The Non-linear Dynamics of Meaning-Processing in Social Systems

Social order cannot be considered as a stable phenomenon because it contains an order of reproduced expectations. When the expectations operate upon one another, they generate a non-linear dynamics that processes meaning. Specific meaning can be stabilized, for example, in social institutions, but all meaning arises from a horizon of possible meanings. Using Luhmann's (1984) social systems theory and Rosen's (1985) theory of anticipatory systems, I submit equations for modeling the processing of meaning in inter-human communication. First, a self-referential system can use a model of itself for the anticipation. Under the condition of functional differentiation, the social system can be expected to entertain a set of models; each model can also contain a model of the other models. Two anticipatory mechanisms are then possible: one transversal between the models, and a longitudinal one providing the modeled systems with meaning from the perspective of hindsight. A system containing two anticipatory mechanisms can become hyper-incursive. Without making decisions, however, a hyper-incursive system would be overloaded with uncertainty. Under this pressure, informed decisions tend to replace the "natural preferences" of agents and an order of cultural expectations can increasingly be shaped

Simple system using natural mineral water for high-throughput phenotyping of Arabidopsis thaliana seedlings in liquid culture

Background: Phenotyping for plant stress tolerance is an essential component of many research projects. Because screening of high numbers of plants and multiple conditions remains technically challenging and costly, there is a need for simple methods to carry out large-scale phenotyping in the laboratory.Methods: We developed a method for phenotyping the germination and seedling growth of Arabidopsis (Arabidopsis thaliana) Col-0 in liquid culture. Culture was performed under rotary shaking in multiwell plates, using Evian natural mineral water as a medium. Nondestructive and accurate quantification of green pixels by digital image analysis allowed monitoring of growth. Results: The composition of the water prevented excessive root elongation growth that would otherwise lead to clumping of seedlings observed when classic nutrient-rich medium or deionized water is used. There was no need to maintain the cultures under aseptic conditions, and seedlings, which are photosynthetic, remained healthy for several weeks. Several proof-of-concept experiments demonstrated the usefulness of the approach for environmental stress phenotyping. Conclusion: The system described here is easy to set up, cost-effective, and enables a single researcher to screen large numbers of lines under various conditions. The simplicity of the method clearly makes it amenable to high-throughput phenotyping using robotics

Association between Post-Hospital Clinic and Telephone Follow-up Provider Visits with 30-Day Readmission Risk in an Integrated Health System

BACKGROUND: Follow-up visits with clinic providers after hospital discharge may not be feasible for some patients due to functional limitations, transportation challenges, need for physical distancing, or fear of exposure especially during the current COVID-19 pandemic. METHODS: The aim of the study was to determine the effects of post-hospital clinic (POSH) and telephone (TPOSH) follow-up provider visits versus no visit on 30-day readmission. We used a retrospective cohort design based on data from 1/1/2017 to 12/31/2019 on adult patients (n = 213,513) discharged home from 15 Kaiser Permanente Southern California hospitals. Completion of POSH or TPOSH provider visits within 7 days of discharge was the exposure and all-cause 30-day inpatient and observation stay readmission was the primary outcome. We used matching weights to balance the groups and Fine-Gray subdistribution hazard model to assess for readmission risk. RESULTS: Unweighted all-cause 30-day readmission rate was highest for patients who completed a TPOSH (17.3%) followed by no visit (14.2%), non-POSH (evaluation and management visits that were not focused on the hospitalization: 13.6%) and POSH (12.6%) visits. The matching weighted models showed that the effects of POSH and TPOSH visits varied across patient subgroups. For high risk (LACE 11+) medicine patients, both POSH (HR: 0.77, 95% CI: 0.71, 0.85, P \u3c .001) and TPOSH (HR: 0.91, 95% CI: 0.83, 0.99, P = .03) were associated with 23 and 9% lower risk of 30-day readmission, respectively, compared to no visit. For medium to low risk medicine patients (LACE\u3c 11) and all surgical patients regardless of LACE score or age, there were no significant associations for either visit type with risk of 30-day readmission. CONCLUSIONS: Post-hospital telephone follow-up provider visits had only modest effects on 30-day readmission in high-risk medicine patients compared to clinic visits. It remains to be determined if greater use and comfort with virtual visits by providers and patients as a result of the pandemic might improve the effectiveness of these encounters

