Phosphorylation modification of BCL2 is involved in receptor activator of nuclear factor-κB ligand (RANKL)-induced autophagy of osteoclast precursors (OCPs) and osteoclastogenesis. As an antiapoptotic molecule, the role of BCL2 phosphorylation in osteoclastogenesis is unknown. This study aimed to explore how BCL2 phosphorylation at specific sites regulates osteoclastogenesis.We first examined the effects of RANKL on BCL2 phosphorylation at different sites (Ser70 and Ser87) in OCPs. In vivo, transgenic mice overexpressing RANKL (Tg-hRANKL mice) were used to observe the effects of RANKL on phosphorylated BCL2 at different sites in OCPs of trabecular bone. Subsequently, using site-directed mutagenesis, we observed the respective effect of BCL2 mutations at different phosphorylation sites in OCPs on osteoclastogenesis, apoptosis, autophagy and the affinity between BCL2 and Beclin1/BAX under RANKL intervention.RANKL promoted BCL2 phosphorylation at the Ser70 (S70) site, but not the Ser87 (S87) site, in OCPs. Moreover, Tg-hRANKL mice had stronger BCL2 phosphorylation capacity at S70, not S87, in the OCPs of trabecular bone than wild-type mice in the same nest. Furthermore, BCL2 mutation at S70, not S87, inhibited RANKL-induced osteoclast differentiation and bone resorption activity. In addition, BCL2 mutation at S70 promoted OCP apoptosis, while BCL2 mutation at S87 showed the opposite effect. Remarkably, the BCL2 mutation at S70, not S87, inhibited OCP autophagic activity. Furthermore, BCL2 mutation at S70 enhanced the coimmunoprecipitation of BCL2 and Beclin1, whereas BCL2 mutation at S87 enhanced the coimmunoprecipitation of BCL2 and BAX in OCPs. More importantly, OCP autophagy, osteoclast differentiation and resorption pits inhibited by BCL2 mutation at S70 could be reversed by Beclin1 upregulation with TAT-Beclin1.RANKL activates OCP autophagy through BCL2 phosphorylation at S70, thereby promoting osteoclastogenesis, which indicates that the inactivation of BCL2 at S70 in OCPs may be a therapeutic strategy for pathological bone loss.The online version contains supplementary material available at 10.1186/s10020-022-00449-w. Pathological bone loss is a group of diseases related to the skeletal system based on the imbalance between osteoblast-mediated bone formation and osteoclast-mediated bone resorption, which is characterized by decrease bone mass and damaged bone microstructure  were obtained from the Animal center of Gem Pharmatech Co., Ltd . The animals were housed in a common environment in which the room temperature was 20 ~ 30 °C and the humidity was 60 ~ 80%, and were fed a general laboratory diet.2 and 37 °C. FBS concentration added to the cell culture medium varies according to different experiments.The tibiae from 4 to  8-week-old C57BL/6 mice were flushed with alfa-Minimum Essential Medium (α-MEM) without serum. The bone marrow cells were incubated with α-MEM supplementing 10% fetal bovine serum (FBS), penicillin (100 U/mL) and streptomycin (100 mg/mL) for 24 h. Non-adherent cells were harvested as bone marrow-derived macrophages (BMMs). BMMs were induced as OCPs under the intervention of M-CSF  for 3 days as previously described  were incubated in 24-well plates and treated with M-CSF plus RANKL  for 4 days. Osteoclast differentiation level was determined by Tartrate resistant acid phosphatase (TRAP) staining using the relevant kit  according to manufacturer’s protocols. TRAP-positive cells having more than 3 nuclei were defined as mature osteoclasts. TRAP-positive cells having more than 5 nuclei were defined as large osteoclasts.Cells , and adhered to the surface for 6 h before reagent treatment. These cells were then subjected to different treatments for another 6 days. Cells were removed by ultrasound and stirring, and the resorption pits were imaged under a scanning electron microscope . Bone resorption areas were evaluated via ImageJ software (version 1.8.0).Mouse BCL2 cDNA was cloned in pUC19 plasmid. The nucleotides related to each serine (s) residue were replaced with corresponding nucleotides to cause a conserved alteration to alanine (A) using a site-directed mutagenesis Kit . Each mutation was identified by cDNA sequencing and then cloned into pCIneo mammalian expression vector . pCIneo plasmid containing each BCL2-mutating cDNA was transfected into M-CSF-pretreated OCPs using electroporation. The efficiency of site-directed mutagenesis was verified using western blot analyses.6 cells/well) with the indicated treatments were prepared from 6-well plates. Cell lysates were packed into 10% SDS-PAGE gels and transferred to polyvinylidene fluoride membranes (PVDF). Subsequently, the PVDF membranes were incubated with antibodies targeting phosphorylated BCL2 , phosphorylated BCL2 , BCL2 (1:1000) , Cathepsin K , matrix metallopeptidase 9 , TRAP (1:2000), Cleaved-caspase3 (1:5000), PARP (1:1000), LC3B (1:2000), p62 , Tubulin . Horseradish peroxidase (HRP)-linked secondary antibodies were applied as the secondary antibodies. The signals were visualized using a chemiluminescence system .The lysates from OCPs . Next, we rinsed the beads with 100 μL iced buffer, added 100 μL antibody-binding buffer to revolve the antibody and magnetic beads for 30 min, and then rinsed the beads 3 times using 200 μL buffer for 5 min each time. Cell lysates and antibody-bound magnetic beads were incubated for 1 h at room temperature and washed using 200 μL buffer for 5 min each time. 20 μL eluent was used to rinse the beads once and the supernatant was removed. The cell lysates were extracted for Co-IP with anti-BCL2 (1:100), Beclin1 (1:100), BAX (1:100) antibodies (Thermo Fisher Scientific), and then, precipitates were detected using western blotting with anti-Beclin1 (1:1000), BAX (1:1000), BCL2 (1:1000) antibodies, respectively.The formation of autolysosomes was identified by transmission electron microscopy (TEM) assays. After the indicated treatments, the preparation, fixation, dehydration, infiltration, baking, slicing and staining of samples were performed as described previously . Firstly, their genotypes were identified by PCR assays. Next, all mice were sacrificed. The tibiae were harvested, wrapped in 0.9% saline-soaked gauze and stored at − 20 °C. The experimental protocols were approved by the Animal Ethics Committee of Fujian Provincial Hospital (2016-0004).Three-dimensional (3D) reconstructions of the cancellous bones in the proximal tibia metaphysis were made using Bruker Micro-CT Skyscan 1276 system . Scan settings are as follows: voxel size 6.533481 μm, medium resolution, 55 kV, 200 mA, 1 mm Al filter and integration time 384 ms. Bone density measurements were calibrated to the manufacturer’s calcium hydroxyapatite (CaHA) phantom. Analysis was performed using the manufacturer’s evaluation software. Three-dimensional (3D) reconstruction was accomplished by NRecon (version 1.7.4.2). The parameters included Bone Mineral Density (BMD), Bone Volume/Tissue Volume (BV/TV), Bone Surface/Bone Volume (BS/BV), Trabecular Thickness (Tb.Th), Trabecular Number (Tb.N) and Trabecular Separation (Tb.Sp) .