealed that the studied species differ significantly in the production of dry matter. They are ranked in ascending order as follow: B. mutica (372.9 Kg DM/ha)

Bacterial multidrug resistance causes many therapeutic failures, making it more difficult to fight against bacterial diseases. This study aimed to investigate the antibacterial activity of extract, fractions, and phytochemicals from Plectranthus glandulosus (Lamiaceae) against multidrug-resistant (MDR) Gram-negative phenotypes expressing efflux pumps. The crude extract after extraction was subjected to column chromatography, and the structures of the isolated compounds were determined using spectrometric and spectroscopic techniques. Antibacterial assays of samples alone and in the presence of an efflux pump inhibitor (phenylalanine-arginine β-naphthylamide, PAβN) were carried out using the broth microdilution method. The phytochemical study of P. glandulosus plant extract afforded seven major fractions (A–G) which lead to the isolation of seventeen known compounds. The ethanol extract of P. glandulosus was not active at up to 1024 μg/mL, whereas its fractions showed MICs varying from 32 to 512 μg/mL on the studied bacteria. Fraction C of P. glandulosus showed the lowest MIC (32 μg/mL) on E. coli ATCC8739 strain. Fraction D presented the highest activity spectrum by inhibiting the growth of 90% (9/10) of the studied bacteria. The presence of PAβN has improved the activity of extract and all fractions. Overall, the tested phytochemicals showed low activity against the studied bacteria. The overall results obtained in this study show that some fractions from P. glandulosus, mainly fractions C and D, should be investigated more for their possible use to fight against MDR bacteria.

Within many urban communities, there has been a steady decline in healthy living practices among minority families. This decline is often due to the lack of health services and health knowledge of youth and adults (Shirley, Roark, & Lewis, 2012). In order to better reach these particular audiences it has been suggested by scholars and practitioners to focus on community based programming. Partnerships formed through entities such as universities and community organizations have been shown to be quite successful in creating healthier families. This paper discusses one such program focused on increasing health knowledge of urban families. Activities offered included health screenings and healthy living programming for youth. Program results showed that nearly 95% of youth found the activities informative and gave them ways to think about their health and the health of their families.

Le présent travail avait pour but d’évaluer la productivité de cinq espèces du genre Brachiaria (B. brizantha, B. decumbens, B. humidicola, B. mutica et B. ruziziensis) sous le régime de fauche à intervalle de 30 jours. Après expérimentation en champ dans un dispositif de quatre blocs aléatoires complets, les résultats obtenus montrent que les espèces étudiées diffèrent significativement dans la production de la matière sèche. Elles se classent dans l’ordre croissant de la manière suivante: B. mutica (372,7 Kg MS/ha) < B. decumbens (1077,1 Kg MS/ha) < B. ruziziensis (1483,2 Kg MS/ha) < B. humidicola (1744,5 Kg MS/ha) < B. brizantha (2292,7 Kg MS/ha). En conséquence, B. brizantha est l’espèce locale la mieux indiquée pour la production de fourrage en régime de coupe de 30 jours dans les conditions naturelles de Kisangani. Mots-Clefs: Pâturage naturel, Biomasse, Graminées tropicales, Brachiaria sp., République démocratique du Congo

Le présent travail a pour but d’évaluer le taux et la vitesse de reprise des éclats de cinq espèces de Brachiaria (B. brizantha, B. decumbens, B. humidicola, B. mutica et B. ruziziensis) dans les conditions écologiques de Kisangani. Après expérimentation dans un dispositif de quatre blocs aléatoires complets, les résultats ont montré que le meilleur taux de reprise a été enregistré avec les éclats de souche de B. mutica (95,5%) suivi de B. humidicola (84,6%), B. decumbens et B. ruziziensis (82 et 81% respectivement) alors que B. brizantha reprend assez difficilement (62%). B. mutica reprend plus vite (en moyenne 11 jours après plantation) suivi de B. decumbens, B. humidicola et B. ruziziensis (en moyenne 14, 16 et 15 jours respectivement). B. brizantha reprend plus lentement (en moyenne 20 jours après plantation). Ces résultats montrent que dans les conditions écologiques de Kisangani, B. mutica est mieux indiqué pour servir d’alternative fourragère au bétail en situation d’urgence, notamment en cas de disette due à un retard de retour des pluies. Cependant, l’évaluation de la productivité de ces différentes espèces est en cours. Mots-Clefs: Plantes fourragères, Graminées tropicales, Brachiaria sp., Pâturage naturel, République démocratique du Congo

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

Tacrolimus exhibits high inter-patient pharmacokinetics (PK) variability, as well as a narrow therapeutic index, and therefore requires therapeutic drug monitoring. Germline mutations in cytochrome P450 isoforms 4 and 5 genes (CYP3A4/5) and the ATP-binding cassette B1 gene (ABCB1) may contribute to interindividual tacrolimus PK variability, which may impact clinical outcomes among allogeneic hematopoietic stem cell transplantation (HSCT) patients. In this study, 252 adult patients who received tacrolimus for acute graft versus host disease (aGVHD) prophylaxis after allogeneic HSCT were genotyped to evaluate if germline genetic variants associated with tacrolimus PK and pharmacodynamic (PD) variability. Significant associations were detected between germline variants in CYP3A4/5 and ABCB1 and PK endpoints (e.g., median steady-state tacrolimus concentrations and time to goal tacrolimus concentration). However, significant associations were not observed between CYP3A4/5 or ABCB1 germline variants and PD endpoints (e.g., aGVHD and treatment-emergent nephrotoxicity). Decreased age and CYP3A5*1/*1 genotype were independently associated with subtherapeutic tacrolimus trough concentrations while CYP3A5*1*3 or CYP3A5*3/*3 genotypes, myeloablative allogeneic HSCT conditioning regimen (MAC) and increased weight were independently associated with supratherapeutic tacrolimus trough concentrations. Future lines of prospective research inquiry are warranted to use both germline genetic and clinical data to develop precision dosing tools that will optimize both tacrolimus dosing and clinical outcomes among adult HSCT patients.

