Shifts In Patterns Research Articles

The West Pacific Teleconnection Drives the Interannual Variability of Autumn Wildfire Weather in the Western United States After 2000

AbstractWildfires pose a significant threat to human society as severe natural disasters. The western United States (US) is one hotspot that has experienced dramatic influences from autumn wildfires especially after 2000, but what has caused its year‐to‐year variations remains poorly understood. By analyzing observational and atmospheric reanalysis datasets, we found that the West Pacific (WP) pattern centered in the western North Pacific acted as a major climatic factor to the post‐2000 autumn wildfire activity by inducing anomalous high pressure over the western US via teleconnections with increased surface temperature, decreased precipitation, and reduced relative humidity. The WP pattern explains about one‐third of the post‐2000 years‐to‐year variance of the western US autumn wildfires. These effects were found to be much weaker in the 1980–1990s, as the active region of WP‐associated high pressure was confined to the eastern North Pacific. Such eastward shift of the WP teleconnection pattern and its resultant, enhanced influence on the weather conditions of western US autumn wildfire after 2000 are also captured by the sea surface temperature (SST)‐forced atmospheric model simulations with the Community Atmosphere Model version 6 (CAM6). The CAM6 ensemble‐mean changes in the WP teleconnection pattern at 2000 is about half of the observed changes, which implies that external radiative forcing and/or SST changes have played an important role in the WP pattern shift. Our results highlight a pressing need to consider the joint impacts of atmospheric internal variability and externally forced climate changes when studying the interannual variations of wildfire activity.

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.

Climate Change Impact on the Stability of Soil Slopes from a Hydrological and Geotechnical Perspective

Climate change (CC) is expected to cause significant changes in weather patterns, leading to extreme phenomena. Specifically, the intensity of precipitation extremes is continuously escalating, even in regions with decreasing average precipitation levels. Given that CC leads to long-term shifts in weather patterns and may affect the precipitation characteristics (i.e., frequency, duration, and intensity) directly related to groundwater table fluctuations and soil erosion phenomena, it has the potential to significantly affect soil slope instabilities. In turn, slope stability and the structural integrity of nearby structures and infrastructure will be affected. Accordingly, the present paper focuses on the impact of CC on the geohazard of soil slope instability by considering both hydrological aspects, i.e., the impact on rainfall intensity on the groundwater table and the geotechnical aspects of this complex problem. The findings reveal that the impact of CC on potential slope instabilities can be detrimental or even beneficial, depending on the specific site and water conditions. Therefore, it is essential to do the following: (a) collect all the available data of the area of interest, (b) assess their variations over time, and (c) examine each potentially unstable slope on a case-by-case basis to properly mitigate this geohazard.

Shifting agricultural land use and their unintended water consumption in the North China Plain

Agricultural land use (ALU) critically influences food production and water resource allocation. This study examines the dynamics of ALU in the North China Plain (NCP), an area marked by intensive agriculture and severe groundwater over-exploitation, with a focus on the multidimensional drivers and their implications for water management. By employing an elaborate classification scheme based on satellite imagery and extensive first-hand field data, we identified significant shifts in crop patterns. From 2013 to 2017, there was a notable transition from double crops (primarily wheat-maize) to single crops (primarily maize), covering 4600 km2 and accounting for 42% of single crops in 2013. From 2017 to 2022, there was a shift from single crops to economic forests, encompassing 3600 km2 and 22% of economic forests in 2017, including orchards, timber trees, and shelter forest belts. These shifts resulted in an 11% decrease in grain acreage (6800 km2) and an 11% increase in crop water consumption (6.3 km3) during 2013–2022. Notably, water consumption by economic forests increased by 126% (9.4 km3) during this period. This study highlights the critical need to balance competing demands for food and water security, providing valuable insights for similar agricultural regions worldwide.

