P25: Use of a technology-based fall prevention program with visual feedback in the setting of early geriatric rehabilitation: a feasibility study

Introduction: The Otago Program (OP) is evidence-based and focuses on fall prevention in older people. It is known for having a long-term effect by reducing falls, increasing strength and balance. We are investigating the feasibility of a technology-based fall prevention program ( FPP), modelled after the principles of the Otago Program, in the setting of early geriatric rehabilitation ( EGR) to improve outcomes. Methods: Ongoing feasibility study in the setting of EGR. A sample of 30 patients (mobility at least by walker; Mini-Mental-Status-Test [MMST] > 17) will be recruited until June 2024 and compared with a retrospective cohort (n = 30, former EGR patients). All patients receive a supervised FPP modified according to OP using a technology-based platform called „Pixformance“. Training is conducted 3x/week for 20 minutes. „Pixformance“ is a virtual trainer and enables real-time corrections. Primary endpoint is the feasibility of the FPP in the setting of EGR. Feasibility is given when 6 trainings are carried out within 2 weeks. Secondary outcomes are: quality of life (EQ-VAS), risk of fall (Berg-Balance Scale [BBS]), mobility (Timed Up and Go Test [ TUG]), frailty status (Clinical Frailty Scale [CFS], hand grip strength [HGS], Strength, Assistance with walking, Rise from a chair, Climb stairs and Falls questionnaire [SARC-F]), anxiety and depression (Four-Item Patient Health Questionnaire [ PH-Q4]) and activity of daily function (Functional Independence Measure [ FIM]; Barthel Index [ BI]). Data are accessed at entry to EGR and after two weeks. This analysis focuses on the descriptive data of already included patients assessed at baseline. The independent t-test was applied to detect sex differences. Results: Until now, 8 patients were included (78.9 ± 5.5 years; 25 % men; MMST 25.8 ± 3.4). Main indication for EGR is the condition after femur fracture (18.2 %). At baseline, 62.5 % had a SARC-F score ≥ 4 points (sarcopenia) and 75.0 % had a CFS ≥ 4 (frail). Other baseline results were EQ-VAS 53.8 ± 7.4, BBS 26.1 ± 9.8, TUG 20.9 ± 7.0 sec, HGS 25.0 ± 6.4 kg, PH-Q4 3.4 ± 2.3, FIM 52.5 ± 10.3 and BI 71.9 ± 18.5. Sex differences were observed in heart rate, height and HGS (p ≤ 0.040). Conclusion: The prevalence of sarcopenia and frailty in patients at EGR entry is high. Long-term studies showed that high-risk patients with a history of falls aged ≥ 80 years benefi t most from OP. Especially this cohort may benefit from an additional short-term, technology-based FPP to improve EGR outcomes

Holocene vegetation dynamics, carbon deposition, sea level changes, and human impact inferred from the Lagoa da Fazenda core in the Baía de Caxiuanã region, Northern Brazil

http://dx.doi.org/10.13039/501100003593 Conselho Nacional de Desenvolvimento Cientifico e Tecnologicohttp://dx.doi.org/10.13039/501100004543 China Scholarship Counci

Transformation scenarios towards multifunctional landscapes: A multi-criteria land-use allocation model applied to Jambi Province, Indonesia

