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SUMMARY: Active or man-made seismic sources are often used to delineate subsurface geological structures via seismic imaging techniques. Although conventional controlled seismic sources have provided high-resolution subsurface images, the high cost of data acquisition necessitates improved use of cheaper alternative seismic sources for subsurface imaging, such as traffic noise, quarry blasts and earthquakes. Here, we explore the potential use of quarry blasts as an active seismic source for subsurface imaging. The goal of our study is to demonstrate the utility of quarry blasts as seismic sources for investigating subsurface structure with a field experiment in Mill Creek, southern Oklahoma. We deployed a 7455-m-long receiver line of 72 receivers (105 m spacing) extending away from a quarry, with a recording time of 28 d. We analyse similarities between blasts, wave types, origin time and excitation location of quarry blasts, which is important information for using such signal as an active source. Given a relative homogeneous geological condition in the study area, we could not observe clear reflected waves. However, the use of P- and S-wave velocities led to the delineation of a major basement damage zone in the study area, which further demonstrates the viability of quarry blasts as a useful seismic source for subsurface geological imaging.

Abstract This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p

Poultry production faces numerous challenges, including infectious diseases, stress, and the need for sustainable alternatives to antibiotics. Herbal additives and probiotics have gained attention as potential immunomodulatory agents to enhance the immune response and overall health of poultry. This review highlights the current understanding of the immunomodulatory effects of dietary Cloves, Syzygium aromaticum on poultry, with a particular emphasis on the need for more molecular research to unravel the underlying mechanisms. This knowledge will facilitate the development of targeted approaches to optimize poultry health, welfare, and productivity in the face of emerging climate change and other challenges.

AimTo evaluate the longitudinal association of sedentary behavior, light and moderate-to-vigorous physical activity (MVPA) participation with depressive symptoms and whether their possible association changed depending on the pandemic phase.MethodsThis longitudinal study conducted secondary analysis from the Spanish cohort of the Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-8). Sedentary behavior and physical activity were estimated via wrist-worn devices. Linear mixed models evaluated the longitudinal associations of sedentary behavior and physical activity (light and moderate-to-vigorous intensities) with depressive symptoms.ResultsIn total, 95 participants (67.5% women, 53.0 [±10.5] years of age on average) were monitored pre-COVID-19 and included in the analyses. Pre-COVID-19, 73.7% of participants presented depression, and, on average, participated in 13.2 (±1.08) hours/day of sedentary behavior, 2.42 (±0.90) hours/day of light physical activity and 23.6 (±19.80) minutes/day of MVPA. Considering all the observations (from November 2019 to October 2020), an additional hour/day of sedentary behavior was longitudinally associated with higher depressive symptoms [βstd = 0.06, 95% confidence interval (CI) 0.10 to 0.47], whereas an additional hour/day in light physical activity was associated with lower depressive symptoms (βstd = −0.06, 95% CI −0.59 to −0.15). Time in MVPA was not associated with depressive symptomatology. The association of sedentary behavior and light physical activity with depressive symptoms was significant only during pre-COVID-19 and COVID-19 relaxation periods, whereas during the strictest periods of the pandemic with regards to the restrictions (lockdown and de-escalation), the association was not observed.ConclusionSedentary behavior and light physical activity were longitudinally associated with depressive symptoms in participants with a history of MDD. The incorporation of light physical activity should be stimulated in adults with a history of MDD. Neither sedentary behavior nor light physical activity were associated with depressive symptoms during the most restrictive COVID-19 phases, whereas sedentary behavior (positively) and light physical activity (negatively) were associated with depressive symptoms in persons with MDD before and after the COVID-19 pandemic.

Abstract BackgroundThe use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient’s condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. ObjectiveSmartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. MethodsWe built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent’s Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. ResultsThe platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. ConclusionsRADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Abstract Background Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse. Methods STORY follows 720 young people aged 16–25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings (‘Ōura ring’) unobtrusively measures individuals’ daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months. Discussion By leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.