+ cells was evaluated using IPP software and calculated as the ratio of RANK+ p-BCL2+ cells to RANK+ cells.The tibiae of all mice were fixed in 4% paraformaldehyde (PFA) for 48 h and decalcified in 10% EDTA (pH 7.3) for 2 weeks at 4 °C. Then, all samples were dehydrated in graded ethanol series and embedded in paraffin (Leica). All section (5 μm thick) were stained with the H&E or TRAP staining kit . The trabecular bone area (%Tb.Ar) of the H&E-stained sections was analysed using Image-Pro Plus  software. The osteoclast number in the TRAP-stained sections were assessed via an eyepiece grid. For IF , all sections were incubated in citrate buffer  at 60 °C overnight to expose the antigens. We incubated the primary antibodies at 4 °C overnight and the secondary antibodies at room temperature for 1 h . The fluorescent intensity of p-BCL2 at the corresponding sites in RANK+ CSF1R+ cells were used for the immunofluorescence assay.Bone marrow cells were isolated from each Tg-hRANKL mouse and corresponding control mouse. 70 micron-nylon mesh was used to disperse cells into single-cell suspensions, rinsed and resuspended in 5 mL ice DMEM for 3 min. Nonspecific binding was blocked by treated cells with rat anti-mouse CD16/32 monoclonal antibody  at room temperature for 15 min. Cells were stained using PE anti-mouse RANK and CSF1R antibodies, and the cells were harvested on a MoFlo XDP flow cytometer (Beckman Coulter). The harvested RANK+ CSF1R+ cells (1 × 106/well) were seeded on 6-cm dishes and fixed using 4% PFA. After perforation, the cells were blocked using 1% bovine serum albumin (BSA) and incubated with anti-p-BCL2  or p-BCL2  antibodies (Thermo Fisher Scientific) at 4 °C overnight. Next, the cells were stained with fluorochrome-labelled secondary antibody for 30 min, and subsequently counterstained with DAPI for 10 min. Finally, the stained cells were observed and recorded using fluorescence microscopy .The enriched RANKApoptosis levels were assessed through Annexin V–FITC/PI staining and detection of apoptotic protein. After the specific treatment in the experimental design, the indicated cells were harvested, and then staining was carried out in accordance with manufacturer’s protocols. Next, the apoptosis levels were measured and quantitatively analysed using the flow cytometer . Apoptotic proteins were detected by western blotting.The mitochondrial membrane potential was detected using JC-10 assays kit . The treated OCPs  were plated in a microplate. The cells were stained with JC-10 solution according to manufacturer’s protocols. The accumulation of JC-10 dye is proportional to mitochondrial membrane potential. The cells were added with about 100 µL JC-10 dye and incubated for 20 min. The alteration in fuorescence intensity was measured using a microplate reader  at λex = 490/λem = 525 nm and λex = 540/λem = 590 nm. The mitochondrial membrane potential levels were determined by measuring the ratio of red/green fluorescence intensity and quantitatively analysed using the flow cytometer (BD Accuri C6 Plus).Data are presented as the mean ± SEM. Statistical analyses were performed using one-way ANOVA and Student’s T Test. The P value was set at 0.05. All statistical analyses were carried out by the SPSS 19.0 software.First, the effect of RANKL on two phosphorylation sites of BCL2 in OCPs was identified. As shown in Fig. We documented the differential effects of RANKL on BCL2 phosphorylation at different sites in OCPs in vitro. Next, we used Tg-hRANKL transgenic mice to observe the in vivo effect of RANKL on BCL2 phosphorylation at different sites in OCPs. Tg-hRANKL mice have been used as a typical animal model of osteoporosis due to overexpressed RANKL , it was found that the alteration of phosphorylated BCL2 at different sites in vitro was consistent with that of phosphorylated BCL2 at different sites in vivo  of trabecular bone in Tg-hRANKL mice (shown as overlaps of phosphorylated BCL2 and RANK). Compared with WT mice, Tg-hRANKL mice had more obvious overlapping of phosphorylated BCL2 at S70 and RANK . We documented that the BCL2 mutation at S70 inhibits osteoclastogenesis and promotes OCP apoptosis under RANKL intervention. Therefore, the effect of the BCL2 mutation at specific sites on RANKL-induced OCP autophagy also needs to be further clarified. As shown in Fig. The positive role of BCL2 phosphorylation at S70 in RANKL-induced OCP autophagy and the promoting role of BCL2 phosphorylation at S87 in OCP apoptosis were confirmed under RANKL intervention. Previous studies have clarified that Beclin1 and BAX were dissociated following BCL2 phosphorylation, which is involved in autophagy activation and apoptotic signal transduction, respectively (Bassik et al. The upregulation of BCL2 phosphorylation at S70 on RANKL-induced OCP autophagy and osteoclastogenesis has been clarified. Moreover, BCL2 phosphorylation at S70 led to the release of the Beclin1 molecule from the BCL2-Beclin1 complex. We combined BCL2 mutation at S70 and pharmacological overexpression of Beclin1 to further confirm the role of autophagy in osteoclastogenesis regulated by BCL2 mutation at S70. TAT-Beclin1, as a Beclin1 recombinant protein, can activate autophagy (Shoji-Kawata et al. RANKL can promote the autophagy of OCPs through BCL2 