Evaluating tourism scenarios within the limit of acceptable change framework in Barcelona

This article introduces an expanded Limit of Acceptable Change model, enhancing the traditional approach by incorporating various tourism scenarios and a broader range of indicators. This approach allows to gain a more comprehensive understanding and foresee the effects of diverse tourist profiles and intensities of use in Barcelona. It considers factors like motivation, origin, and accommodation type, and evaluates their effects on key sustainability indicators. The study reveals that significant shifts in tourism patterns are required to impact these indicators noticeably, emphasizing the need to consider both tourist numbers and typologies in sustainable tourism management. This approach equips destination planners with a valuable tool for strategic decision-making and long-term planning.

MaEng1, an endo-1,3-glucanase, contributes to the conidiation pattern shift through changing the cell wall structure in Metarhizium acridum

Microcycle conidiation has displayed the greater potential than normal conidiation in large-scale production of mycopesticides. Fungi require partial hydrolysis of the cell wall to achieve the necessary plasticity during their morphological changes. Therefore, various cell wall-associated hydrolases are crucial for fungal morphogenesis. Eng1, as an endo-β-1,3-glucanase, is involved in the cell separation of fungi, but its role in morphological changes of entomopathogenic fungi is not yet clear. Here, the endo-β-1,3-glucanase gene MaEng1 was characterized in the model entomopathogenic fungi M. acridum. MaEng1 possesses a typical carbohydrate hydrolase domain and belongs to the GH81 family. The functions of MaEng1 in fungal growth, stress tolerance, pathogenicity, and conidiation capacity were analyzed using targeted gene disruption. The results displayed that the absence of MaEng1 does not affect the fungal growth, stress tolerances, and pathogenicity in M. acridum. However, the knockout of MaEng1 led to the normal conidiation of M. acridum on the SYA medium, which can induce the microcycle conidiation. Moreover, the content of β-1,3-glucan in the cell wall of the MaEng1-disruption strain were significantly reduced and the exposures of β-1,3-glucan on the surface of the mature conidia and mycelia in ΔMaEng1 were declined, indicating that MaEng1 contributes to the conversion of conidiation mode in M. acridum by affecting the cell wall structure.

Not All Carcinogens Are Directly Mutagenic: A Holistic View of the Environmental Exposure–Cancer Connection

Cancers, a leading cause of morbidity and mortality worldwide, are undergoing notable shifts in incidence patterns. Cancer subtypes generally associated with advancing age are now increasingly being diagnosed in younger populations, while gender disparities in cancer susceptibility have fluctuated across various malignancies. Some cancer subtypes previously confined to specific geographical populations are now globally prevalent. Remarkably, these changing patterns of cancer incidence have occurred despite relatively stable mutational landscapes specific to each cancer type, highlighting the role of environmental exposures (exposomes) as potential drivers of these trends. Intriguingly, evidence suggests that the exposome may exert some of its influence through nonmutagenic mechanisms, although the precise ways in which environmental carcinogens trigger or promote cancer development remain poorly understood. This review summarizes the current understanding of the mutagenic and nonmutagenic mechanisms through which carcinogens, including tobacco smoke, air pollutants, metals, diet, and alcohol, impact cancer development, as this may help inform precision prevention and public health policy.

Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

KENDALIKAN TEKANAN DARAH PADA LANSIA DENGAN TERAPI KOMPLEMENTER

Non-Communicable Diseases (NCDs) often referred to as NCDs have become a considerable public health problem in Indonesia, this is marked by a shift in epidemiological disease patterns from infectious diseases to NCDs which globally continues to increase, one of which is hypertension. The purpose of this study is to determine the effect of complementary therapy on lowering blood pressure in the elderly with hypertension. This research method is pre-experimental with a one-group pre-post test design. The population in this study is 152 people with a sample of 18 respondents, Sampling with non-probability sampling techniques, The instrument that will be used in this study is a data processing questionnaire using the wilcoxon test. The results showed that the Sig (2-tailed) value for systolic blood pressure before and after the administration of complementary therapy was 0.000 < 0.05 its means that Ha is accepted and Ho is rejected. In conclusion, there is an effect of providing complementary therapy on reducing blood pressure in the elderly with hypertension

Disentangled Dynamic Graph Attention Network for Out-of-distribution Sequential Recommendation