In this study, we have analyzed fluid mobility and thermal transport of the SiO2/kerosene nanofluid within two rotating stretchable disks. The top disk is simulated to be oscillating with a periodic velocity and squeezing continuously the nanofluid within a porous medium and making the fluid to flow perpendicularly to the situated magnetic field. Thermal radiation effects are considered in the heat transfer model. The non-linear (NL) PDEs that describe the nanofluid mobility structure and thermal transport are transformed into system of NL-ODEs by introducing adequately suitable non-dimensional variables after which the NL-ODEs were numerically solved via spectral quasi-linearization method (SQLM) on overlapping grids. The consequences of several pertinent parameters of the model on pressure, temperature, velocity, skin drag coefficient and thermal transport rate are examined and elucidated in detail with the aid of figures and tables. It was found that the flow structure with prescribing conditions develops negative pressure situation which has vast applications in modern day medical engineering, especially in the construction of air pressure stabilizers used in medical isolation and wound therapy physiology.

Squirrels are diurnal rodents with high visual acuity including unique properties well-suited for their natural environment. This study was conducted to explore some ocular microscopic features of tree-harbouring squirrels in the University of Ibadan, Ibadan, Nigeria. Two male squirrels were cage-trapped within the University premises. Light microscopic analysis was carried on paraffin-embedded eye samples harvested from the animals. The densely compacted stromal fibres, 351 ± 52.5 µm thick, formed the thickest part of the cornea, and the basement membrane of the corneal epithelium, 63.8 ± 13.0 µm thick, was notably positive with Periodic Acid Schiff (PAS) stain. Strong pigmentation was present at the choroid as well as the iridal and ciliary epithelia. The multiple layering of the retinal structure exhibited densely packed ganglion cells at the ganglion cell layer which together with the nerve fibre layer was observed to be thinnest at the more peripheral portion but becomes thicker towards the optic disc. Strongly positive glia fibrillary acidic protein (GFAP+) cells with their abundant fibrous processes were demonstrated immunohistochemically at the retinal nerve fibre layer and the optic nerve. Histological features of the retinal cellular components of the tree squirrels investigated has thus highlighted the structural adaptation of these animal species to their environmental arboreal habitat and diurnal lifestyle. Findings from this study, while further noted to be similar to that in human, showed that African tree squirrels represent promising rodent model for human retinal/ocular research.

Objectives Long-term sickness absence from employment has negative consequences for the economy and can lead to widened health inequalities. Sick notes (also called ‘fit notes’) are issued by general practitioners when a person cannot work for health reasons for more than 7 days. We quantified the sick note rate in people with evidence of COVID-19 in 2020, 2021 and 2022, as an indication of the burden for people recovering from COVID-19.Design Cohort study.Setting With National Health Service (NHS) England approval, we used routine clinical data (primary care, hospital and COVID-19 testing records) within the OpenSAFELY-TPP database.Participants People 18–64 years with a recorded positive test or diagnosis of COVID-19 in 2020 (n=365 421), 2021 (n=1 206 555) or 2022 (n=1 321 313); general population matched in age, sex and region in 2019 (n=3 140 326), 2020 (n=3 439 534), 2021 (n=4 571 469) and 2022 (n=4 818 870); people hospitalised with pneumonia in 2019 (n=29 673).Primary outcome measure Receipt of a sick note in primary care.Results Among people with a positive SARS-CoV-2 test or COVID-19 diagnosis, the sick note rate was 4.88 per 100 person-months (95% CI 4.83 to 4.93) in 2020, 2.66 (95% CI 2.64 to 2.67) in 2021 and 1.73 (95% CI 1.72 to 1.73) in 2022. Compared with the age, sex and region-matched general population, the adjusted HR for receipt of a sick note over the entire follow-up period (up to 10 months) was 4.07 (95% CI 4.02 to 4.12) in 2020 decreasing to 1.57 (95% CI 1.56 to 1.58) in 2022. The HR was highest in the first 30 days postdiagnosis in all years. Among people hospitalised with COVID-19, after adjustment, the sick note rate was lower than in people hospitalised with pneumonia.Conclusions Given the under-recording of postacute COVID-19-related symptoms, these findings contribute a valuable perspective on the long-term effects of COVID-19. Despite likely underestimation of the sick note rate, sick notes were issued more frequently to people with COVID-19 compared with those without, even in an era when most people are vaccinated. Most sick notes occurred in the first 30 days postdiagnosis, but the increased risk several months postdiagnosis may provide further evidence of the long-term impact.