phosphorylation, which exists in RANKL-induced osteoclastogenesis (Ke et al. First, RANKL promoted BCL2 phosphorylation at the S70 site in OCPs in vitro and in vivo. However, RANKL did not affect BCL2 phosphorylation at S87. These results are similar to the previous study. Our team previously demonstrated on RAW264.7-derived OCPs that RANKL can promote BCL2 phosphorylation at the S70 site in a time-dependent and concentration-dependent manner (Ke et al. Protective autophagy can act as an antiapoptotic mechanism. Autophagy can protect cancer cells from apoptosis, which has been proven (Fitzwalter and Thorburn BCL2 phosphorylation is known to promote the dissociation of autophagic molecule Beclin1 from the BCL2-Beclin1 complex, resulting in the entry of Beclin1 into autophagic flux and subsequent autophagy activation (Pattingre et al. In the present study, the role of BCL2 phosphorylation at different sites in osteoclastogenesis and its underlying mechanism are described. According to the overall data, BCL2 phosphorylation at the S70 site mediates RANKL-induced OCP autophagy due to its positive regulation of the BCL2-Beclin1 signalling pathway, thereby resisting the apoptosis of OCPs and promoting osteoclastogenesis. This study is the first to clarify the long-standing doubts about the specific role of the BCL2 molecule and its phosphorylation modification in osteoclastogenesis, which provides more potential clues for the clinical diagnosis and treatment of pathological bone loss dominated by osteoclastic bone resorption.Additional file 1: Figure S1. Tg-hRANKL mice had less bone mass and destructive bone microstructure. Figure S2. Supplementary data on BCL2 phosphorylation in Tg-hRANKL mice. Figure S3. Bone resorptive activity inhibited by BCL2 mutation at S70 was reversed by TAT-Beclin1.


Cataglyphis velox rapidly learned aversive views, when associated with a negative outcome, a period of captivity in vegetation, triggering increases in hesitation behavior. These memories were based on the accumulation of experiences over multiple trips with each new experience regulating forager hesitancy. Foragers were also sensitive to captivity time differences, suggesting they possess some mechanism to quantify duration. Finally, we analyzed foragers' perception of risky (i.e. variable) versus stable aversive outcomes by associating two sites along the route with distinct captivity schedules, a fixed or variable duration, with the same mean across training. Foragers exhibited fewer hesitations in response to risky outcomes compared to fixed ones, indicating they perceived risky outcomes as less severe. Results align with a logarithmic relationship between captivity duration and hesitations, suggesting that aversive stimulus perception is a logarithm of its actual value. We discuss how aversive view learning could be executed within the mushroom bodies circuitry following a prediction error rule.Many ants establish foraging routes through learning views of the visual panorama. Route models have focused primarily on attractive view use, which experienced foragers orient towards to return to known sites. However, aversive views have recently been uncovered as a key component of route learning. Here,  The most well studied components of this toolkit are the path integration (PI) system7, which is useful when visual terrestrial cues are not available as well as during route formation11, and the learned visual cues of the panorama17. Many ant species rapidly learn visual landmark information to navigate while foraging and rather than learn individual landmarks, foragers acquire panoramic views around goal locations and along their foraging routes13. View learning first occurs around the nest during learning walks prior to the onset of foraging15. Foragers also acquire views while en-route as they move away from known locations17, and can rapidly learn the panorama at a new site, often after only one previous experience19. Foragers retain long term-memories of these panoramas20 and, while navigating, compare these memories to their current view to recover their goal direction21.The navigational abilities of solitarily foraging ants can be attributed to a toolkit comprised of multiple strategies26, yet these models rely principally on the forager orienting towards attractive views via view comparison. Recent work has expanded this modelling to include the learning of views that are repellent or aversive and cause foragers to turn away from views not associated with the current goal, resulting in orientation away from incorrect directions28. Interactions between these learned attractive and aversive views permit navigators to compare a single current view to their view memories to quickly decide whether to move toward or turn away from a given direction28. It has been suggested that learned views can become aversive depending on their orientation relative to the nest29, on the foraging motivational context30 or when these views are associated with aversive outcomes31. Additionally, a view memory’s valence can be altered, with previously attractive views becoming aversive when they are associated with negative outcomes. In both Cataglyphis and Melophorus desert ants, when a pit trap was added along a forager’s homeward route, resulting in foragers falling into dry vegetation, ants quickly memorized the views experienced just before this negative experience as aversive or repellent. Eventually, after a few experiences falling into this pit, foragers formed new routes detouring around it. The interplay of aversive and attractive views appears to facilitate the formation of these detours. As foragers attempt to avoid aversive views, novel views that pilot around these areas become positively reinforced, leading to the development of new routes detouring around obstacles and areas with difficult terrain31. Dense vegetation can often be hard for desert ants to move through effectively, especially when carrying food, resulting in increases to both expenditure of effort and the delay to return to the nest. After only a few trips experiencing the vegetation-filled pit, foragers began to hesitate near the pit’s edge, increasing