Sequential recommendation, leveraging user-item interaction histories to provide personalized and timely suggestions, has drawn significant research interest recently. With the power of exploiting spatio-temporal dynamics, dynamic graph neural networks (DyGNNs) show great potential in sequential recommendation by modeling the dynamic relationship between users and items. However, spatio-temporal distribution shifts naturally exist in out-of-distribution sequential recommendation, where both user-item relationships and temporal sequences demonstrate pattern shifts. The out-of-distribution scenarios may lead to the failure of existing DyGNNs in handling spatio-temporal distribution shifts in sequential recommendation, given that the patterns they exploit tend to be variant w.r.t labels under distribution shifts. In this paper, we propose D isentangled I ntervention-based D ynamic graph A ttention networks with I nvariance Promotion ( I-DIDA ) to handle spatio-temporal distribution shifts in sequential recommendation by discovering and utilizing invariant patterns, ie , structures and features whose predictive abilities are stable across distribution shifts. Specifically, we first propose a disentangled spatio-temporal attention network to capture the variant and invariant patterns. By utilizing the disentangled patterns, we design a spatio-temporal intervention mechanism to create multiple interventional distributions and an environment inference module to infer the latent spatio-temporal environments, and minimize the invariance loss to leverage the invariant patterns with stable predictive abilities under distribution shifts. Extensive experiments demonstrate the superiority of our method over state-of-the-art sequential recommendation baselines under distribution shifts.

Unveiling the functions of the Lim-domain binding protein MaPtaB in Metarhizium acridum.

The Lim-domain binding protein PtaB, a homolog of Mfg1, governs conidiation and biofilm formation in several fungi. PtaB includes a conserved Lim-binding domain and two predicted nuclear localization sequences at its C terminus, and is co-regulated with the transcription factor Som1 downstream of the cyclic AMP-dependent protein kinase A (cAMP/PKA) pathway. However, the function of PtaB in entomopathogenic fungi remain poorly understood. Inactivation of PtaB in Metarhizium acridum resulted in delayed conidial germination, reduced conidial yield and increased sensitivities to cell wall disruptors, ultraviolet B irradiation and heat shock. In addition, the fungal virulence was significantly decreased after deletion of MaPtaB because of impairments in appressorium formation, cuticle penetration and evasion of insect immune responses in M. acridum. The MaPtaB-deletion and MaSom1-deletion strains showed similar phenotypes supporting that MaSom1/MaPtaB complex controls M. acridum normal conidiation and pathogenic progress. Upon loss of MaPtaB or MaSom1, the fungal sporulation mode in M. acridium shifted from microcycle conidiation to normal conidiation on SYA, a microcycle conidiation medium. Transcriptional analysis showed that more differentially expression genes were identified in MaSom1 RNA sequencing, and MaSom1 and MaPtaB may regulate the expression of genes for conidiation, nutrient metabolism and the cell cycle to control conidiation pattern shift. These data corroborate a complex control function for MaPtaB as an important central factor interacting with MaSom1 in the cAMP/PKA pathway, which links stress tolerance, conidiation and virulence in the entomopathogenic fungus M. acridum. © 2024 Society of Chemical Industry.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Postgraduate medical education in Public Health: the Italian scenario and a call to action

Abstract Introduction Public health training in Italy takes place within postgraduate medical training in Hygiene and Preventive Medicine. With 39 university schools of public health, spread across 17 of Italy’s 20 regions, the landscape is diverse. The government determines the number of available contracts annually. This study aims to provide an up-to-date analysis of the saturation of places in Public Health programmes, as well as an assessment of mobility trends between northern and southern Italy from 2017 to 2023. Methods Data on admissions, withdrawals and resignations were obtained from the Ministries of Universities and Health and from the archives of the ‘Associazione Liberi Specializzandi’ (ALS). Mobility was assessed as moving from the central-northern regions to the southern-islands of Italy, and vice versa, between registration with the Medical Council and specialisation programme enrollment. Results Between 2017 and 2023, a total of 2301 residents enrolled across Italy, out of 2953 available positions nationwide. During this period the number of available national places, increased from 180 to 576, impacting overall seat saturation. In 2017, 98.3% of positions were covered, compared to only 46.5% in 2023. Regarding the mobility trend, southern students attending northern schools increased from 8.3% in 2017 to 22.4% in 2023, while northern students attending southern schools decreased from 17.9% to 1%. However, the number of dropouts and withdrawals was higher in the North. Mobility students scored lower than non-mobility students nationally. Key messages • From 2017 to 2023, the number of residents enrolled increased, while the allocation rate decreased. There was a shift in mobility patterns, with more southern students moving north. • Addressing the identified inequalities and understanding the underlying factors driving mobility patterns is crucial for effective planning and allocation of resources.

