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Introduction First responders to disasters are at risk of developing post-traumatic stress disorder (PTSD). The trajectories of post-traumatic stress symptom severity differ among individuals, even if they are exposed to similar events. These trajectories have not yet been reported in non-Western first responders. Objectives We aimed to explore post-traumatic stress symptom severity trajectories and their risk factors in first responders to the 2011 Great East Japan Earthquake (GEJE)— a historically large earthquake that resulted in a tsunami and a nuclear disaster. Methods 56 388 Japan Ground Self-Defense Force (JGSDF) personnel dispatched to the GEJE were enrolled in this seven-year longitudinal cohort study. PTSD symptom severity was measured using the Impact of Event Scale-Revised (IES-R). Trajectories were identified using latent growth mixture models (LGMM). Nine potential risk factors for the symptom severity trajectories were analyzed using multinomial logistic regression. Results Five symptom severity trajectories were identified: “resilient” (54.7%), “recovery” (24.5%), “incomplete recovery” (10.7%), “late-onset” (5.7%), and “chronic” (4.3%). The main risk factors for the four non-resilient trajectories were older age, personal disaster experiences, and working conditions. These working conditions included duties involving body recovery or radiation exposure risk, longer deployment length, later or no post-deployment leave, and longer post-deployment overtime. Conclusions The majority of first responders to GEJE were resilient and developed few or no PTSD symptoms. A substantial minority experienced late-onset and chronic symptom severity trajectories. The identified risk factors can inform policies for prevention, early detection, and intervention in individuals at risk of developing symptomatic trajectories. Disclosure No significant relationships.

At present， parallelogram traction steering mechanism is mostly adopted in traction vehicles used in warehousing cargo loading and transfer. Because the steering mechanism does not conform to Ackermann's principle， the sliding friction force is large when steering， which will lead to traction and steering operation laborious. A trapezoidal traction steering mechanism based on Ackerman principle is designed， and uses the sequential quadratic programming genetic algorithm in multivariate nonlinear programming to optimize the specific dimensions of the trapezoidal traction steering mechanism， the size parameters which more satisfy the Ackerman principle are obtained. A prototype is made based on the optimized parameters. By using the Adams dynamics simulation software， the simulation and comparative experiment of the trapezoidal steering mechanism with the optimized parallelogram mechanism from three dimensions of steering trajectory， steering start torque， and energy conversion efficiency are carried out. The analysis results show that the optimized mechanism has a smoother steering trajectory， smaller steering starting torque and higher energy conversion efficiency.

The accurate prediction of human movement trajectory has a variety of benefits for many applications such as optimizing nurse’s trajectory in a hospital, optimizing movements of old or disabled people to minimize their routine efforts, etc. To perform human movement prediction, large amount of historical positioning data from sensors has to be collected and mined. We analyzed different human sequential movement prediction approaches and their limitations. In this work, we propose a new classifier named Apriori based Probability Tree Classifier (APTC) which predicts the human movement sequence patterns in indoor environment. The APTC classifier is integrated into Bagged J48 Machine learning algorithm which results in an ensemble model to predict the future human movement patterns. The patterns are mined based on spatial, temporal and social data which add more accuracy to our prediction. Our model also performs clustering mechanism to detect the abnormal patterns.

The fall armyworm (FAW), Spodoptera frugiperda, is a migratory noctuid pest that has recently invaded eastern Asia. From 2017 up till 2020, searchlight trapping was used to assess the extent of FAW migration across the South China Sea. Molecular and morphology-based identification confirmed that FAW made its first appearance on Yongxing Island on 11 April 2019, with most trapped individuals belonging to the S. frugiperda “corn-strain”. Carbon isotope analysis further showed that FAW moths originated from C4 host plants, while trajectory analyses revealed that migratory S. frugiperda adults are able to cross the South China Sea and enter mainland China. This long-distance migration process plausibly results in frequent genetic mixing between domestic FAW populations and those of neighboring Southeast Asian countries. Overall, this work provides unique insights into FAW migration across eastern Asia and ultimately can help advance pest forecasting, risk assessment and area-wide pest management.

The global water industry has a greater emphasis on energy management than ever before. The confluence of rising energy demand and costs, and net-zero greenhouse gas emission targets means the sector must rapidly transition to a new 'energy future'. Yet, few cities have assessed the long-term energy use of their water and wastewater systems. Here, we undertake a novel and integrated assessment of the historical trends of energy use for water and wastewater in three Australian and two US cities, collectively 17 million people. The key research question is what were the historical trends of energy use for water supply and wastewater treatment, and what can we understand about the drivers? The research contributes a first systematic time-series assessment of energy trajectories of both water and wastewater, across multiple cities. Uniquely, it integrates long-term (up to 20 years) energy dynamics in a comparative analysis. The work also contributes a qualitative analysis of driving factors behind the observed energy variations. The time-series analysis (2001−2020) identifies how energy use is evolving through time in widely differing climate, urban and water infrastructure conditions. The cities studied demonstrated downward trends in water use by 30–42% and wastewater collected by 5–30%, primarily due to water conservation and drought-related restrictions. Annual per-capita energy use for water supply reduced in Los Angeles (−58%, from 276 to 116.5 kWh/p/a), San Diego (−59%, from 503.7 to 204.2 kWh/p/a), Sydney (−26%, from 40.6 to 30.1 kWh/p/a) and increased in Melbourne (+859%, from 15.7 to 150.6 kWh/p/a) and Perth (+139%, from 118.1 to 281.9 kWh/p/a). Compared to water supply, energy use for wastewater was far more stable (it varied between 45 and 85 kWh/p/a), and not the crucial contributor to overall energy use dynamics. The significant increase in seawater desalination is identified as the primary driver of increased energy use in the three Australian cities. To offset this huge demand, developing renewable energy generation emerged as the key strategy. It causes high fluctuation of renewable energy use shares (Sydney: 317 GWh, accounting for 48.5% energy use for water and wastewater in 2011; compared to 19 GWh, accounting for only 2.5% in 2008). In contrast, both Los Angeles and San Diego managed to considerably reduce energy use by decreasing their imported water volume and energy intensity (the result of an adjusted supply portfolio). However, the absence of consistently comprehensive water/energy/renewable energy data remains a significant hurdle for a thorough quantitative analysis of drivers. Given these observations, it is evident that detailed quantitative analysis for influencing factors (e.g. water use, climate, infrastructure upgrading, sustainability targets), requires separately reported energy use for both water supply and wastewater, reported water from categorized sources and significant other data. By addressing these issues, we can have a clear path towards an energy-efficient, sustainable urban water system with net-zero emissions. • Urban per capita water supply and wastewater decreased by 30–42% and 5–30%. • Related energy showed contrasty trajectories, from −58% reduction to +859% increase. • Water supply options impacted more than wastewater on total energy management. • Source optimisation and renewable energy implementation caused the contrasting trajectories. • The growth of renewable energy products met some industry energy demand. [ABSTRACT FROM AUTHOR]

Understanding changes in land use and land cover (LULC) is a prerequisite for the sustainable management of ecosystems, with implications for regulatory policy. In this study, we use a hierarchical approach to quantify long-term changes in LULC in the catchment of the largest Central-European shallow lake (Lake Balaton, Hungary). We evaluate changes over three centuries at the catchment and different spatial levels of the shoreline. Within this framework, we develop a methodology suitable for analysing land use transformations in the shoreline zone, typically affected by LULC changes stemming from mass tourism. Our findings indicate that LULC transformations at the catchment level were relatively minor compared to those observed along the shoreline, where urbanization driven by mass tourism emerged as a drivers of LULC changes. The dynamics of urbanization closely mirrored socio-political shifts and exhibited a strong dependence on distance from the shore. Temporal trajectories of LULC changes revealed that the most significant alterations occurred within the 0–500 m and 500–1000 m shoreline distance zones during the socialist era when the lake served as a focal point for tourism in Central and Eastern Europe. While the process of mass tourism-induced urbanization has declined significantly since the collapse of the socialist regime, the sprawl of urban (built up) areas into rural territories persist. Given the unregulated nature of urbanization at the forefront, we emphasize the importance for policy makers and local stakeholders to control further transformations of semi-natural and natural areas into built up areas along the shoreline. Achieving sustainable LULC management in lakes affected by mass tourism, such as Lake Balaton, necessitates robust legislative and administrative efforts in spatial planning and regulation. This endeavour should be pursued as a shared interest among residents and tourists alike. • Long-term changes in land transformation were quantified. • Temporal trajectories of land conversion depended on distance from lakeshore. • Urban land transformation by mass tourism was the main driver. • Changes corresponded with characteristic socio-political periods. • Tourism planning and land use regulation is required to reach sustainability goals. [ABSTRACT FROM AUTHOR]

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China’s railway network.