BackgroundPrevious mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. ObjectiveThis study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. MethodsData were sourced from a large longitudinal mHealth study, wherein participants’ depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants’ behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. ResultsAnalyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=–93.61, P

Introduction & Background RADAR-Pipeline is an open-source Python framework designed to simplify and enhance mobile health data analysis. It has been designed to efficiently read and process the large amount of data generated through the RADAR-Base platform. RADAR-base is a scalable, real-time streaming and analytics open-source platform to facilitate research access and customisation requirements. Studies using the Radar-base platform have collected fine-grained longitudinal data from wearables and phones. The data can potentially create multitudes of digital biomarkers, which can be used to inform us greatly about the disease condition. Due to the sheer size of the data, it can be difficult for researchers to read and process those data -- a common task is identifying useful features and common data processing/analysis steps previously used by the community. Up to now, these have been hand-crafted by individual data scientists, often lacking the capability to be easily reused by the community without author-specific knowledge. Furthermore, generating variables based on already established research on a larger scale can be challenging and could hinder replication. Hence, we have designed RADAR-Pipeline to help researchers overcome these challenges. It empowers them to create and share their data analysis and visualisation pipelines, fostering collaboration and knowledge sharing within the research community. Objectives & Approach The primary objective of RADAR-Pipeline is to offer researchers a user-friendly and powerful platform to develop and share their research. Researchers can build reusable analysis and visualisation pipelines to ensure consistent and reliable results. It simplifies big data analysis by leveraging Apache Spark to handle large and complex mobile health datasets efficiently. Researchers can also save time and effort by reusing and extending existing pipelines built by others. Finally, the RADAR-Pipeline promotes collaboration and recognition by allowing researchers to share their work through the RADAR-base Analytics Catalogue, making their pipelines citable and accessible to the wider research community. Whilst Radar-pipeline has been designed to read data from Radar-base, it can also be used to read data from any dataset which uses Hadoop Distributed File System (HDFS) file system namespace. Relevance to Digital Footprints Mobile health data is rich and valuable for understanding human behaviour and health. RADAR-Pipeline addresses the challenges associated with analysing large and complex mobile health datasets, enabling researchers to extract valuable insights that can be used to (1) Improve public health: By enabling efficient analysis of large-scale mobile health data, RADAR-Pipeline can contribute to research efforts aimed at improving population health outcomes and developing effective interventions; (2) Personalised healthcare: By facilitating the extraction of individual-level features from mobile health data, RADAR-Pipeline can seamlessly be integrated with Kafka data streams and machine learning pipelines to process the data in real-time, which can then be utilised to create more effective and targeted real-time interventions. (3) Promote reproducible research: The framework's emphasis on transparency and reproducibility in research aligns with the conference's focus on the responsible use of digital mobile health data. Conclusions & Implications RADAR-Pipeline is a valuable tool for researchers, offering them the means to harness the potential of mobile health data. By adopting this framework, researchers can achieve efficient and scalable data analysis, thereby streamlining the extracting insights from digital footprints. This efficiency enables researchers to delve deeper into the data and uncover valuable patterns and trends. Furthermore, RADAR-Pipeline promotes collaboration and knowledge sharing within the research community. By providing a standardised framework for data analysis, RADAR-Pipeline facilitates collaboration among researchers, leading to the sharing of best practices and the dissemination of knowledge.

Introduction & Background With the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms. Objectives & Approach The present study summarizes findings from our five recent investigations that explored the relationships between depression severity and digital biomarkers captured by wearables and smartphones. These studies analyzed data from RADAR-MDD, a multinational mobile health program, involving 623 participants and tracked for up to two years. Participants' depression severity was measured biweekly using the PHQ-8 questionnaire conducted via smartphones. Concurrently, participants’ Fitbit and smartphone data were also collected. Given the longitudinal nature and repeated measurements for each participant, multilevel modeling techniques were employed to analyze the data. Relevance to Digital Footprints Our approach involved extracting features from passive data that reflect various aspects of daily behavior—such as sleep quality, social interaction, physical activity, and walking patterns—akin to digital footprints. Results We found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams). Conclusions & Implications Leveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues.