their hesitancy to pass through the area, evidenced through increases in both scanning behavior and path meander31. Scanning behavior consists of a forager stopping forward movement and rotating their body on the spot. This behavior is associated with instances of increased navigational uncertainty: when the familiarity of the panorama decreases, the PI and panorama enter into conflict, or when the current route’s panorama is associated with failure33. Thus, the incidence of scanning is a good behavioral proxy to assess the ant’s uncertainty and here we used it, along with stopping behaviors, to quantify the strength of the aversion associated with a given location30.View memories and their importance in route following have been well modeled36. For example, when risk is generated by variability in amount, animals are often risk-averse or risk neutral, whereas when risk is generated by variability in delay, animals are typically risk-prone37. These preferences are believed to flow from animals’ perception of the world, where true stimulus strength has a logarithmic relationship with the animal’s perception40. Based on this principle, animals’ choices between risky (variable) and fixed outcomes should be predicted not by the arithmetic mean of these options but instead by their geometric means.While navigating, foraging animals must make decisions assessing risky or safe options both in the resources they collect and in their foraging routes. Across a number of animal models, individuals will sometimes behave in seemingly non-optimal or irrational ways with regards to their perception and preference for risk41. Much of the risk preference research in Hymenoptera has focused on foragers’ preference for risk solely in regards to the amount or quality of a given reward43. In honeybees and bumble bees, a variety of outcomes have been reported with foragers showing evidence of no preference, risk avoidance and risk seeking foraging choices based on factors such as colony resource levels47. In ants, risk perception and sensitivity have been explored on the colony level, focusing on how collective decision-making influences choice in the assessment of potential nesting site quality and in food reward quality. Rock ants  were shown to exhibit risk seeking behavioral choices when making collective choices between nests48. The collective decision-making of the colony has also been shown to result in the avoidance of certain irrational choice behaviors observed in individual ants, including reducing the time to choose between potential nest sites50. Recently, De Agrò and colleagues43 have shown that non-optimal risk preferences in foraging ants may stem from how they perceive stimulus strength, with perception of a given stimulus having a logarithmic relationship with the stimulus’ strength. The researchers showed that ant foragers’ perceptions of food reward quality were on a logarithmic scale. Individuals were risk averse and preferred the fixed option when choosing between food rewards with the same mean values, however this preference disappeared when they chose between two logarithmically balanced alternatives43.Non-optimal responses to risk have been shown to occur across a range of animals, even in species which typically forage by collecting food to provide energy for a group or colony such as ants and bees31 with individual foragers struggling through vegetation before returning to the homebound route. Holding foragers within a vial instead of using the pit trap allowed us to more easily control hold time durations and prevented foragers from forming routes that avoid the negative outcome. Foragers’ behaviors were recorded using a trial-by-trial approach to describe navigational learning during natural tasks51. We first studied the dynamics of view learning, as well as retention across non-reinforced trials. Second, we explored foragers’ perception of captivity duration by training foragers to associate sites along the route with two distinct fixed durations of captivity (15 s vs. 300 s). Finally, we characterized foragers’ perception of risk when sites were associated with ‘Fixed’ or ‘Risky’ outcomes with the same mean duration across training (~ 150 s). Here, the ‘Fixed’ outcome was associated with a constant period held in vegetation (150 s) while the ‘Risky’ outcome was associated with a variable time period where foragers had a 50/50 chance on each trip of being held within vegetation for either 1 s or 300 s. We found that C. velox foragers rapidly learned to associate the (previously positive) homeward route views with aversive outcomes, with as few as two prior experiences. These aversive view memories persisted over multiple trips after the outcome was removed. Foragers were able to perceive differences in outcome severity, learning more rapidly and exhibiting more overall hesitations to views associated with more severe outcomes (300 s) compared to less severe outcomes (15 s). Finally, foragers showed significantly less apprehension to travel through sites associated with risky aversive outcomes compared to a fixed outcome with the same mean, suggesting non-linear scaling of captivity duration. The observed forager hesitations at the risky site were in line with the geometric average of the captivity durations, suggesting that the perception of aversive stimulus strength is logarithmic.In the current study, we characterized foragers’ learning and memories of aversive views when these views are associated with aversive, high effort outcomes, i.e. being kept within a vegetation-filled vial for set time periods. Forcing foragers into vegetation simulates areas along the homeward route that contain dense clutter, compelling the forager to struggle through in order to reach the nest with its food piece, increasing both their time and energy expenditure. Here, we used a similar negative outcome to the previously described pit trap experimentC. velox nest located at an established field site ~ 6 km south of Seville, Spain . C. velox inhabit visually cluttered semi-arid environments, densely covered in grass tussocks, scattered bushes and with distant stands of trees and man-made structures. Foraging C. velox