Mosquito-borne diseases account for around one million deaths annually. There is a constant need for novel intervention mechanisms to mitigate transmission, especially as current insecticidal methods become less effective with the rise of insecticide resistance among mosquito populations. Previously, we used a near infra-red tracking system to describe the behaviour of mosquitoes at a human-occupied bed net, work that eventually led to an entirely novel bed net design. Advancing that approach, here we report on the use of trajectory analysis of a mosquito flight, using machine learning methods. This largely unexplored application has significant potential for providing useful insights into the behaviour of mosquitoes and other insects. In this work, a novel methodology applies anomaly detection to distinguish male mosquito tracks from females and couples. The proposed pipeline uses new feature engineering techniques and splits each track into segments such that detailed flight behaviour differences influence the classifier rather than the experimental constraints such as the field of view of the tracking system. Each segment is individually classified and the outcomes are combined to classify whole tracks. By interpreting the model using SHAP values, the features of flight that contribute to the differences between sexes are found and are explained by expert opinion. This methodology was tested using 3D tracks generated from mosquito mating swarms in the field and obtained a balanced accuracy of 64.5% and an ROC AUC score of 68.4%. Such a system can be used in a wide variety of trajectory domains to detect and analyse the behaviours of different classes, e.g., sex, strain, and species. The results of this study can support genetic mosquito control interventions for which mating represents a key event for their success.

Purpose. One of the perspectives that receives the most attention from studies in international business is cultural dimensions. This is due to the greater complexity and incidence of cultural aspects in economic performance. This paper explored the moderation effect of cultural orientations on the creation of innovation trajectories related to levels of innovation and their outcomes in countries from various geographical areas between 2011 and 2021. Design/Methodology/Approach. A growth trajectories model is conducted to achieve the research's aim, considering the country's cultural orientation, innovation inputs (institutions, human capital and research, infrastructure, market sophistication, and business sophistication), and impact on innovation output. The Global Innovation Index, Globe Project, and Global Entrepreneurship Index databases used this analysis, containing data from nations on different continents. The trajectories' analysis approach is utilized to achieve the desired goal, which allows for the assessment of the variations in innovation trajectory across countries with cultural tendencies towards performance and humane orientation from 2011 to 2021. Findings. The literature affirms positive results for various innovation inputs, but the results show differences in innovation outputs. The difference is related to their inputs (institutions, human capital and research, infrastructure, market sophistication, business sophistication), institutions, and market sophistication. Additionally, a difference depends on the country's performance culture, generating options to obtain higher outputs, such as knowledge and creative results. Research Limitations/Implications. Based on the results achieved, an attempt is made to provide a different perspective on innovation, especially evaluating the results over time and identifying decreasing trajectories that affect the innovation results in countries with different economic development conditions and cultural characteristics. Practical Implications. The results achieved make it possible to strengthen the analysis of the countries' strategies regarding innovation, especially in the permanent evaluation of the results, which encourages changes in the execution of innovative activities to maintain their performance over time. Social Implications. The contributions allow us to understand the dynamics of innovation in countries' knowledge and creative outputs over time. Originality/Value. The trajectory analysis used in the data analysis is perhaps one of the most robust techniques for a time series analysis. This allows for identifying trajectories for the study's independent variables and their influence on a country's innovation. [ABSTRACT FROM AUTHOR]

Background: Understanding the acute kidney injury (AKI) microenvironment changes and the complex cellular interaction is essential to elucidate the mechanisms and develop new targeted therapies for AKI. Methods: We employed unbiased single-cell RNA sequencing to systematically resolve the cellular atlas of kidney tissue samples from mice at 1, 2 and 3 days after ischemia-reperfusion AKI and healthy control. The single-cell transcriptome findings were validated using multiplex immunostaining, western blotting, and functional experiments. Results: We constructed a systematic single-cell transcriptome atlas covering different AKI timepoints with immune cell infiltration increasing with AKI progression. Three new proximal tubule cells (PTCs) subtypes (PTC-S1-new/PTC-S2-new/PTC-S3-new) were identified, with upregulation of injury and repair-regulated signatures such as Sox9, Vcam1, Egr1, and Klf6 while with downregulation of metabolism. PTC-S1-new exhibited pro-inflammatory and pro-fibrotic signature compared to normal PTC, and trajectory analysis revealed that proliferating PTCs were the precursor cell of PTC-S1-new, and part of PTC-S1-new cells may turn into PTC-injured and then become fibrotic. Cellular interaction analysis revealed that PTC-S1-new and PTC-injured interacted closely with infiltrating immune cells through CXCL and TNF signaling pathways. Immunostaining validated that injured PTCs expressed a high level of TNFRSF1A and Kim-1, and functional experiments revealed that the exogenous addition of TNF-α promoted kidney inflammation, dramatic injury, and specific depletion of TNFRSF1A would abrogate the injury. Conclusions: The single-cell profiling of AKI microenvironment provides new insight for the deep understanding of molecular changes of AKI, and elucidates the mechanisms and developing new targeted therapies for AKI. [ABSTRACT FROM AUTHOR]

Background and Aim: Evidence about recently proposed data-driven clusters of type 2 diabetes (T2D) is mainly about its prognostic effects and Western populations. We tested the applicability of this clustering approach among the Chinese population. We further investigated the cardiorenal risk profiles among different T2D sub-phenotypes cross-sectionally and before diabetes diagnosis. Methods: With the use of data from the China Health and Nutrition Survey (1989–2009), 6,728 participants with available fasting blood samples and completed questionnaires in the 2009 survey were included. Glycemic statuses (normoglycemia, prediabetes, and new-onset T2D) were defined according to the 2020 American Diabetes Association criteria. Data-driven cluster analysis was conducted among new-onset T2D based on five variables: age at onset, body mass index (BMI), hemoglobin A1c, homeostasis model estimates of β-cell function, and insulin resistance. Linear regression models were used to cross-sectionally examine the differences of cardiorenal risk factors (body fat distribution, blood pressure, lipid profiles, and kidney function) between glycemic statuses. Mixed-effects models were used to explore a maximum of 20-year trajectories of cardiovascular risk factors (body fat distribution and blood pressure) before diabetes diagnosis. Results: Among 557 (8.3%) new-onset T2D, four sub-phenotypes were found, with 57 (10.2%) assigned to the severe insulin-resistant diabetes (SIRD), 72 (12.9%) to the severe insulin-deficient diabetes (SIDD), 167 (30.0%) to the mild obesity-related diabetes (MOD), and 261 (46.9%) to the mild age-related diabetes (MARD). People clustered within different T2D sub-phenotypes had different cardiorenal risk profiles. Three T2D sub-phenotypes (SIRD, SIDD, and MOD) had worse cardiorenal abnormalities, while the risk burden in the MARD sub-phenotype was similar to that in prediabetes. Compared with people with other T2D sub-phenotypes, people in the MOD sub-phenotype had a faster increment in BMI, waist, upper arm circumference, and triceps skinfold up to 10 years before diagnosis. Blood pressure was less distinct in different T2D sub-phenotypes; however, SIDD and MOD clusters had higher blood pressure levels before diabetes diagnosis. Conclusions: Data-driven T2D sub-phenotyping is applicable in the Chinese population. Certain sub-phenotypes such as MARD only have a minor cardiorenal risk burden, and distinct cardiovascular risk development occurs long before diabetes diagnosis. Our findings can help improve early prevention and targeted treatment for diabetes. [ABSTRACT FROM AUTHOR]