Abstract West Africa continues to host a growing number of low and intermediate‐magnitude earthquakes (M2‐5) along its passive margins, and its continental interior. Earthquake activity in these regions raises the need to comprehend the causes and the tectonic controls of the seismicity. Unfortunately, such studies are rare. Here, we apply single‐station inversion techniques to constrain fourteen focal mechanisms, computed after compiling a set of high‐quality waveforms. We describe the connection between seismicity, the contemporary stress field, anthropogenic activity and Holocene fault scarps in the region. Our results indicate transpressive stresses acting on the inherited brittle structures in the passive margins. We also observe a compressive regime in the intracontinental failed rifts. We attribute the seismicity to the reactivation of “weak” faults in the Neoproterozoic and Mesozoic failed rifts, the passive transform structures, and the intracratonic Precambrian brittle shear zones.

Discharge of untreated wastewater into water bodies pollutes the receiving waters. This study assessed the impact of abattoir and Kampala designers' effluent discharge on the water quality of the Yemoja River in Abeokuta, Nigeria. Twenty-seven water samples collected at three points, covering 180 m length, for 6 months were assessed for physicochemical parameters and metals and compared with the World Health Organization (WHO) and Standard Organization of Nigeria (SON) permissible standards. Most discharge point levels were found to be higher than their corresponding upstream and downstream values. Temperature, turbidity, magnesium, alkalinity, DO, TSS, phosphate, lead, BOD and potassium were found to be higher than normal levels for river water as prescribed by the WHO and SON while parameters like TDS, TS, calcium, chloride, nitrate, sulphate, iron and COD were lower than the standards. The total coliform values were higher than both national and international permissible limits, indicating contamination by human sewage or animal droppings. The water quality index indicated polluted water that is unfit for consumption. Findings from this research indicate that butchering and tie-and-dye activities have impacted river Yemoja water quality. Therefore, wastewater from the abattoir and textile industries be treated before discharge into water bodies. HIGHLIGHTS Temperature, turbidity, Mg, alkalinity, DO, TSS, phosphate, Pb, BOD and K were higher than prescribed normal levels for river water.; TDS, TS, Ca, Cl, nitrate, sulphate, iron and COD were lower than permissible standards.; The water quality index indicated polluted water that is unfit for consumption.; The Yemoja River had been impacted by discharge of abattoir and textile industry effluents.;

Abstract Tectonic forces alone cannot drive rifting in old and thick continental lithosphere. Geodynamic models suggest that thermal weakening is critical for lithospheric extension, yet many active rifts lack volcanism, seeming to preclude this process. We focus on one such rift, the Tanganyika-Rukwa segment of the East African Rift System, where we analyze local seismicity for shear wave anisotropy and couple the results with numerical modeling. The strongest splitting measurements are from earthquakes with paths sampling lower crustal regions of high compressional-to-shear wave velocity ratios and have fast polarization directions parallel to the local mantle flow, implying the existence of oriented melt lenses. This lower crustal magmatism and observed high surface heat flow are consistent with substantial lithospheric weakening and explain the enigmatic relief and increasing strain accommodation along the rift axis. We conclude that progressive nonvolcanic rifting is assisted by deep crustal melts yet to breach the surface.

EDIFY (Eating Disorders: Delineating Illness and Recovery Trajectories to Inform Personalised Prevention and Early Intervention in Young People) is an ambitious research project aiming to revolutionise how eating disorders are perceived, prevented and treated. Six integrated workstreams will address key questions, including: What are young people's experiences of eating disorders and recovery? What are the unique and shared risk factors in different groups? What helps or hinders recovery? How do the brain and behaviour change from early- to later-stage illness? How can we intervene earlier, quicker and in a more personalised way? This 4-year project, involving over 1000 participants, integrates arts, design and humanities with advanced neurobiological, psychosocial and bioinformatics approaches. Young people with lived experience of eating disorders are at the heart of EDIFY, serving as advisors and co-producers throughout. Ultimately, this work will expand public and professional perceptions of eating disorders, uplift under-represented voices and stimulate much-needed advances in policy and practice.