travel between sites alone and do not rely on pheromone trails while foraging. While navigating, these foragers rely heavily on these visual cues to return to the nest and known food sites, creating stable routes between locations11. There are no institutional or governmental regulations (in Canada or Spain) for research in invertebrates, manipulations were non-invasive and all individuals were returned to the nest after testing.Testing was conducted in June and July 2019 on a single A plastic square-shaped feeder (15 cm × 15 cm × 8 cm) was sunk into the ground 12 m from the nest entrance and was continuously stocked with crushed cookie pieces . The smooth walls of the feeder prevented foragers that dropped in from exiting without being lifted out by the researcher. All vegetation in a 200 cm wide band from the nest to the feeder and in a 100 cm radius around both sites was removed using an edge trimmer. To entice foragers to collect food only from the feeder, an arena was erected using a 10 cm high smooth plastic barrier, enclosing the nest and feeder site and restricting the nest to forage only within the arena. This arena was 150 cm in width and extended in a 75 cm radius semi-circle around both feeder and nest .31. In the current study, we collected two types of hesitation behaviour, scans and stops. Scans were defined as the ceasing of forager movement that was accompanied by the forager clearly turning on the spot, rotating in place. In contrast, stops were cataloged as the ceasing of forward movement with no accompanying rotation.We initially tested foragers’ learning of aversive view memories by collecting inbound foragers as they reached Site 2 Figs. a, 2. WheDuring training, each forager n = 16) was exposed to ten consecutive training trips, followed by five tests Fig. . At the 6 was expAfter foragers completed their ten training trips, they began the testing phase and were no longer collected when passing through the site. As this testing was conducted to determine the persistence of the view memory’s current valance after the aversive outcome was removed and to characterize the number of experiences necessary to return back to pre-training baseline, we determined prior to the onset of the experiment that foragers would be tested for a total of five trips after training. On the forager’s eleventh trip to the feeder Test 1), individuals were released from the feeder and allowed to travel the full inbound route to the nest without being collected and held (i.e. in the absence of the aversive outcome). As in training, foragers were recorded at Site 2 beginning just before they entered the grid. Given there was no collection during testing trips, recording ceased once foragers reached 25 cm past the grid at the end of the collection area. All of these foragers reached the nest and freely entered. On their next foraging trip, foragers were tested twice. These inbound foragers were recorded and allowed to pass through Site 2 (Test 2), identically to the previous test, until they reached the nest entrance. As foragers reached within 20 cm of the nest entrance with their path integrator now near zero and directionally uninformative , each forager was picked up using an empty vial and returned to the foraging route 10 m from the nest  were exposed to ten consecutive training trips where Site 1 was associated with a hold period of 300 s while Site 2 was associated with a hold period of 15 s , Site 1 was associated with a fixed aversive outcome, being held in vegetation for a period of 150 s on every training trip, while Site 2 was associated with a variable outcome, with a 50/50 chance of being held for a longer (300 s) or shorter (1 s) period. The arithmetic mean values of the Fixed and Risky site hold durations over training was chosen to be equal (~ 150 s), yet these training schedules had very different geometric means . Each forager’s identity was coded prior to data collection so that the scoring was blind to the ant’s hold time condition. Paths were digitized by marking the ant’s mesosoma at 200 ms intervals beginning when the forager entered the grid and ceasing once foragers were collected during training or when they reached 25 cm past the grid edge during testing. Aversive view learning and memory were assessed by recording the number of hesitations exhibited by the forager leading up to collection. Two types of hesitation behavior were observed during testing, scans and stops and these were confirmed during video playback. We classified a ‘stop’ as a ceasing of forward movement with the ant remaining stationary until forward movement resumed. In contrast, ‘scans’ were classified as the ceasing of forward movement that is accompanied by the ant pirouetting, or rotating in place before resuming forward movement. Both of these behaviors were collected by the experimenter during the experiment and both positioning and behavior type was confirmed using video analysis. The quantity and position of both behaviors were recorded along the forager’s digitized paths. For statistical analysis, scan and stop behaviors were combined to create a total hesitation count for each training and test trip.Site Number and Hold Condition were analyzed as fixed effects. Conover’s post hoc pairwise comparisons of forager hesitations during the baseline during Trip 1 and after training/testing were conducted using p values corrected with the Bonferroni method for multiple comparisons . All p values are presented post this correction (p × comparison #). Within individual comparisons between hold regimes in the Hold Condition (15 s vs. 300 s) and the Risk Sensitivity (Fixed vs. Risky) tests were compared using Wilcoxon Signed Rank Tests with p-values corrected with the Bonferroni method.In the Aversive Learning tests, we compared hesitation numbers (Stops + Scans) across training/testing trips using a General Linear Model (GLM) for count data (Poisson loglinear) with individuals as a random effect. In the Hold Duration and Risk Perception tests, where foragers were collected for distinct hold periods at both Site 1 and Site 2, both To further characterize the change in hesitation numbers at the Risky site, we