The stochastically perturbed parametrisation tendency (SPPT) scheme is a well‐established technique in ensemble forecasting to address model uncertainty by introducing perturbations into the tendencies provided by the physics parametrisations. The magnitude of the perturbations scales with the local net parametrisation tendency, resulting in large perturbations where diabatic processes are active. Rapidly ascending air streams, such as warm conveyor belts (WCBs) and organized tropical convection, are often driven by cloud diabatic processes and are therefore prone to such perturbations. This study investigates the effects of SPPT and initial condition perturbations on rapidly ascending air streams by computing trajectories in sensitivity experiments with the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system, which are set up to disentangle the effects of initial conditions and physics perturbations. The results demonstrate that SPPT systematically increases the frequency of rapidly ascending air streams. The effect is observed globally, but is enhanced in regions where the latent heating along the trajectories is larger. Despite the frequency changes, there are only minor modifications to the physical properties of the trajectories due to SPPT. In contrast to SPPT, initial condition perturbations do not affect WCBs and tropical convection systematically. An Eulerian perspective on vertical velocities reveals that SPPT increases the frequency of strong upward motions compared with experiments with unperturbed model physics. Consistent with the altered vertical motions, precipitation rates are also affected by the model physics perturbations. The unperturbed control member shows the same characteristics as the experiments without SPPT regarding rapidly ascending air streams. We make use of this to corroborate the findings from the sensitivity experiments by analyzing the differences between perturbed and unperturbed members in operational ensemble forecasts of ECMWF. Finally, we give an explanation of how symmetric, zero‐mean perturbations can lead to a unidirectional response when applied in a nonlinear system. [ABSTRACT FROM AUTHOR]

Objective: Breast cancer survivors (BCS) may experience problems to adjust to their situation after cancer treatment completion. In case of severe distress, an adjustment disorder (AD) might develop. This study investigates the course of AD symptoms during 1 year and its predictors in BCS up to 5 years post-treatment.Methods: BCS completed the Hospital Anxiety and Depression Scale (HADS) at baseline, 3, 6, and 12 months. HADS total scores were defined as no mental disorder (MD) symptoms (≤ 10), AD symptoms (11-14), and any other MD symptoms (≥ 15). Over the course of four assessments, symptom trajectories were a priori defined as no MD symptoms, AD symptoms, fluctuating AD symptoms below and above cut-offs, or any other MD symptoms. Complementary, latent class growth analysis (LCGA) was used to identify data-driven trajectories.Results: Among 293 BCS with complete data, the majority was classified as no MD symptoms (54.4%), followed by 37.5% in the fluctuating AD symptoms trajectory. Only 1.4% had AD symptoms, and 6.8% had any other MD symptoms. With LCGA (N = 459), three trajectories were found: stable no MD symptoms (58.6%), stable AD symptoms (32.9%), and high increasing any other MD symptoms (8.5%). Compared to BCS with no MD symptoms, BCS with fluctuating AD symptoms or any other MD symptoms were younger, less able to handle daily activities, and showed more social support discrepancy, neuroticism, and less optimism.Conclusions: Results of our study showed that AD symptoms in BCS up to 5 years post-treatment fluctuate over 1 year. It is thus important to appropriately assess AD over the course of 5 years post-treatment as AD symptoms can fluctuate. [ABSTRACT FROM AUTHOR]

The construction professionals attempt to enhance construction site visualization and modeling using unmanned aerial vehicle (UAV) technology. However, there is no research covering best flying and environmental variables for this purpose. This research aimed to determine the optimized flying and environmental variables for 2D dronography and 3D modeling for the construction progress monitoring. The research focused on building façade facing east and conducted an experimental study in the Eco-Home building at the University of Technology Malaysia. For 2D visualization, 900 photos were captured based on the three setpoints (20 m, 30 m, and 40 m) perpendicular to the building façade. Flying operation features of the employed drone (i.e., DJI Phantom4) enforced to set the first setpoint at 20 m perpendicular to the façade to get the whole façade in the required 80% of the photos. The 30 m and 40 m setpoints were defined based on the horizontal circles making the angle of 12° from the drone's z-ax to x-ax perpendicular to the façade. The images were analyzed using image color summarizer (ICS) software. The research found that the best distance is 30 m, and noontime is the best time for dronography. The proper temperature, humidity, lux, and wind speed to quality 2D image are; 38.1°, 55.5% Rh, 38,107 lx, and 0.1 m/s. The ICS software results were validated by applying the Image Processing Toolbox of MATLAB, particularly the thresholding-based image RGB pixel analyzing technique. The regression analysis showed 93% accuracy and completeness of the results. A trajectory optimization study has also been conducted, which determined that the drone could considerably control trajectories at the target positions and referenced velocity. The findings can significantly help the construction professionals to calibrate their drone's flying variables for quality 3D modeling the construction progress monitoring. [ABSTRACT FROM AUTHOR]

Purpose: Little research has investigated the correlation of changes in long-term apolipoprotein B/apolipoprotein A-I (ApoB/ApoA-I) ratio with risk of new-onset type 2 diabetes (T2D) among ordinary people. Therefore, the research took long-term ApoB/ApoA-I ratio trajectories as independent variables for exploring their association with the risk of newly diagnosed T2D. Methods: Altogether 5362 non-diabetic participants with a median age of 49 were enrolled in the cohort study. Their ApoB/ApoA-I ratio trajectories from 2016 to 2019 were analyzed and grouped using group-based trajectory modeling. The Kaplan–Meier approach was employed for calculating the newly diagnosed T2D-related incidence with different ApoB/ApoA-I ratio trajectories. A log-rank test was conducted for testing the presence of statistical difference in new-onset T2D incidence among the different ApoB/ApoA-I ratio trajectory groups. A multivariate Cox proportional hazards regression model was adopted for analyzing how ApoB/ApoA-I ratio trajectory changes affected new-onset T2D. Results: From 2016 to 2019, 199 patients developed T2D (3% in 3 years). The incidence of T2D was 2.0%, 3.28%, 5.86%, and 6.92% for low, middle, upper, and high ApoB/ApoA-I ratio trajectories, respectively. Following adjustment of underlying confounding factors, in contrast to low ApoB/ApoA-I ratio trajectory, new-onset T2D risk ratios and hazard ratio (HR) (95% confidence intervals [CI]) for the middle lower ApoB/ApoA-I ratio trajectory, and upper middle and high ApoB/ApoA-I ratio trajectories were [HR (95% CI)] 1.35(0.88–2.08), 1.98(1.27–3.09) and 2.42(1.35–4.34), respectively, indicating high and statistically significant risks of T2D. Conclusion: Variations of the ApoB/ApoA-I ratio trajectory exerted independent effects on the 3-year incidence of T2D. Long-term monitoring on the ApoB/ApoA-I ratio locus may help improve the identification on patients with T2D. [ABSTRACT FROM AUTHOR]

Background: Single amino acid substitutions in the Iduronate-2-sulfatase enzyme result in destabilization of the protein and cause a genetic disorder called Hunter syndrome. To gain functional insight into the mutations causing Hunter syndrome, various bioinformatics tools were employed, and special significance is given to molecular docking. Results: In-silico tools available online for preliminary analysis including SIFT, PolyPhen 2.0, etc., were primarily employed and have identified 51 Non-synonymous Single Nucleotide Polymorphisms (ns-SNPs) as possibly deleterious. Further, modelling and energy minimization followed by Root Mean Square Deviation (RMSD) calculation has labelled 42 mutations as probably deleterious ns-SNPs. Later, trajectory analysis was performed using online tools like PSIPRED, SRide, etc., and has predicted six ns-SNPs as potentially deleterious. Additionally, docking was performed, and three candidate ns-SNPs were identified. Finally, these three ns-SNPs were confirmed to play a significant role in causing syndrome through root mean square fluctuation (RMSF) calculations. Conclusion: From the observed results, G134E, V503D, and E521D were predicted to be candidate ns-SNPs in comparison with other in-silico tools and confirmed by RMSF calculations. Thus, the identified candidate ns-SNPs can be employed as a potential genetic marker in the early diagnosis of Hunter syndrome after clinical validation. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level. [ABSTRACT FROM AUTHOR]