Effects of short-term exposures to lower air temperature and cold spells on myocardial infarction hospital admissions: a nationwide study in Sweden

Abstract Background Exposures to lower air temperature and cold spells have been associated with an increased risk of various diseases. However, the short-term effect of lower air temperature and cold spells on myocardial infarction (MI) remains incompletely understood. Purpose We aimed to evaluate the short-term effects of lower air temperature and cold spells on MI hospital admissions within the nationwide SWEDEHEART registry. Methods This population-based nationwide study included 120,380 MI cases admitted to hospitals in Sweden during the cold season (October to March) from 2005 to 2019. Daily mean air temperature was estimated at 1 km2 spatial resolution across Sweden using a three-stage machine learning approach and percentiles of daily temperatures experienced by individuals in the same municipality were employed as individual exposure indicators to account for potential geographic adaptation. Cold spells were defined as periods of at least two consecutive days with a daily mean temperature below the 10th percentile of the temperature distribution for each municipality. A time-stratified case-crossover design incorporating a conditional logistic regression model with distributed lag non-linear model using lag 0-1 and lag 2-6 days was employed to evaluate the short-term effects of lower air temperature and cold spells on total MI, non-ST-segment elevation MI (NSTEMI) and ST-segment elevation MI (STEMI). Results We found significant increases in risk for total MI, NSTEMI, and STEMI in association with a decrease of one percentile in temperature at a lag of 2-6 days, indicated by ORs (95% CI) of 1.099 (1.057-1.142), 1.110 (1.06-1.164), and 1.076 (1.004-1.153), respectively. Additionally, we found that cold spells were significantly associated with increased risks for total MI, NSTEMI, and STEMI at a lag of 2-6 days, with ORs (95% CI) of 1.077 (1.037-1.12), 1.069 (1.02-1.119), and 1.095 (1.023-1.172), respectively. Moreover, independent effects of lower air temperature and cold spells on MI occurrence were found after adjusting for each other. Conversely, we observed that lower air temperature and cold spells were associated with decreased risks for MI at a lag of 0-1 days. Conclusions This nationwide case-crossover study demonstrated a novel biphasic pattern in MI risk following exposure to lower air temperatures and cold spells, characterized by an initial transient reduction in risk at lags 0-1 day, followed by a significant increase in risk at lags 2-6 days. As climate change continues to induce complex shifts in regional weather patterns, enhancing protections to reduce future cold-related cardiac hospitalizations is crucial.

Temporal trends in antithrombotic therapy following transcatheter aortic valve implantation in denmark