Osteoporosis is a bone disease that commonly results in a 30% incidence of fracture in hens used to produce eggs for human consumption. One of the causes of osteoporosis is the lack of mechanical strain placed on weight-bearing bones. In conventionally-caged hens, there is inadequate space for chickens to exercise and induce mechanical strain on their bones. One approach is to encourage mechanical stress on bones by the addition of perches to conventional cages. Our study focuses on the molecular mechanism of bone remodeling in end-of-lay hens (71 weeks) with access to perches. We examined bone-specific transcripts that are actively involved during development and remodeling. Using real-time quantitative PCR, we examined seven transcripts (COL2A1 (collagen, type II, alpha 1), RANKL (receptor activator of nuclear factor kappa-B ligand), OPG (osteoprotegerin), PTHLH (PTH-like hormone), PTH1R (PTH/PTHLH type-1 receptor), PTH3R (PTH/PTHLH type-3 receptor), and SOX9 (Sry-related high mobility group box)) in phalange, tibia and femur. Our results indicate that the only significant effect was a difference among bones for COL2A1 (femur > phalange). Therefore, we conclude that access to a perch did not alter transcript expression. Furthermore, because hens have been used as a model for human bone metabolism and osteoporosis, the results indicate that bone remodeling due to mechanical loading in chickens may be a product of different pathways than those involved in the mammalian model.

Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency (OMIM 201450) is the most common inherited disorder of fatty acid metabolism presenting with hypoglycaemia, hepatopathy and Reye-like symptoms during catabolism. In the past, the majority of patients carried the prevalent c.985A>G mutation in the ACADM gene. Since the introduction of newborn screening many other mutations with unknown clinical relevance have been identified in asymptomatic newborns. In order to identify functional effects of these mutant genotypes we correlated residual MCAD (OMIM 607008) activities as measured by octanoyl-CoA oxidation in lymphocytes with both genotype and relevant medical reports in 65 newborns harbouring mutant alleles. We identified true disease-causing mutations with residual activities of 0 to 20%. In individuals carrying the c.199T>C or c.127G>A mutation on one allele, residual activities were much higher and in the range of heterozygotes (31%-60%). Therefore, both mutations cannot clearly be associated with a clinical phenotype. This demonstrates a correlation between the octanoyl-CoA oxidation rate in lymphocytes and the clinical outcome. With newborn screening, the natural course of disease is difficult to assess. The octanoyl-CoA oxidation rate, therefore, allows a risk assessment at birth and the identification of new ACADM genotypes associated with asymptomatic disease variants.

BACKGROUND:Newborn screening for medium- and very long-chain acyl-CoA dehydrogenase (MCAD and VLCAD, respectively) deficiency, using acylcarnitine profiling with tandem mass spectrometry, has increased the number of patients with fatty acid oxidation disorders due to the identification of additional milder, and so far silent, phenotypes. However, especially for VLCADD, the acylcarnitine profile can not constitute the sole parameter in order to reliably confirm disease. Therefore, we developed a new liquid chromatography tandem mass spectrometry (LC-MS/MS) method to rapidly determine both MCAD- and/or VLCAD-activity in human lymphocytes in order to confirm diagnosis. METHODOLOGY:LC-MS/MS was used to measure MCAD- or VLCAD-catalyzed production of enoyl-CoA and hydroxyacyl-CoA, in human lymphocytes. PRINCIPAL FINDINGS:VLCAD activity in controls was 6.95+/-0.42 mU/mg (range 1.95 to 11.91 mU/mg). Residual VLCAD activity of 4 patients with confirmed VLCAD-deficiency was between 0.3 and 1.1%. Heterozygous ACADVL mutation carriers showed residual VLCAD activities of 23.7 to 54.2%. MCAD activity in controls was 2.38+/-0.18 mU/mg. In total, 28 patients with suspected MCAD-deficiency were assayed. Nearly all patients with residual MCAD activities below 2.5% were homozygous 985A>G carriers. MCAD-deficient patients with one other than the 985A>G mutation had higher MCAD residual activities, ranging from 5.7 to 13.9%. All patients with the 199T>C mutation had residual activities above 10%. CONCLUSIONS:Our newly developed LC-MS/MS method is able to provide ample sensitivity to correctly and rapidly determine MCAD and VLCAD residual activity in human lymphocytes. Importantly, based on measured MCAD residual activities in correlation with genotype, new insights were obtained on the expected clinical phenotype.

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail., Author(s): Maurice D. Mulvenna [sup.1], Raymond Bond [sup.1], Jack Delaney [sup.2], Fatema Mustansir Dawoodbhoy [sup.2], Jennifer Boger [sup.3], Courtney Potts [sup.1], Robin Turkington [sup.1] Author Affiliations: (1) grid.12641.30, 0000000105519715, School [...]