The purpose of this study was to gain a clearer understanding of the relation between parental relationship qualities and overall emerging adulthood (EA) marijuana use processes. The present study drew from an ethnically and socioeconomially diverse sample of EAs (ages 19–22) and their parents (n = 470) from the Pacific Northwest region. This study used parent-report and child-report data to capture measures of parenting and EA marijuana use outcomes. Latent Class Growth Analysis (LCGA) was used to model trajectories of marijuana use and risk factor analyses were used to examine how marijuana group membership varied by covariates and parental relationship qualities. Results revealed that lower levels of family cohesion and quality of parent–child communication were more likely to predict membership in the high-using groups and moderate-decreasing user groups in comparison to low-to-non users. Results also indicated that lower levels of frequency of parent–child communication were more likely to predict membership in the high-users group compared to the low-to-non users. Regarding parent knowledge of marijuana use, trends toward congruence and underestimation of EA marijuana use predicted membership in the high-using and moderate-decreasing groups compared to the low-to-non users. Study results indicate EAs in their early 20s may be more likely to engage in healthy decision-making regarding marijuana use in an environment that includes warm, supportive parent–child relationships where parents are aware of their EAs use without focusing on their EA's perceptions of risk. [ABSTRACT FROM AUTHOR]

Objective: To examine global disability trajectories in US military with and without traumatic brain injury (TBI) over the first decade following deployment to identify risk profiles for better intervention stratification, hopefully reducing long-term cost. Setting: Patients and participants were enrolled in combat or directly following medical evacuation at the time of injury and followed up every 6 months for 10 years. Participants: There are 4 main groups (n = 475), 2 primary and 2 exploratory: (1) combat-deployed controls without a history of blast exposure "non-blast- control" (n = 143), (2) concussive blast TBI "'blast-TBI" (n = 236) (primary), (3) combat-deployed controls with a history of blast exposure "blast-control" (n = 54), and (4) patients sustaining a combat concussion not from blast "non-blast-TBI" (n = 42) (exploratory). Design: Prospective, observational, longitudinal study. Main Measures: Combat concussion, blast exposure, and subsequent head injury exposure over the first decade post-deployment. Global disability measured by the Glasgow Outcome Scale Extended (GOSE). Results: Latent class growth analysis identified 4 main trajectories of global outcome, with service members sustaining combat concussion 37 to 49 times more likely to be in the worse disability trajectories than non-blast-controls (blast-TBI: odds ratio [OR] = 49.33; CI, 19.77-123.11; P <.001; non-blast-TBI: OR = 37.50; CI, 10.01-140.50; P <.001). Even blast-exposed-controls were 5 times more likely to be in these worse disability categories compared with non-blast-controls (OR = 5.00; CI, 1.59-15.99; P =.007). Adjustment for demographic factors and subsequent head injury exposure did not substantially alter these odds ratios. Conclusions: Very high odds of poor long-term outcome trajectory were identified for those who sustained a concussion in combat, were younger at the time of injury, had lower education, and enlisted in the Army above the risk of deployment alone. These findings help identify a risk profile that could be used to target early intervention and screen for poor long-term outcome to aid in reducing the high public health cost and enhance the long-term quality of life for these service members following deployment. [ABSTRACT FROM AUTHOR]

Abstract Background Japan, like many developed countries, now faces fiscal problems from the escalating health-care expenditures associated with an aging population. Mental health problems such as depression contribute as much to these growing demands as physical disease, and measures to prevent depression are important to controlling costs. There are few longitudinal studies examining the relation between objectively measured physical activity and depressive symptoms. Therefore, the aims of our study were to explore the patterns of change of physical activity in older Japanese adults for 5 years through the use of trajectory analysis and to examine the relation between physical activity trajectories and depressive mood states. Main body Ninety-two male and 99 female volunteers aged 65–85 years were asked to equip themselves with an electronic accelerometer with a 60-day storage capacity for at least 5 years. The parameters calculated each July for the 5 years were the average daily step count and the average daily duration of activity > 3 METs (moderate to vigorous physical activity: MVPA). Hospital Anxiety and Depression Scale (HADS) assessed corresponding mood states (HADS-A and HADS-D). Trajectories of the accelerometer data were analyzed and fifth-year HADS-D and HADS-A scores were compared among trajectory groups using an analysis of covariance (ANCOVA) that controlled for baseline scores and for baseline scores and age. Six and five distinct trajectories were identified for daily step count and for daily duration of MVPA, respectively. Using ANCOVA controlling for baseline scores, HADS-D scores differed significantly among trajectory groups classed by daily duration of MVPA (p = 0.04), and Tukey’s multiple comparison tests showed significant differences between group 2, whose pattern was stable with the middle duration of MVPA, and group 1, whose pattern was stable with the lowest duration of MVPA (p = 0.02), while the results were not significant controlling for both baseline scores and age. Conclusions Older people with less MVPA continued to do less MVPA over the 5 years of study, which may be related to a future more depressive mood. Further clinical studies will be necessary to clarify these findings.

Abstract The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in March 2011 emitted a considerable amount of radioactive materials. This study isolated radiocesium-bearing microparticles (CsMPs), a form of radioactive materials emitted from the FDNPP at the early stage of the accident, from aerosols collected hourly on filter tapes at seven monitoring stations at the greater Kanto Region, including the Tokyo metropolitan area, on 15 March 2011. The aerosols had a spherical shape ~ 1 μm in diameter with activity of less than 1 Bq of 137Cs per particle. Their physical and chemical characteristics, including radioactivity ratio 134Cs/137Cs as well as chemical composition and state, are essentially the same as previously reported CsMPs. This study demonstrated that air parcels containing CsMPs emitted from the FDNPP were widespread over the greater Kanto Region, more than 250 km away from the FDNPP, during the daytime of 15 March. Trajectory analysis indicated that these particles were emitted from the reactor No. 2 of FDNPP between 14 March evening and 15 March early morning. The information obtained on the widespread distribution of CsMPs can be useful for assessing the actual impacts of radioactive contamination from the FDNPP accident on the environment and human health.

BackgroundUnderstanding the acute kidney injury (AKI) microenvironment changes and the complex cellular interaction is essential to elucidate the mechanisms and develop new targeted therapies for AKI.MethodsWe employed unbiased single-cell RNA sequencing to systematically resolve the cellular atlas of kidney tissue samples from mice at 1, 2 and 3 days after ischemia-reperfusion AKI and healthy control. The single-cell transcriptome findings were validated using multiplex immunostaining, western blotting, and functional experiments.ResultsWe constructed a systematic single-cell transcriptome atlas covering different AKI timepoints with immune cell infiltration increasing with AKI progression. Three new proximal tubule cells (PTCs) subtypes (PTC-S1-new/PTC-S2-new/PTC-S3-new) were identified, with upregulation of injury and repair-regulated signatures such as Sox9, Vcam1, Egr1, and Klf6 while with downregulation of metabolism. PTC-S1-new exhibited pro-inflammatory and pro-fibrotic signature compared to normal PTC, and trajectory analysis revealed that proliferating PTCs were the precursor cell of PTC-S1-new, and part of PTC-S1-new cells may turn into PTC-injured and then become fibrotic. Cellular interaction analysis revealed that PTC-S1-new and PTC-injured interacted closely with infiltrating immune cells through CXCL and TNF signaling pathways. Immunostaining validated that injured PTCs expressed a high level of TNFRSF1A and Kim-1, and functional experiments revealed that the exogenous addition of TNF-α promoted kidney inflammation, dramatic injury, and specific depletion of TNFRSF1A would abrogate the injury.ConclusionsThe single-cell profiling of AKI microenvironment provides new insight for the deep understanding of molecular changes of AKI, and elucidates the mechanisms and developing new targeted therapies for AKI.