Abstract Background A number of randomized clinical trials have examined the use of antithrombotic therapy after transcatheter aortic valve implantation (TAVI), and guidelines have changed over time. Yet, data from clinical practise patterns are lacking and it is important to evaluate adherence to guideline recommendations over time. Purpose This study aims to evaluate the temporal use of antithrombotic therapy after TAVI in Denmark. Methods A nationwide Danish cohort of patients undergoing first-time TAVI from 2008 to 2017 were identified from Danish nationwide registries. Patients were categorized into four groups according to data on prescription filling of antithrombotic therapy within a three-months period post-TAVI: i) No antithrombotic therapy, ii) single antiplatelet therapy (SAPT), iii) dual antiplatelet therapy (DAPT), or iv) anticoagulant therapy. The use of antithrombotic therapy post-TAVI was examined for each year from 2008 to 2017. Results We identified 3187 patients undergoing first-time TAVI, among whom 120 (3.8%) did not receive any antithrombotic therapy, 715 (22.4%) were prescribed SAPT, 1244 (39.0%) were on DAPT, and 1108 (34.8%) were treated with anticoagulant therapy at three months post-TAVI. Patients without antithrombotic therapy were more often men (64.2%) and were more likely to have a history of malignancy (23.3%) compared with the other treatment groups. Patients who received oral anticoagulation had a higher burden of atrial fibrillation (75.7%). The proportion of patients with atrial fibrillation remained stable over time. During the first decade of TAVI, there was a shift in the antithrombotic treatment pattern: no therapy was ranging from 1.1% to 6.6%, SAPT from 19.1% to 29.4%, while DAPT decreased from 51.0% to 31.2% and anticoagulant therapy increased from 21.6% to 39.8% (Figure 1). Conclusion During the first decade of TAVI, antithrombotic therapy patterns post-TAVI changed substantially, such that the use of dual antiplatelet therapy decreased over time, while the use of anticoagulant therapy increased. Since atrial fibrillation remained stable over time, the increase in anticoagulant therapy is most likely due to changes in guidelines for management of atrial fibrillation in 2010. These findings show the evolving treatment patterns in the first decade of TAVI, which reflects the recommendations from clinical trials and guidelines for the management of antithrombotic therapy for patients with TAVI.

Climate projection of tropical cyclone lifetime in the western North Pacific basin

Abstract In this study, the potential changes of tropical cyclone (TC) lifetime in the western North Pacific basin are examined for different future climates. Using homogeneous 9 km-resolution dynamical downscaling with the Weather Research and Forecasting (WRF) model, we show that TC averaged lifetime displays insignificant change under both low and high greenhouse-gas concentration scenarios. However, more noticeable changes in the tails of TC lifetime statistics are captured in our downscaling simulations, with more frequent long-lived TCs (lifetime of 8-11 days) and less short-lived TCs (lifetime of 3-5 days). Unlike present-day simulations, it is found that the correlation between TC lifetime and the Niño index is relatively weak and insignificant in all future downscaling simulations, thus offering little explanation for these changes in TC lifetime statistics based on the El Niño-Southern Oscillation. More detailed analyses of TC track distribution in the western North Pacific basin reveal, nevertheless, a noticeable shift of TC track patterns towards the end of the 21st century. Such a change in TC track climatology results in an overall longer duration of TCs over the open ocean, which is consistent across future scenarios and periods examined in this study. This shift in the TC track pattern is ultimately linked to changes in the Western North Pacific Subtropical High, which retreats to the south during July and to the east during August-September. The results obtained in this study provide new insights into how large-scale circulations can affect TC lifetime in the western North Pacific basin in warmer climates.

Healthy lifestyle promotion project among healthcare workers

Abstract Background The psychosocial risks generated from difficult work conditions are damaging not only for the workers’ health and safety, but also have a huge impact on the organization and management efficiency. Description Some of the primary challenges that health workers face are demanding schedules and stressful shift patterns which lead to higher levels of worker burnout. Ensuring the well-being of healthcare staff is crucial not only for the individual workers, but it also brings important lessons to the community creating an environment built on a culture of health. Results In the past year, the Hospital of Cittadella (Province of Padua) has launched a set of programmes structured to help the staff make better lifestyle choices that affect not only their personal life but also create a more sustainable work environment. One of the projects involved various meetings with a Physical Medicine-Rehabilitation physician, a physical therapist, an Occupational Medicine physician and a medical yoga teacher. The encounters, attended by nearly 10% of the hospital staff, were aimed to help healthcare workers create a more balanced lifestyle and to implement physical activity and posture exercises in their daily routine. Four nutrition classes were also organized with the help of a nutritional biologist. The aim of this course was to show the basics of healthy diets supported by scientific data and guide the participants towards realistic and healthy meal plans. Lessons In conclusion, increasing the general wellbeing within the healthcare context is not an easy task, especially when organizational circumstances like staff reduction are being faced. These projects represent a small step towards facing these difficulties. It is important to note that the motivation, collaboration, inclusivity, flexibility and reliance are all elements that contribute to better mental and physical health of the healthcare staff which is crucial on an individual, organizational and societal level. Key messages • Increasing personal well being amongst healthcare workers can help create a more sustainable work environment. • A healthy work environment is crucial for a better functioning organization.