Associations of physical inactivity and COVID-19 outcomes among subgroups

Introduction Physical activity before COVID-19 infection is associated with less severe outcomes. The study determined whether a dose‒response association was observed and whether the associations were consistent across demographic subgroups and chronic conditions. Methods A retrospective cohort study of Kaiser Permanente Southern California adult patients who had a positive COVID-19 diagnosis between January 1, 2020 and May 31, 2021 was created. The exposure was the median of at least 3 physical activity self-reports before diagnosis. Patients were categorized as follows: always inactive, all assessments at 10 minutes/week or less; mostly inactive, median of 0–60 minutes per week; some activity, median of 60–150 minutes per week; consistently active, median>150 minutes per week; and always active, all assessments>150 minutes per week. Outcomes were hospitalization, deterioration event, or death 90 days after a COVID-19 diagnosis. Data were analyzed in 2022. Results Of 194,191 adults with COVID-19 infection, 6.3% were hospitalized, 3.1% experienced a deterioration event, and 2.8% died within 90 days. Dose‒response effects were strong; for example, patients in the some activity category had higher odds of hospitalization (OR=1.43; 95% CI=1.26, 1.63), deterioration (OR=1.83; 95% CI=1.49, 2.25), and death (OR=1.92; 95% CI=1.48, 2.49) than those in the always active category. Results were generally consistent across sex, race and ethnicity, age, and BMI categories and for patients with cardiovascular disease or hypertension. Conclusions There were protective associations of physical activity for adverse COVID-19 outcomes across demographic and clinical characteristics. Public health leaders should add physical activity to pandemic control strategies

Communications Biophysics

Contains reports on five research projects.National Science Foundation (Grant G-16526)National Institutes of Health (Grant MH-04737-02

Neuroharmony: a new tool for harmonizing volumetric MRI data from unseen scanners

We present Neuroharmony, a harmonization tool for images from unseen scanners. We developed Neuroharmony using a total of 15,026 sMRI images. The tool was able to reduce scanner-related bias from unseen scans. Neuroharmony represents a significant step towards imaging-based clinical tools.This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust’s Innovator Award (208519/Z/17/Z) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong (81220108013, 8122010801, 81621003, 81761128023 and 81227002). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, under the following NIH grants: 1R43NS074540, 2R44NS074540, and 1U24EB023398and previously GSA Contract No. GS-00F-0034P, Order Number HHSN268200100090U. This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Repository Without Borders (ARWiBo – www.arwibo.it). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID (https://neugrid2.eu), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer’ and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/ P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network (www.mrn.org). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581. MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ‘Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis’, TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database (http://central.xnat.org/REST/projects/NUDataSharing) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]. Part of the data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435. Jo~ao Sato is supported by Sao Paulo Research Foundation (FAPESP, Brazil) Grants 2018/04654-9 and 2018/21934-5

Der diskrete Charme der Bourgeoisie - Ein Beitrag zur Soziologie des modernen Wirtschaftsbürgertums

Entgegen der These der Auflösungserscheinungen des Bürgertums stellt der Autor die Annahme auf den Prüfstand, dass wir es nach wie vor mit gesellschaftlichen Fraktionierungen bürgerlicher Lebensweisen zu tun haben. Am Beispiel autobiographischer Schriften von deutschen Topmanagern stellt der Text ein modernes Verständnis des Wirtschaftsbürgertums vor, das organisational (durch die Karrieremechanismen der Organisation) und institutionell (im Feld der Wirtschaft) verankert ist. Die moderne Sozialformation des Wirtschaftsbürgertums ist nur noch auf der Grundlage von Organisationen denkbar. Sie lässt sich, jenseits von Klasse und Stand, als Positionselite beschreiben. Anhand der Autobiographien lässt sich die Reproduktion dieser Elite auf Basis einer engen Verknüpfung zwischen familialer Herkunft, an organisationale Karrieren gebundene Leistungsbereitschaft und hoher formaler Bildung nachzeichnen. Die Abgrenzung in der Statusreproduktion zwischen Bildungs- und Wirtschaftsbürgertum weist der Autor am jeweiligen Verhältnis zur Bildung nach; zwar können beide einen hohen Bildungsgrad in Form von Bildungspatenten nachweisen, doch im Falle des Wirtschaftsbürgertums herrscht ein instrumentelles Verhältnis zur Bildung vor. Der hohe Bildungsgrad folgt hier dem Bedürfnis, den Status mittels formaler Bildung abzusichern und damit die Gefahr der eigenen Austauschbarkeit - als Personal der Organisation - zu kompensieren. Der Text macht außerdem generationale Effekte sichtbar; insbesondere indem er darlegt, inwieweit der "moderne Manager" einerseits in der Betonung seines Status seinen Vorgängern gleicht und sich doch gleichzeitig in der Art der Unternehmensführung abgrenzt - indem er bspw. die Managementkonzepte seiner Zeit aufgreift

Upright activity and higher motor function may preserve bone mineral density within 6 months of stroke: a longitudinal study

2017-2018 > Academic research: refereed > Publication in refereed journal201807 bcrcVersion of RecordOthersAustralian Research Council (ARCFT09901086), Austin Health Medical Research Fund and La Trobe University Faculty Research Grant.PublishedC