Changes in circulation patterns associated with climate change have led to an increase in the frequency and intensity of Saharan dust events (SDEs) in the Carpathian Basin. The annual number of dust events was 4.2 on average between 1979 and 2010, while in the period of 2011–2018, it has increased to 10.3. This study presents a quantitative assessment of wet deposition of dust particles to Lake Balaton (Central Europe) with mineralogical and particle size distribution measurements. In addition to a comprehensive, systematic SDE identification process, rainwater samples were collected in 2016, and the particles on the filters were characterised using a range of analytical techniques. The atmospheric transport of particulates and moisture was evaluated using HYSPLIT Lagrangian trajectory model. XRD measurements revealed that during these events, the major minerals were quartz, kaolinite and 10-Å phyllosilicates. In addition, mineralogical source markers of arid dust (palygorskite and smectite) were also identified in majority of the analysed samples. Based on the results, wet deposition fluxes of dust particles were estimated for the region. 2016 was a typical and representative year for the decade, with twelve identified dust episodes. The synoptic patterns of the events fit well with the typical meteorological character of the North African dust intrusions with enhanced atmospheric meridionality described earlier. From the twelve episodes of 2016, seven were depositional events when wet deposition could be observed at ground level. Dust material of five episodes was analysed in detail, while particles of the two February wet deposition episodes were only granulometrically characterised. General uncertainties that hinder the characterisation of mineral dust in climate models were also observed in the granulometric and dust flux data of the sampled material. The particle size of the deposited dust was coarser than the upper cut-off level of grain size of the models. In addition, the discrepancies between measured and simulated deposition values clearly demonstrated the incorrect representation of dust flux in numerical simulations (partly due to inaccurate grain size data in the models).

Outdoor physical activities can promote public health and they are largely influenced by the built environment in different urban settings. Understanding the association between outdoor physical activities and the built environment is important for promoting a high quality of life. Existing studies typically focus on one type of outdoor activity using interview-based small samples and are often lack of systematic understanding of the activities' intensity and frequency. In this study, we intend to gain deeper insight into how the built environment influences physical activities using the data extracted from individual's wearables and other open data sources for integrated analysis. Multi-linear regression with logarithm transformation is applied to perform the analysis using the data from Changsha, China. We found that built environment impacts on outdoor physical activities in Changsha are not always consistent with similar studies' results in other cities. The most effective measures to promote outdoor physical activities are the provision of good arterial and secondary road networks, community parks, among others in Changsha. The results shed light on future urban planning practices in terms of promoting public health.

Background and AimEvidence about recently proposed data-driven clusters of type 2 diabetes (T2D) is mainly about its prognostic effects and Western populations. We tested the applicability of this clustering approach among the Chinese population. We further investigated the cardiorenal risk profiles among different T2D sub-phenotypes cross-sectionally and before diabetes diagnosis.MethodsWith the use of data from the China Health and Nutrition Survey (1989–2009), 6,728 participants with available fasting blood samples and completed questionnaires in the 2009 survey were included. Glycemic statuses (normoglycemia, prediabetes, and new-onset T2D) were defined according to the 2020 American Diabetes Association criteria. Data-driven cluster analysis was conducted among new-onset T2D based on five variables: age at onset, body mass index (BMI), hemoglobin A1c, homeostasis model estimates of β-cell function, and insulin resistance. Linear regression models were used to cross-sectionally examine the differences of cardiorenal risk factors (body fat distribution, blood pressure, lipid profiles, and kidney function) between glycemic statuses. Mixed-effects models were used to explore a maximum of 20-year trajectories of cardiovascular risk factors (body fat distribution and blood pressure) before diabetes diagnosis.ResultsAmong 557 (8.3%) new-onset T2D, four sub-phenotypes were found, with 57 (10.2%) assigned to the severe insulin-resistant diabetes (SIRD), 72 (12.9%) to the severe insulin-deficient diabetes (SIDD), 167 (30.0%) to the mild obesity-related diabetes (MOD), and 261 (46.9%) to the mild age-related diabetes (MARD). People clustered within different T2D sub-phenotypes had different cardiorenal risk profiles. Three T2D sub-phenotypes (SIRD, SIDD, and MOD) had worse cardiorenal abnormalities, while the risk burden in the MARD sub-phenotype was similar to that in prediabetes. Compared with people with other T2D sub-phenotypes, people in the MOD sub-phenotype had a faster increment in BMI, waist, upper arm circumference, and triceps skinfold up to 10 years before diagnosis. Blood pressure was less distinct in different T2D sub-phenotypes; however, SIDD and MOD clusters had higher blood pressure levels before diabetes diagnosis.ConclusionsData-driven T2D sub-phenotyping is applicable in the Chinese population. Certain sub-phenotypes such as MARD only have a minor cardiorenal risk burden, and distinct cardiovascular risk development occurs long before diabetes diagnosis. Our findings can help improve early prevention and targeted treatment for diabetes.

Fibroblasts have been shown to be one of the essential players for mammary gland organization. Here, we identify two major types of mouse mammary gland fibroblasts through single-cell RNA sequencing analysis: Dpp4+ fibroblasts and Dpp4- fibroblasts. Each population exhibits unique functional characteristics as well as discrete localization in normal mouse mammary glands. Remarkably, estrogen, a crucial mediator of mammary gland organization, alters the gene expression profiles of fibroblasts in a population-specific manner, without distinct activation of estrogen receptor signaling. Further integrative analysis with the inclusion of five other publicly available datasets reveals a directional differentiation among the mammary gland fibroblast populations. Moreover, the combination with the mouse mammary epithelium atlas allows us to infer multiple potential interactions between epithelial cells and fibroblasts in mammary glands. This study provides a comprehensive view of mouse mammary gland fibroblasts at the single-cell level.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Abstract: Tapering opioids for chronic pain can be challenging for both patients and prescribers, both of whom may be unsure of what to expect in terms of pain, distress, activity interference, and withdrawal symptoms over the first few weeks and months of the taper. To better prepare clinicians to provide patient-centred tapering support, the current research used prospective longitudinal qualitative methods to capture individual-level variation in patients' experience over the first few months of a voluntary physician-guided taper. The research aimed to identify patterns in individuals' experience of tapering and explore whether patient characteristics, readiness to taper, opioid tapering self-efficacy, or psychosocial context were related to tapering trajectory. Twenty-one patients with chronic noncancer pain commencing tapering of long-term opioid therapy were recruited from a metropolitan tertiary pain clinic (n = 13) and a regional primary care practice (n = 8). Semistructured phone interviews were conducted a mean of 8 times per participant over a mean duration of 12 weeks (N = 173). Four opioid-tapering trajectories were identified, which we characterised as thriving, resilient, surviving, and distressed. High and low readiness to taper was a defining characteristic of thriving and distressed trajectories, respectively. Life adversity was a prominent theme of resilient and distressed trajectories, with supportive relationships buffering the effects of adversity for those who followed a resilient trajectory. Discussion focuses on the implications of these findings for the preparation and support of patients with chronic pain who are commencing opioid tapering. [ABSTRACT FROM AUTHOR]

