7 Figure Hygiene

[Caterina Haggins]

Effectivehand hygiene is not only a key measure for preventing the spread of SARS-CoV-2 and for safe COVID-19 vaccination, but it also reduces the burden of health care-associated infections and the spread of antimicrobial resistance.

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Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense.

a At each moment, an ant is either inactive (IDLE state), selfgrooming (SELF) or grooming another ant (ALLO). Stochastic transitions between states (arrows) depend on a range of factors to be identified. (b) Model selection identified predictive factors that the ant experienced in the recent past, such as performed (P) and received (R) allogrooming, and encountered spore load (L), by minimizing prediction error on 5 independent replicate simulation sets for the time-resolved activity across all ant classes (F,f,C,N) and treatment combinations. The best model (green) uses factors R and L, c an individual disturbance factor (ρ) by which all treated ants (F,f,C) equally suppress their allogrooming compared to untreated ants, as well as d a sequential-choice rule with dynamic updating of spore load information (rule 5, green bar) to pick a target for grooming. This rule is favored by model selection (bars at left) over alternative rules (rules 1-4; all rules schematized at right, see Supplementary Note 1 for details). In alternative rule 1, ants pick grooming targets uniformly at random. Alternative rule 2 is a variant of the sequential-choice rule with the same parameters as its dynamic counterpart (green bar), but with loads being the initial rather than dynamically-updated current loads. Alternative rule 3 uses static probabilities for picking each ant depending on its treatment; probabilities have been optimized for best fit to data. Alternative rule 4 assumes each ant has access to global current load information to deterministically groom the ant with the currently maximal load (circle darkness reflects spore load intensity). In b-d, bars represent the mean of the 5 individual simulations (each depicted by a circle; error bar shows std), relative to the constant rates model (dashed horizontal line). e Schematic of the identified best model. Ants amplify allogrooming when recently having perceived high spore load on others, and suppress it after having received grooming. Transition to allogrooming is additionally suppressed (ρ) in all treated ants.

Ants incur a time-cost tE per encounter to estimate the spore load on a target ant (exploration) and tG to groom it (exploitation). The inferred sequential-choice rule (SEQ, partial information) outperforms the maximal rule (MAX, complete information) as the colony size grows for any nonzero tE/tG, in terms of time needed to remove 90% of pathogen.

Taken together, the observed higher spore removal of better choosers (Fig. 7a), as well as the observed higher spore removal of groups with choice (Fig. 7b), indicate that many small grooming biases in individual ant decisions accumulate to a functional benefit at the collective level. The ability of individual ants to preferentially target higher-load individuals (rather than choosing uniformly at random; Fig. 3d, Supplementary Note 1), combined with the higher grooming efficiency at higher spore loads (Type II functional response model, see above), builds up to highly efficient pathogen removal at the group level. This effect is already detectable for our experimental groups of only six ants, in line with previous reports that collective benefits can emerge already at these very small group sizes36.

In this study, we extracted individual decision-making rules underlying collective hygiene in ants. These rules are cognitively simple, scalable, and plausible, since they only require short-term memory, contact-based social feedback, and locally-accessible pathogen threat information. Despite these limitations that exert their effect at the level of individual ants, the identified rules interact synergistically and amplify into efficient pathogen removal with expected epidemiological benefits at the colony level. Below we rationalize how this synergy comes about.

For each of the spore-treated ants (F, f) we estimated the current spore load contamination on its body with a resolution of 30-s time windows in the 90-min post-treatment period (180 time windows in total) from: (i) the sum of the GFP- and RFP spore counts that remained on its body at the end of the experiment, (ii) the time it selfgroomed its body and was groomed by others, and (iii) the initial distribution of spore loads after F- and f-treatment, as quantified for the 30 F- and f-workers frozen directly after exposure. Given a mathematical model for how the number of spores on an ant could decrease with grooming (spore decay model), one can back-compute the spore load on each ant from its remaining measured spore count (i), and the sequence of grooming events (ii), to any time t during the experiment. If spore loads are back-computed to the beginning of the experiment for all F- and all f-ants, they should recover the experimentally measured distributions (iii). The last fact allowed us to select a best spore decay model and fit its parameters, such that spore loads could subsequently be imputed at any time during the experiment.

The Supplementary Information contains a detailed description of all statistical tests in the Supplementary Notes 3, as well as all ordering details in Supplementary Table 4. The source data to our analyses are provided as source data files, the code is available under GitHub with the input raw data files for the code being accessible under -explorer.ista.ac.at/record/12945.

The data generated in this study are provided as Source Data files with this paper. In addition, raw input data files for the code have been deposited in the Research Explorer database of the Institute of Science and Technology Austria under -explorer.ista.ac.at/record/12945. Source data are provided with this paper.

On GitHub, we provide a complete code for (i) the statistical analysis of our experimental data and (ii) for the inference of the models for the spore removal behavior in the groups of ants, studied in our work, and for the stochastic simulation of these models. The first can be used to generate data tables from output files of behavioral annotation software and spore measurements, and to produce the statistical analyses and generate the plots for the experimental data analysis. The latter performs three tasks: (1) Reads and statistically analyzes the experimental input files and stores the outcome of the analysis in a form of sufficient statistics; (2) Uses the output of the statistical analysis to infer a model of a specific type; and (3) Uses the inferred rates from the previous step along with the initial segment of the experimental data to initialize and run a stochastic simulation of the inferred model. The code is available under

S.C., B.C.P., K.B., and G.T. conceptualized the study. Experimental data were generated by B.C.P. and A.V.G., curated by B.C.P., and analyzed by B.C.P., K.B., and G.T. Modeling was performed by K.B. with input of G.T. Figures were created by B.C.P. and K.B. The manuscript was written by S.C., G.T., B.C.P., and K.B. and approved by all authors. Funding was obtained by S.C., K.B., and G.T.

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Abstract: The incidence of work-related musculoskeletal disorders (MSDs) among dental workers has been increasing. Many ergonomic devices and accessories have been introduced. The aim of this study was to investigate the effects of an 8-figure shoulder brace on posture-related muscle activities in dental hygiene practitioners during scaling procedures. In this study, 33 participants (age: 21.9 2.1 years, height: 162.0 6.0 cm, weight: 55.8 9.0 kg, body mass index: 21.2 2.4 kg/m2) performed the scaling procedure with and without the 8-figure shoulder brace in a randomized order. The normalized electromyography activity in the amplitude probability distribution function and joint angles (cervical, thoracic, lumbar, and shoulder joints) were simultaneously recorded during scaling. A paired t test was used to compare the differences in muscle kinematics, with the alpha level set at 0.05. The dental hygienists who wore the 8-figure shoulder brace during scaling showed thoracic and lumbar extension, improved sitting postures, and reduced shoulder joint abduction. However, we also observed an unintended increase in internal rotation. Use of the 8-figure shoulder brace could prevent work-related MSDs in lumbar and thoracic regions by reducing the effort exerted by the upper trapezius and deltoid muscles, despite the increased muscular effort of the cervical erector spinae. Keywords: dental hygiene; dental scaling; EMG activity; musculoskeletal disorder; 8-figure shoulder brace

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