calculated the change in hesitation (current hesitations minus hesitations on previous trip) number based on the outcome of the previous trip (held for 1 s or 300 s). For each individual forager, the mean hesitation change (excluding Trip 1) after a 300 s hold time was compared to the mean hesitation change after a 1 s hold time using a Wilcoxon Signed Rank Test.U tests.For between experiment comparisons, we chose to focus on forager hesitation numbers during training Trip 10 as, up to this point, the training schedule for each individual forager was consistent across all experiments. Across testing, we compared the Fixed and Risky site hesitation numbers to those of the 15 s site and 300 s hold conditions using Mann–Whitney In a final analysis exploring why foragers in the Risky condition exhibited low hesitation numbers, we characterized the hesitation response by calculating a logarithmic curve of predicted hesitation numbers based on hesitations with captivity duration in abscise and hesitation number in ordinate. This curve of expected hesitations was calibrated using the hesitation numbers observed on Trip 10 in the 15 s hold condition in the Hold Duration experiment, the 150 s (Fixed) condition in the Risk Sensitivity experiment, and the 300 s conditions in the Aversive Views and Hold Duration experiments. We then compared the observed hesitations during Trip 10 of the Risky condition in the Risk Sensitivity experiment (μ = 3.33) to the predicted hesitations of the curve based on the Risky condition’s arithmetic mean hold time (150.5 s) and geometric mean hold time (17.32 s) using one sample T-tests to determine which hypothesis was favored.S.E. = 0.0 ± 0.0; Figs. Trip Number had a significant effect on foragers’ hesitation numbers  with repeated exposures to 300 s captivity resulting in increased hesitations and this pattern increased as training progressed. Post hoc comparisons showed that hesitations did not significantly increase from the baseline on training trips 2–4  and continuing through the rest of training (Trip 6–10), hesitations were significantly higher than foragers’ baseline hesitation counts observed on Trip 1 (p < 0.001).Foragers traveling through Site 2 at the onset of training (Trip 1) showed no signs of hesitation leading up to the collection site , as well as two further trips after the aversive outcome was removed; Test 2 , and Test 3 ZV  and continuing during Test 5 , hesitation counts returned to baseline and were not significantly different from Trip 1, suggesting that after three exposures to the site without the negative outcome, foragers’ level of aversion to the site had returned to pre-training levels. There was no significant difference between hesitation numbers during the last training trip, Trip 10 and Test 1  as well as between Test 2 and Test 3 ZV (p = 0.87). Overall, these results suggest foragers exhibit evidence of aversive view learning after four prior experiences and these hesitations increased throughout training, peaking at Test 1 (μ ± S.E. = 13.8 ± 2.9). Evidence of aversive view memory retention persisted for two trips after the aversive outcome was removed (Test 2 and Test 3 ZV) before hesitations returned to baseline , post hoc comparisons revealed that hesitation numbers remained significantly above baseline (Trip 1) during Test 1  with repeated exposures to captivity resulting in increased hesitations and this pattern increased as training progressed with the two distinct hold times of 15 s and 300 s having a significant effect on these increases in hesitation numbers. Additionally, there was a significant interaction between Trip Number and Hold Condition .Foragers at the onset of training (Trip 1) exhibited few pre-training hesitations leading up to both collection sites  had no significant effect on forager hesitation numbers  or significant interaction with either Hold Condition  or Trip Number , indicating that there was no difference between the sites or effect of the order foragers were exposed to the different hold times on aversive view learning.Finally, we found no difference in hesitation behaviour with regards to either the condition or training trip when hold times at Site 1 and Site 2 were mirrored. T = 3.11; p = 0.022). However, during Trip 8 the Bonferroni adjusted p-value of the baseline hesitation comparison , fell just above (p = 0.05) the significance threshold. Hesitation counts were again significantly above baseline for the final two training trips  as well as the zero vector Test . In contrast at the 300 s hold site, post hoc comparisons showed that foragers learned the association after only two exposures, with hesitations significantly above Trip 1’s baseline beginning on Trip 3 . This significant increase in hesitations persisted through the rest of training on Trips 4–10 as well as the zero vector Test (p < 0.001). Comparisons between the final training trip, Trip 10, and the zero vector Test showed no significant difference in hesitations at either the 15 s site  or 300 s site .At the 15 s hold associated site, foragers took six prior exposures to the outcome before a significant increase in hesitation behaviour was observed. Post hoc comparisons with the pre-training baseline showed that hesitations did not significantly increase on training Trips 2–6 (p > 0.05). Increased hesitation behaviour was first observed on training Trip 7 (Comparisons of forager hesitation numbers between the 15 s and 300 s sites showed that before training (Trip 1) foragers showed no significant differences between Site 1 and Site 2 . Foragers also showed no significant increase in their hesitations at the 300 s site during Trip 2 (p = 0.23). Beginning on Trip 3 (p = 0.002), and continuing for the rest of training (Trips 4–10), foragers exhibited significantly higher hesitations at the 300 s hold site compared to the 15 s hold site (p < 0.005) and this difference persisted during the zero vector Test (p < 0.001). Foragers were able to perceive differences in outcome severity between two hold times, as foragers learned the association at the 300 s hold site faster than the 15 s site (Trip 3 vs. Trip 7) and showed higher hesitation counts