Objective: To examine the hypothesis that PTSD with delayed expression in some cases occurs without subthreshold PTSD symptoms above background levels bridging the gap between the traumatic exposure(s) and the clinical diagnosis. Methods: We performed systematic searches of peer‐reviewed papers in English referenced in Pubmed, Embase, or PsycINFO and ascertained 34 prospective studies of PTSD symptom trajectories identified by latent class growth statistical modeling. Studies with delayed and low‐stable trajectories provided appropriate data for this study. We computed the difference between the delayed trajectory PTSD symptom sumscore and the low‐stable PTSD sumscore at the observed points in time after the traumatic event(s). Results: In 29 study populations, the latent class growth analyses displayed delayed trajectories, and in these, we identified 110 data points (% PTSD sumscore difference/months since traumatic exposure). The median PTSD symptom sumscore was 25% higher during the initial 6 months among individuals in the delayed trajectory compared to those in low‐stable trajectory. From this level, the difference widened and reached a plateau of 40–50% higher. The variation was large, and the baseline participation rate and loss to follow‐up were exceeding 25% in the majority of the studies. Heterogeneity of populations, measures, and analyses precluded formal meta‐analysis. Conclusion: Delayed PTSD is preceded by PTSD symptoms during the first year in most cases. Still, few individuals may experience an asymptomatic delay. The results underpin the rationale for monitoring PTSD symptoms and may inform forensic assessments in that delayed PTSD without symptoms bridging the traumatic event is rare. [ABSTRACT FROM AUTHOR]

The frequency of forest fires has recently increased. Combustion aerosol can enter the stratosphere, which can have noticeable consequences for the climate. In this work, we examine the transport of combustion aerosol into the stratosphere in summer 2019, when numerous wildfires occurred in Siberia, Canada, and Alaska. According to lidar observations at the stratospheric lidar station of the V.E. Zuev Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences, in Tomsk in August 2019, layers of aerosol scattering were pronounced in the lower stratosphere. The analysis of back air mass trajectories calculated on the basis of satellite radiometric information about fires in the Northern Hemisphere and CALIPSO lidar data allows a conclusion that the aerosol layers observed result from transfer of the combustion products of Siberian wildfires into the stratosphere. [ABSTRACT FROM AUTHOR]

Objectives: To elaborately decipher the mouse and human bladders at single‐cell levels. Materials and Methods: We collected more than 50,000 cells from multiple datasets and created, up to date, the largest integrated bladder datasets. Pseudotime trajectory of urothelium and interstitial cells, as well as dynamic cell‐cell interactions, was investigated. Biological activity scores and different roles of signaling pathways between certain cell clusters were also identified. Results: The glucose score was significantly high in most urothelial cells, while the score of H3 acetylation was roughly equally distributed across all cell types. Several genes via a pseudotime pattern in mouse (Car3, Dkk2, Tnc, etc.) and human (FBLN1, S100A10, etc.) were discovered. S100A6, TMSB4X, and typical uroplakin genes seemed as shared pseudotime genes for urothelial cells in both human and mouse datasets. In combinational mouse (n = 16,688) and human (n = 22,080) bladders, we verified 1,330 and 1,449 interactive ligand‐receptor pairs, respectively. The distinct incoming and outgoing signaling was significantly associated with specific cell types. Collagen was the strongest signal from fibroblasts to urothelial basal cells in mouse, while laminin pathway for urothelial basal cells to smooth muscle cells (SMCs) in human. Fibronectin 1 pathway was intensely sent by myofibroblasts, received by urothelial cells, and almost exclusively mediated by SMCs in mouse bladder. Interestingly, the cell cluster of SMCs 2 was the dominant sender and mediator for Notch signaling in the human bladder, while SMCs 1 was not. The expression of integrin superfamily (the most common communicative pairs) was depicted, and their co‐expression patterns were located in certain cell types (eg, Itgb1 and Itgb4 in mouse and human basal cells). Conclusions: This study provides a complete interpretation of the normal bladder at single‐cell levels, offering an in‐depth resource and foundation for future research. [ABSTRACT FROM AUTHOR]

The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between two trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering similar subtrajectories within a single trajectory or across multiple trajectories. In this paper, we adopt DFD as the similarity measure, and study two representative trajectory analysis problems, namely, motif discovery and frequent pattern discovery. Due to the time complexity of DFD, these tasks are computationally challenging. We address that challenge with a suite of novel lower bound functions and a grouping-based solution. Our techniques apply directly when the analysis tasks are defined within the same or across multiple trajectories. An extensive empirical study on real trajectory datasets reveals that our approaches are 3 orders of magnitude faster than baseline solutions. [ABSTRACT FROM AUTHOR]

Background: Orthostatic hypotension (OH) in Parkinson's disease (PD) can lead to falls, impair quality of life, and increase mortality. A trajectory analysis of OH could be useful to predict and prevent the hypotension incidence early. Methods: The longitudinal data of 660 patients with PD with disease duration up to 12 years were extracted from an integrated PD database. We used latent class mixed modeling (LCMM) to identify patient subgroups, demonstrating trajectories of changes in orthostatic blood pressure (BP) over time. The optimal number of subgroups was selected by several criteria including the Bayesian Information Criterion. Baseline information comparison between groups and backward stepwise logistic regression were conducted to define the distinguishing characteristics of these subgroups and to investigate the predictors for BP trajectory. Results: We identified three trajectories for each orthostatic change of systolic blood pressure (ΔSBP), namely, Class 1 (i.e., the increasing class) consisted of 18 participants with low ΔSBP that increased continuously during the follow-up; Class 2 (i.e., the low-stable class) consisted of 610 participants with low ΔSBP that remained low throughout the follow-up; and Class 3 (i.e., the high-stable class) consisted of 32 participants with high ΔSBP at baseline that was relatively stable throughout the follow-up. Several parameters differed among subgroups, but only male sex [odds ratio (OR) = 4.687, 95% confidence interval (CI) = 1.024–21.459], lower supine diastolic blood pressure (DBP) (OR = 0.934, 95% CI = 0.876–0.996), and lower level of total protein at baseline (OR = 0.812, 95% CI = 0.700–0.941) were significant predictors of an increasing ΔSBP trajectory. Conclusion: This study provides new information on the longitudinal development of ΔSBP in patients with PD with distinct trajectories of rapidly increasing, low-stable, and high-stable class. The parameters such as male sex, lower supine DBP, and lower total proteins help to identify the rapidly increasing class. [ABSTRACT FROM AUTHOR]

High levels of early emotionality (of either negative or positive valence) are hypothesized to be important precursors to early psychopathology, with attention-deficit/hyperactivity disorder (ADHD) a prime early target. The positive and negative affect domains are prime examples of Research Domain Criteria (RDoC) concepts that may enrich a multilevel mechanistic map of psychopathology risk. Utilizing both variable-centered and person-centered approaches, the current study examined whether levels and trajectories of infant negative and positive emotionality, considered either in isolation or together, predicted children's ADHD symptoms at 4 to 8 years of age. In variable-centered analyses, higher levels of infant negative affect (at as early as 3 months of age) were associated with childhood ADHD symptoms. Findings for positive affect failed to reach statistical threshold. Results from person-centered trajectory analyses suggest that additional information is gained by simultaneously considering the trajectories of positive and negative emotionality. Specifically, only when exhibiting moderate, stable or low levels of positive affect did negative affect and its trajectory relate to child ADHD symptoms. These findings add to a growing literature that suggests that infant negative emotionality is a promising early life marker of future ADHD risk and suggest secondarily that moderation by positive affectivity warrants more consideration. [ABSTRACT FROM AUTHOR]

Abstract Background Decreased monocytic (m)HLA-DR expression is the most studied biomarker of sepsis-induced immunosuppression. To date, little is known about the relationship between sepsis characteristics, such as the site of infection, causative pathogen, or severity of disease, and mHLA-DR expression kinetics. Methods We evaluated mHLA-DR expression kinetics in 241 septic shock patients with different primary sites of infection and pathogens. Furthermore, we used unsupervised clustering analysis to identify mHLA-DR trajectories and evaluated their association with outcome parameters. Results No differences in mHLA-DR expression kinetics were found between groups of patients with different sites of infection (abdominal vs. respiratory, p = 0.13; abdominal vs. urinary tract, p = 0.53) and between pathogen categories (Gram-positive vs. Gram-negative, p = 0.54; Gram-positive vs. negative cultures, p = 0.84). The mHLA-DR expression kinetics differed between survivors and non-survivors (p