Transposable element small and long RNAs in aging brains and implications in Huntington's and Parkinson's disease.

Transposable Elements (TEs) are implicated in aging and neurodegenerative disorders, but the impact of brain TE RNA dynamics on these phenomena is not fully understood. Therefore, we quantified TE RNA changes in aging post-mortem human and mouse brains and in the neurodegenerative disorders Huntington's Disease (HD) and Parkinson's Disease (PD). We tracked TE small RNAs (smRNAs) expression landscape to assess the relationship to the active processing from TE long RNAs (lnRNAs). Human brain transcriptomes from the BrainSpan Atlas displayed a significant shift of TE smRNA patterns at age 20 years, whereas aging mouse brains lacked any such marked change, despite clear shift in aging-associated mRNA levels. Human frontal cortex displayed pronounced sense TE smRNAs during aging with a negative relationship between the TE smRNAs and lnRNAs indicative of age associated regulatory effects. Our analysis revealed TE smRNAs dysregulation in HD, while PD showed a stronger impact on TE lnRNAs, potentially correlating with the early average age of death for HD relative to PD. Furthermore, TE-silencing factor TRIM28 was down-regulated only in aging human brains, possibly explaining the lack of substantial TE RNA changes in aging mouse brains. Our study suggests brain TE RNAs may serve as novel biomarkers of human brain aging and neurodegenerative disorders.

Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care

Background:Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates. Objective:To develop a lightweight, explainable, self-supervised approach with personalized alert thresholds to detect adverse health events in PLwD, using changes in home activity. Methods:We hypothesized that health downturns manifest as detectable shifts in household movement patterns. Our approach leverages a Graph Barlow Twins contrastive model, which uses granular activity data and a macroscopic view to extract noise-robust, high-level and low-level discriminative features that represent daily activity patterns. Household-personalized alert thresholds are calculated based on clinician-set target alert rates, and daily anomaly scores are compared against these thresholds, triggering alerts for the clinical monitoring team. Model attention weights support explainability. Data were collected from a real-world dataset by the UK Dementia Research Institute (August 2019-April 2022). Results:Our model outperformed state-of-the-art temporal graph algorithms in detecting agitation and fall events across three patient cohorts, achieving 81% average recall and 88% generalizability at a target alert rate of 7%. Conclusion:We developed a novel, lightweight, explainable, and personalized Graph Barlow Twins model for real-world remote health monitoring in dementia care, with potential for broader applications in healthcare and sensor-based environments.

Extracting Meso- and Microscale Patterns of Urban Morphology Evolution: Evidence from Binhai New Area of Tianjin, China

Understanding and recognizing urban morphology evolution is a crucial issue in urban planning, with extensive research dedicated to detecting the extent of urban expansion. However, as urban development patterns shift from incremental expansion to stock optimization, related studies on meso- and microscale urban morphology evolution face limitations such as insufficient spatiotemporal data granularity, poor generalizability, and inability to extract internal evolution patterns. This study employs deep learning and meso-/microscopic urban form indicators to develop a generic framework for extracting and describing the evolution of meso-/microscale urban morphology. The framework includes three steps: constructing specific urban morphology datasets, semantic segmentation to extract urban form, and mapping urban form evolution using the Tile-based Urban Change (TUC) classification system. We applied this framework to conduct a combined quantitative and qualitative analysis of the internal urban morphology evolution of Binhai New Area from 2009 to 2022, with detailed visualizations of morphology evolution at each time point. The study identified that different locations in the area exhibited seven distinct evolution patterns: edge areal expansion, preservation of developmental potential, industrial land development pattern, rapid comprehensive demolition and construction pattern, linear development pattern, mixed evolution, and stable evolution. The results indicate that in the stock development phase, high-density urban areas exhibit multidimensional development characteristics by region, period, and function. Our work demonstrates the potential of using deep learning and grid classification indicators to study meso-/microscale urban morphology evolution, providing a scalable, cost-effective, quantitative, and portable approach for historical urban morphology understanding.