associated with the more severe outcome associated site.S.E. = 0.60 ± 0.16; Risky Trip 1, μ ± S.E. = 0.69 ± 0.18; Fig. Trip Number and Hold Condition (classified as Fixed or Risky) had a significant effect on hesitations .As in previous conditions, foragers at the onset of training (Trip 1) in both the Fixed and Risky conditions exhibited few hesitations leading up to the collection sites  had no significant effect on forager hesitation numbers  or significant interaction with Hold Condition  or Trip Number . This indicates that there was no difference between the sites or effect of the order foragers were exposed to the different hold times on aversive view learning.Lastly, we found no difference in hesitation behaviour with regards to either the condition or training trip when hold times at Site 1 and Site 2 were mirrored. At the Fixed site, post hoc comparisons showed that hesitations did not significantly increase from the baseline on training Trips 2–6 , foragers showed no significant differences between Site 1 and Site 2 . During training, foragers also showed no significant difference in their hesitations between sites during Trip 2  and this persisted through Trip 6 . Beginning on Trip 7  and continuing through the rest of training (Trips 8–20), foragers exhibited significantly higher hesitations at the Fixed hold site compared to the Risky hold site (p < 0.05) and this difference was also present during the zero vector Test (p < 0.001).S.E. =  + 1.19 ± 0.44) while experiencing the less aversive 1 s outcome resulted in hesitations being regulated downward (mean hesitation change ± S.E. =  − 0.53 ± 0.29) and these changes based on the last experience were significant .During training at the Risky site, changes in hesitation numbers suggest that the effect of training was continuously regulating hesitation behavior up and down based upon differences in the expected outcome and forager’s experience on each trip Fig. a. After . In contrast, forager hesitations in the Risky condition, (mean hold time = 150.5 s) were not significantly higher from the hesitation numbers of foragers in the 15 s condition of the Hold Duration tests  despite the order of magnitude difference in mean hold time  showed no significant differences  and these data were combined for future comparisons. Hesitation numbers during the Fixed 150 s condition were significantly lower than the 300 s conditions  and significantly higher than the 15 s condition T = 0.377; p = 0.712; Fig. T = 4.67; p < 0.001).Finally, observed hesitations during Trip 10 of the Risky condition (μ = 3.33) were compared along the logarithmic curve of predicted hesitation numbers, calibrated by the other conditions (y = 2.182 × ln(x) − 2.465; R = 0.9293) at both its arithmetic (150.5 s) and geometric (17.32 s) mean hold times. At Trip 10, observed hesitations in the Risky condition were not significantly different from the predicted hesitation number (3.75) based upon the geometric mean hold time of 17.32 s  and induce forward movement31, our results show rapid acquisition of aversive view memories at specific spatial locations associated with negative outcomes, resulting in increased hesitations leading up to these sites. Views that previously had a positive association, formed during the initial formation of the homeward route, subsequently become negative when associated with the experience of struggling within the vegetation. Unlike previous work, here foragers were unable to form new positively reinforced routes detouring around these negative outcomes as all available homeward routes resulted in collection. Once the negative outcome was removed, foragers were shown to take two exposures to the route to re-learn its positive association, reinforced through re-entering the nest (during Test 1 and Test 3 ZV), and reduce hesitations to baseline. As Test 2 did not result in the forager successfully entering the nest (due to collection for Test 3 ZV testing), it is likely the observed lack of a decrease in hesitations between Test 2 and Test 3  than the 15 s site (μ = 3.8) during the final training trip (Trip 10). In contrast, foragers trained in the Risk Perception tests at the fixed 150 s site, exhibited a hesitation response mid-point between these two extremes (μ = 7.3). These differences suggest foragers were able to perceive time disparities spent struggling within the vegetation and recalled this outcome severity on subsequent foraging trips, leading to distinct levels of aversion behavior expressed at each site.Risk variance did not affect how quickly foragers learned the negative association, but had a significant effect on the degree of hesitation that developed. Over the final five training trips, foragers exhibited twice the number of hesitations at sites that resulted in a fixed negative outcome  compared to sites leading to risky outcomes  suggesting foragers perceived the risky outcome as less severe than the fixed outcome despite these schedules having the same mean hold time over the course of training (~ 150 s). This increased hesitation number at the fixed site versus the risky site persisted during the test (μ = 20.6 and 7.9 respectively). Furthermore, hesitation number at the risky site was shown to increase or decrease based upon the outcome experienced on the forager’s previous trip. When foragers experienced a severe 300 s hold time on the preceding trip, hesitations increased (μ =  + 1.16) while when the less aversive 1 s hold time was experienced on the preceding trip, hesitations decreased (μ =  − 0.51). These changes indicate that forager’s hesitancy to pass through the site is being continuously regulated up or down with each new experience of the site. During each training trip, the hesitation behavior expressed represents the forager’s level of aversion to the expected outcome while each new experience regulates this expectation.36. This has been demonstrated in ants, as De Agrò et al.43 showed that ant foragers were risk averse when reward options were balance by mean value. Yet, fixed option preference disappeared when the two reward options were altered to be geometrically balanced. This finding makes sense if animals perceive