In tropical regions, shifting from forests and traditional agroforestry to intensive plantations generates conflicts between human welfare (farmers’ demands and societal needs) and environmental protection. Achieving sustainability in this transformation will inevitably involve trade-offs between multiple ecological and socioeconomic functions. To address these trade-offs, our study used a new methodological approach allowing the identification of transformation scenarios, including theoretical landscape compositions that satisfy multiple ecological functions (i.e., structural complexity, microclimatic conditions, organic carbon in plant biomass, soil organic carbon and nutrient leaching losses), and farmers needs (i.e., labor and input requirements, total income to land, and return to land and labor) while accounting for the uncertain provision of these functions and having an actual potential for adoption by farmers. We combined a robust, multi-objective optimization approach with an iterative search algorithm allowing the identification of ecological and socioeconomic functions that best explain current land-use decisions. The model then optimized the theoretical land-use composition that satisfied multiple ecological and socioeconomic functions. Between these ends, we simulated transformation scenarios reflecting the transition from current land-use composition towards a normative multifunctional optimum. These transformation scenarios involve increasing the number of optimized socioeconomic or ecological functions, leading to higher functional richness (i.e., number of functions). We applied this method to smallholder farms in the Jambi Province, Indonesia, where traditional rubber agroforestry, rubber plantations, and oil palm plantations are the main land-use systems. Given the currently practiced land-use systems, our study revealed short-term returns to land as the principal factor in explaining current land-use decisions. Fostering an alternative composition that satisfies additional socioeconomic functions would require minor changes (“low-hanging fruits”). However, satisfying even a single ecological indicator (e.g., reduction of nutrient leaching losses) would demand substantial changes in the current land-use composition (“moonshot”). This would inevitably lead to a profit decline, underscoring the need for incentives if the societal goal is to establish multifunctional agricultural landscapes. With many oil palm plantations nearing the end of their production cycles in the Jambi province, there is a unique window of opportunity to transform agricultural landscapes

Why do children from age four fail true belief tasks? A decision experiment testing competence versus performance limitation accounts

The developmental litmus test of Theory of Mind is the false belief (FB) task in which children have to represent how another agent misrepresents the world. Children typically start mastering this task around age four. The standard interpretation is that this marks the ontogenetic emergence of full-fledgedmeta-representational Theory of Mind. Recently, however, a puzzling finding has emergedthat challenges this standard interpretation: Once children master the FB task, they begin to fail true belief (TB) control tasks(analogous to FB tasks with the exception that the agent has full informational access and thus veridical beliefs).This finding threatens the validity of the standard FB tasks and thus stands in urgent need of explanation. Here, we test two prominent explanatory approaches against each other.The perceptual access reasoning account is a competence limitation account. Itassumes that children at age fourdo not yet engage in meta-representation, but only in simpler heuristics(“if an agent has perceptual access to relevant event, she knows and then acts successfully; otherwise she actsunsuccessfully”). These heuristics produce false positives: they allow childrento perform correctly on FB tasksfor the wrong reasons, but they make them fail TB tasks. In contrast, the pragmatics approachis aperformance limitation account. Itsuggests that children at age fourdo havemeta-representational Theory of Mindbut are confused by pragmatic task factors of the TB task(involving utterly trivial, academic test questions about the beliefs of an agent in the absence of the possibility of any mis-representation etc.).The current study tested competingpredictionsofboth accounts against each other. To this end,different versions of the TBtaskwere implemented that systematically varied intask features deemed relevant by the two accounts. Results from 165four-to seven-year-olds reveal that the basic TB effect (children from age fouron pass FB and fail TB tasks) could be replicated but disappeared once TB tasks were modified: children mastered both FB and TB tasks when the latter were adapted in terms of heuristic and pragmatic factors. Importantly, this pattern held in conditions inwhich the pragmatics account predictssuccess, but the perceptual access account predicts failure. Overall, the present findings thus corroborate the standard picture (children use meta-representational ToMfrom age four, at the latest)and suggest that difficulties with TB tasks merely reflect pragmatic performance factors

A Balanced Statistical Boosting Approach for GAMLSS via New Step Lengths

Component-wise gradient boosting algorithms are popular for their intrinsic variable selection and implicit regularization, which can be especially beneficial for very flexible model classes. When estimating generalized additive models for location, scale and shape (GAMLSS) by means of a component-wise gradient boosting algorithm, an important part of the estimation procedure is to determine the relative complexity of the submodels corresponding to the different distribution parameters. Existing methods either suffer from a computationally expensive tuning procedure or can be biased by structural differences in the negative gradients' sizes, which, if encountered, lead to imbalances between the different submodels. Shrunk optimal step lengths have been suggested to replace the typical small fixed step lengths for a non-cyclical boosting algorithm limited to a Gaussian response variable in order to address this issue. In this article, we propose a new adaptive step length approach that accounts for the relative size of the fitted base-learners to ensure a natural balance between the different submodels. The new balanced boosting approach thus represents a computationally efficient and easily generalizable alternative to shrunk optimal step lengths. We implemented the balanced non-cyclical boosting algorithm for a Gaussian, a negative binomial as well as a Weibull distributed response variable and demonstrate the competitive performance of the new adaptive step length approach by means of a simulation study, in the analysis of count data modeling the number of doctor's visits as well as for survival data in an oncological trial

Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19

Abstract A novel virus emerged from Wuhan, China, at the end of 2019 and quickly evolved into a pandemic, significantly impacting various industries, especially healthcare. One critical lesson from COVID-19 is the importance of understanding and predicting underlying comorbidities to better prioritize care and pharmacological therapies. Factors like age, race, and comorbidity history are crucial in determining disease mortality. While clinical data from hospitals and cohorts have led to the identification of these comorbidities, traditional approaches often lack a mechanistic understanding of the connections between them. In response, we utilized a deep learning approach to integrate COVID-19 data with data from other diseases, aiming to detect comorbidities with mechanistic insights. Our modified algorithm in the mpDisNet package, based on word-embedding deep learning techniques, incorporates miRNA expression profiles from SARS-CoV-2 infected cell lines and their target transcription factors. This approach is aligned with the emerging field of network medicine, which seeks to define diseases based on distinct pathomechanisms rather than just phenotypes. The main aim is discovery of possible unknown comorbidities by connecting the diseases by their miRNA mediated regulatory interactions. The algorithm can predict the majority of COVID-19's known comorbidities, as well as several diseases that have yet to be discovered to be comorbid with COVID-19. These potentially comorbid diseases should be investigated further to raise awareness and prevention, as well as informing the comorbidity research for the next possible outbreak

In silico analysis of alpha‐synuclein protein variants and posttranslational modifications related to Parkinson's disease

Abstract Parkinson's disease (PD) is among the most prevalent neurodegenerative disorders, affecting over 10 million people worldwide. The protein encoded by the SNCA gene, alpha‐synuclein (ASYN), is the major component of Lewy body (LB) aggregates, a histopathological hallmark of PD. Mutations and posttranslational modifications (PTMs) in ASYN are known to influence protein aggregation and LB formation, possibly playing a crucial role in PD pathogenesis. In this work, we applied computational methods to characterize the effects of missense mutations and PTMs on the structure and function of ASYN. Missense mutations in ASYN were compiled from the literature/databases and underwent a comprehensive predictive analysis. Phosphorylation and SUMOylation sites of ASYN were retrieved from databases and predicted by algorithms. ConSurf was used to estimate the evolutionary conservation of ASYN amino acids. Molecular dynamics (MD) simulations of ASYN wild‐type and variants A30G, A30P, A53T, and G51D were performed using the GROMACS package. Seventy‐seven missense mutations in ASYN were compiled. Although most mutations were not predicted to affect ASYN stability, aggregation propensity, amyloid formation, and chaperone binding, the analyzed mutations received relatively high rates of deleterious predictions and predominantly occurred at evolutionarily conserved sites within the protein. Moreover, our predictive analyses suggested that the following mutations may be possibly harmful to ASYN and, consequently, potential targets for future investigation: K6N, T22I, K34E, G36R, G36S, V37F, L38P, G41D, and K102E. The MD analyses pointed to remarkable flexibility and essential dynamics alterations at nearly all domains of the studied variants, which could lead to impaired contact between NAC and the C‐terminal domain triggering protein aggregation. These alterations may have functional implications for ASYN and provide important insight into the molecular mechanism of PD, supporting the design of future biomedical research and improvements in existing therapies for the disease.Financiadora de Estudos e Projetos https://doi.org/10.13039/50110000480

Socio-economic pandemic modelling: case of Spain

Abstract A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661Horizon 2020 http://dx.doi.org/10.13039/501100007601Max Planck Institute for Solar System Researc