In this study, we utilize aerosol mass spectrometer (AMS) and multi-angle absorption photometer (MAAP) measurements to assess the most predominant source regions of various atmospheric aerosol constituents transported to Eastern Finland. The non-refractory composition data from the AMS were measured during three intensive measurements campaigns in autumn 2012, 2014 and 2016, whereas the continuous long-term measurements on equivalent black carbon were conducted in 2012–2017. According to observations, the highest concentrations of particulate organics, sulphate, ammonium and black carbon originated from western parts of Russia and Eastern Europe, whereas Central Europe showed lesser contribution. In addition, exceptionally high concentrations of sulphate and ammonium were associated with the Timan-Pechora basin located in north-western Russia. Assumingly, this phenomenon could be linked to intensive gas flaring activities taking place in the area. We also performed positive matrix factorization analysis of organic fraction measured by the AMS. The trajectory analysis revealed increased concentration fields (CF) for low-volatility oxygenated organic aerosol (LVOOA) and hydrocarbon-like organic aerosol (HOA) in the same areas as for sulphate and ammonium. Meanwhile, the CF of semi-volatile oxygenated organic aerosol (SVOOA) suggested a local origin. To summarize, our results suggest that Western Russia and Eastern Europe are the most important source regions of several long-range transported aerosol constituents for Eastern Finland. Besides influencing the air quality and aerosol chemical composition on a local scale, these regions may also play a crucial role as the pollutants are transported further north, towards the vulnerable Arctic region.

Vessel monitoring system (VMS) is an effective tool for the quantified study of fishing. As the fishing vessels equipped with VMS clients, a large amount of trajectory data has been collected, which brings a new opportunity for fishing research. According to fishery safety production regulations, fishing vessels should perform grouping operations based on actual conditions, but the group information cannot be collected by the VMS. In this paper, we propose the Fishing Vessels Relationships Discovery system (FVRD) by calculating the interaction time among fishing vessels and then using it as a weight to generate a relationship network. The experiment of the proposed FVRD on the vessel dataset of Zhejiang Province reveals that the generated fishing community is consistent with the type of operation of the fishing vessels, which means the proposed method is effective. The experiment also indicates that the fishing vessel relationship network has the characteristics of small-world and scale-free that is similar to the human social network, Moreover, FVRD shows that 86.78% of vessels share the collaboration relationships over one week, 10.72% of vessels are in the long-term cooperation, confirming the regulation that most fishing vessels are sailing together for fishing.

A hotspot is an axis-aligned square of fixed side length s, where the amount of time a moving entity spends within it is maximised. An exact hotspot of a polygonal trajectory with n edges can be found with time complexity O(n2). We define a c-approximate hotspot as an axis-aligned square of side length cs, in which the presence duration of the entity is no less than that of an exact hotspot. In this paper we present an algorithm to find a (1 + ϵ)-approximate hotspot of a polygonal trajectory with time complexity O(nϕϵlognϕϵ)O\left( {{{n\phi } \over \varepsilon }\log {{n\phi } \over \varepsilon }} \right), where ϕ is the ratio of average trajectory edge length to s.

Irrigated agriculture plays a crucial role in the local economic and social development of the Tarim Basin (TB), but its sustainability is threatened by water scarcity due to the arid environment. In this study, we investigate the impact of irrigation on the atmospheric hydrological cycle in the region using the weather research and forecast model. We conduct simulations for a three month period under two scenarios: present-day and future warming. Our results show that, in the present-day scenario, 90.5% of irrigated water is transported via atmospheric hydrological processes, with precipitation and water vapor transport being the dominant components. However, in the future warming scenario, more atmospheric water (45.2%) will leave the area due to weakened wind regimes, resulting in significant water loss. Furthermore, our analysis using the HYbrid Single Particle Lagrangian Integrated Trajectory model indicates that irrigation contributes to extreme rainfall events, and the southwestern TB is a primary destination for irrigated water. Our findings highlight the urgency of addressing the sustainability of irrigated agriculture and local water resources in the face of impending global warming.

The Yangtze River Delta, located in East China, is an important passage on the eastern pathway of the northward migration of fall armyworm Spodoptera frugiperda (Smith) in China, connecting China’s year-round breeding area and the Huang-Huai-Hai summer maize area. Clarifying the migration dynamics of S. frugiperda in the Yangtze River Delta is of great significance for the scientific control and prevention of S. frugiperda in the Yangtze River Delta, even in the Huang-Huai-Hai region and Northeast China. This study is based on the pest investigation data of S. frugiperda in the Yangtze River Delta from 2019 to 2021, combining it with the migration trajectory simulation approach and the synoptic weather analysis. The result showed that S. frugiperda migrated to the Yangtze River Delta in March or April at the earliest, and mainly migrated to the south of the Yangtze River in May, which can be migrated from Guangdong, Guangxi, Fujian, Jiangxi, Hunan and other places. In May and June, S. frugiperda migrated further into the Jiang–Huai region, and its source areas were mainly distributed in Jiangxi, Hunan, Zhejiang, Jiangsu, Anhui and Hubei provinces. In July, it mainly migrated to the north of Huai River, and the source areas of the insects were mainly distributed in Jiangsu, Anhui, Hunan, Hubei and Henan. From the south of the Yangtze River to the north of the Huai River, the source areas of S. frugiperda were constantly moving north. After breeding locally, S. frugiperda can not only migrate to other regions of the Yangtze River Delta, but also to its surrounding provinces of Jiangxi, Hunan, Hubei, Henan, Shandong and Hebei, and even cross the Shandong Peninsula into Northeast China such as Liaoning and Jilin provinces. Trajectory simulation showed that the emigrants of S. frugiperda from the Yangtze River Delta moved northward, westward and eastward as wind direction was quite diverse in June–August. This paper analyzes the migration dynamics of S. frugiperda in the Yangtze River Delta, which has important guiding significance for the monitoring, early warning and the development of scientific prevention and control strategies for whole country.

Climate–land interaction over West Africa has often been assessed using climate simulations, although the model-based approach suffers from the limitations of climate models for the region. In this paper, an alternative method based on the analysis of historical land cover data and standardized climatic indices is used to investigate climate–land interactions, in order to establish climatic conditions and their corresponding land cover area changes. The annual variation in land cover area changes and climatic changes are first estimated separately and then linked using various spatiotemporal scales. The results show that incidences of land cover change result from abrupt changes in climatic conditions. Interannual changes of −1.0–1.0 °C, 0–1.5 °C, and −0.5–0.5 °C, and up to ±50 mm changes in precipitation and climatic water balance, lead to 45,039–52,133 km2, 20,935–22,127 km2, and approximately 32,000 km2 changes, respectively, while a ±0.5 °C and ±20 mm change represents normal climate conditions with changes below 20,000 km2. Conversely, conversions of cropland, forest, grassland, and shrubland are the main land cover change types affecting the climate. The results offer a basis for the re-evaluation of land cover change and climate information used in regional climate models simulating land–climate interactions over West Africa.

A stay point of a moving entity is a region in which it spends a signifiant amount of time. In this paper, we identify all stay points of an entity in a certain time interval, where the entity is allowed to leave the region but it should return within a given time limit. This definition of stay points seems more natural in many applications of trajectory analysis than those that do not limit the time of entity's absence from the region. We present an O(n log n) algorithm for trajectories in R^1 with n vertices and a (1 + \epsilon)-approximation algorithm for trajectories in R^2 to identify all such stay points. Our algorithm runs in O(kn^2), where k depends on \epsilon and the ratio of the duration of the trajectory to the allowed gap time. We also present an algorithm to answer stay point queries in logarithmic time, after an O(kn log n) time preprocessing.

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the “shape” of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis.

Background: Orthostatic hypotension (OH) in Parkinson’s disease (PD) can lead to falls, impair quality of life, and increase mortality. A trajectory analysis of OH could be useful to predict and prevent the hypotension incidence early.Methods: The longitudinal data of 660 patients with PD with disease duration up to 12 years were extracted from an integrated PD database. We used latent class mixed modeling (LCMM) to identify patient subgroups, demonstrating trajectories of changes in orthostatic blood pressure (BP) over time. The optimal number of subgroups was selected by several criteria including the Bayesian Information Criterion. Baseline information comparison between groups and backward stepwise logistic regression were conducted to define the distinguishing characteristics of these subgroups and to investigate the predictors for BP trajectory.Results: We identified three trajectories for each orthostatic change of systolic blood pressure (ΔSBP), namely, Class 1 (i.e., the increasing class) consisted of 18 participants with low ΔSBP that increased continuously during the follow-up; Class 2 (i.e., the low-stable class) consisted of 610 participants with low ΔSBP that remained low throughout the follow-up; and Class 3 (i.e., the high-stable class) consisted of 32 participants with high ΔSBP at baseline that was relatively stable throughout the follow-up. Several parameters differed among subgroups, but only male sex [odds ratio (OR) = 4.687, 95% confidence interval (CI) = 1.024–21.459], lower supine diastolic blood pressure (DBP) (OR = 0.934, 95% CI = 0.876–0.996), and lower level of total protein at baseline (OR = 0.812, 95% CI = 0.700–0.941) were significant predictors of an increasing ΔSBP trajectory.Conclusion: This study provides new information on the longitudinal development of ΔSBP in patients with PD with distinct trajectories of rapidly increasing, low-stable, and high-stable class. The parameters such as male sex, lower supine DBP, and lower total proteins help to identify the rapidly increasing class.

Objective: Longitudinal cohort analysis was performed to identify the association between changes in the gamma-glutamyl transferase (GGT)/high-density lipoprotein cholesterol (HDL-C) ratio trajectory and the risk of developing diabetes mellitus., Methods: This was a retrospective cohort study. We analyzed the latent trajectory classes of changes in the GGT/HDL-C ratio by applying a latent class mixture model with healthy individuals who underwent medical checkups from January 2017 to December 2021 as the study subjects. To analyze the effect of the GGT/HDL-C ratio trajectory classes on new-onset diabetes mellitus, we then applied a multivariate Cox proportional risk regression model. Statistical analysis was performed using the R-software with the LCMM package., Results: The study cohort comprised 3410 participants. All participants were followed up for 5 years, and 95 developed diabetes (4-year incidence of 2.78%). By applying the latent class mixed model, we categorized participants into three trajectory groups: low-stability group (n = 2253), medium-increase group (n = 941), and high-increase group (n = 216). The Cox proportional risk regression model analysis showed that the hazard ratio (95% confidence interval) for the incidence of diabetes mellitus was 1.73 (1.04-2.87) in the medium-increase group and 3.96 (2.11-7.44) in the high-increase group. Moreover, we calculated the estimated model-based levels and linear slopes of the GGT/HDL-C ratios for each age group between 26 and 85 years at 10-year intervals, respectively. The results indicated the strongest correlation between the GGT/HDL-C ratio slope and diabetes in the 46-55 year age group, with an odds ratio of 1.51 (1.25-1.83)., Conclusion: A large increase in the GGT/HDL-C ratio was highly associated with the risk of developing diabetes mellitus. This result suggests that vigilance for changes in the GGT/HDL-C ratio trajectory during community health screening can help identify potential patients with diabetes, enabling early intervention and treatment., Competing Interests: All authors declare that they have no conflicts of interest in this work., (© 2024 Liang and Yang.)

Background: The binary diagnosis of Metabolic Syndrome(MetS) fails to accurately evaluate its severity, and the association between MetS severity and frailty progression remains inadequately elucidated. This study aims to clarify the relationship between the severity of MetS and the progression of frailty among the middle-aged and elderly population in China., Method: Participants from the 2011-2018 China Health and Retirement Longitudinal Study(CHARLS) were included for a longitudinal analysis. The study employs a frailty index(FI) based on 32 health deficits to diagnose frailty and to assess FI trajectories. An age-sex-ethnicity-specific MetS scoring model (MetS score) was used to assess metabolic syndrome severity in Chinese adults. The Cumulative MetS score from 2012 to 2015 was calculated using the formula: (MetS score in wave 1 + MetS score in wave 3) / 2 × time(2015 - 2012). The association between MetS score, Cumulative MetS score, and the risk and trajectory of frailty were evaluated using Cox regression/logistic regression, and linear mixed models. Restricted Cubic Splines(RCS) models were utilized to detect potential non-linear associations., Results: A higher MetS score was significantly associated with an increased risk of frailty(HR per 1 SD increase = 1.205; 95%CI: 1.14 to 1.273) and an accelerated FI trajectory(β per 1 SD increase = 0.113 per year; 95%CI: 0.075 to 0.15 per year). Evaluating changes in MetS score using a Cumulative MetS score indicated that each 1 SD increase in the Cumulative MetS score increased the risk of frailty by 22.2%(OR = 1.222; 95%CI: 1.133 to 1.319) and accelerated the rate of increase in FI(β = 0.098 per year; 95%CI: 0.058 to 0.138 per year). RCS model results demonstrated a dose-response curve relationship between MetS score and Cumulative MetS score with frailty risk. Stratified analysis showed consistency across subgroups. The interaction results indicate that in males and individuals under aged 60, MetS score may accelerate the increase in FI, a finding consistent across both models., Conclusions: Our findings underscore the positive correlation between the severity of MetS and frailty progression in the middle-aged and elderly, highlighting the urgent need for early identification of MetS and targeted interventions to reduce the risk of frailty., (© 2024. The Author(s).)

Background and Objective: TP53 loss-of-function (TP53LOF) mutations might be a driver of poor prognosis and chemoresistance in both human papillomavirus (HPV)-independent (HPV-) and HPV-associated (HPV+) penile squamous cell carcinoma (PSCC). Here, we aim to describe transcriptomic differences in the PSCC microenvironment stratified by TP53LOF and HPV status., Methods: We used single-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing to obtain a comprehensive atlas of the cellular architecture of PSCC. TP53LOF and HPV status were determined by targeted next-generation sequencing and sequencing HPV-DNA reads. Six HPV+ TP53 wild type (WT), six HPV- TP53WT, and four TP53LOF PSCC samples and six controls were included. Immunohistochemistry and hematoxylin-eosin confirmed the morphological context of the observed signatures. Prognostic differences between patient groups were validated in 541 PSCC patients using Kaplan-Meier survival estimates., Key Findings and Limitations: Patients with aberrant p53 staining fare much worse than patients with either HPV- or HPV+ tumors and WT p53 expression. Using scRNA-seq, we revealed 65 cell subtypes within 83 682 cells. TP53LOF tumors exhibit a partial epithelial-to-mesenchymal transition, immune-excluded, angiogenic, and morphologically invasive environment, underlying their aggressive phenotype. HPV- TP53WT tumors show stemness and immune exhaustion. HPV+ TP53WT tumors mirror normal epithelial maturation with upregulation of antibody-drug-conjugate targets and activation of innate immunity. Inherent to the scRNA-seq analysis, low sample size is a limitation and validation of signatures in large PSCC cohorts is needed., Conclusions and Clinical Implications: This first scRNA-seq atlas offers unprecedented in-depth insights into PSCC biology underlying prognostic differences based on TP53 and HPV status. Our findings provide clues for testing novel biomarker-driven therapies in PSCC., Patient Summary: Here, we analyzed tissues of penile cancer at the level of individual cells, which helps us understand why patients who harbor a deactivating mutation in the TP53 gene do much worse than patients lacking such a mutation. Such an analysis may help us tailor future therapies based on TP53 gene mutations and human papillomavirus status of these tumors., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)