reward value on a logarithmic scale. For positive outcomes, when the geometric average of a risky reward option falls below the fixed reward option, animals should perceive the fixed option as preferable. Our research suggests that this relationship also fits with the study of variability of negative outcomes. When the geometric average of a negative outcome falls below the fixed outcome, animals should perceive this risky outcome as less severe and become risk seeking, which aligns with our results. In the Risk Sensitivity tests, the two outcomes were only balanced by true value (150 s vs. 150.5 s), while the geometrical average of the hold duration of the Risky site (17.32) was lower than the Fixed site (150). This corresponded with an overall lower level of hesitations at the Risky site compared to the Fixed site. While we did not test purely geometrically balanced Risky vs. Fixed outcomes, the perceived stimulus strength of the Risky site based on the geometrical average would make it very similar to that of the 15 s outcome in the Hold Duration tests (17.32 s vs. 15 s). Interestingly, the hesitation levels during training (Trip 10) between the 15 s outcome (μ = 3.8) and the Risky outcome (μ = 3.3) were not significantly different from one another despite the order of magnitude difference in actual mean hold duration  and thus respond less negatively to the associated views, on par with hesitations to a fixed 15 s hold time , which project onto a number of motor output neurons (MBON). Each MBON conveys an attractive or aversive valence, and changes in synaptic strength between KCs and MBONs by activation of dopaminergic neurons mediating negative or positive experiences, modulates the association between a stimulus and its outputted valence61. Changes within these synaptic compartments mediate the view’s current overall valence by weighting the attractive and aversive valences of the forager’s accumulated experiences. More specifically, the aversive outcome of being captured, as in our experiment, must result in dopaminergic neurons decreasing the connection strength between the recently encountered view specific pattern of Kenyon cells and attractive valence MBONs31. Such a neural architecture can explain the hesitation behaviours observed after training foragers with the aversive outcome.The use of learned route memory in ants involves the Mushroom bodies, MBs62. Second, while aversively trained foragers were highly hesitant to travel through these sites, no forager refused to cross the line, even when they lacked information from a path integration based vector  when compared to a less severe outcome (15 s). Additionally, we show that the foragers show significantly less apprehension to travel through a site associated with a risky aversive outcome compared to a fixed outcome with the same mean and that forager hesitation responses at these sites across experiments was in line with the logarithmic relationship between stimulus strength and perception. Finally, our findings fit within the current modeling of view-based route learning and memory in the mushroom bodies of the insect brain. The behavioral dynamics observed here align well with the complex and parallel memory dynamics of the MB as studied in the context of olfaction in flies. In closing, a final intriguing question remains. Namely, the foragers’ response to different hold durations suggests that they can somehow quantify or estimate their duration of capture, yet the mechanism by which this estimate over such short time scales (~ 135 s) is accomplished currently remains unknown.We found that Supplementary Information.


Thamnophis sirtalis tetrataenia) to fit an integrated growth–survival model. Growth models showed that females reach longer asymptotic lengths than males and that the magnitude of sexual size dimorphism differed among populations. The capture probability and availability of San Francisco gartersnakes for capture increased with snout–vent length. The survival rate of female snakes exhibits a nonlinear relationship with snout–vent length (SVL), with survival flat between 300 mm and 550 mm SVL before decreasing for females between 550 mm and 700 mm SVL. For male snakes, survival decreased for adult males >550 mm SVL. The survival rates of the smallest and largest San Francisco gartersnakes were highly uncertain because recapture rates were very low for these sizes. By integrating growth and survival models and using penalized splines, we found support for size‐dependent survival in San Francisco gartersnakes. Our results have applications for devising management activities for this endangered subspecies, and our methods could be applied broadly to the study of size‐dependent demography among animals.Estimates of demographic rates for animal populations and individuals have many applications for ecological and conservation research. In many animals, survival is size‐dependent, but estimating the form of the size–survival relationship presents challenges. For elusive species with low recapture rates, individuals’ size will be unknown at many points in time. Integrating growth and capture–mark–recapture models in a Bayesian framework empowers researchers to impute missing size data, with uncertainty, and include size as a covariate of survival, capture probability, and presence on‐site. If there is no theoretical expectation for the shape of the size–survival relationship, spline functions can allow for fitting flexible, data‐driven estimates. We use long‐term capture–mark–recapture data from the endangered San Francisco gartersnake  exhibit size‐dependent survival by integrating growth and capture–mark–recapture models. We found that both capture probability and survival are size‐dependent in this species, with individuals of intermediate size having the highest survival rate. Integrating growth models into models of survival has the potential to reveal insights into species demography.We investigated whether San Francisco gartersnakes ( Capture probabilities for this species are low  that occurs in San Mateo and northwestern Santa Cruz counties in California, USA, and is listed as endangered under the federal Endangered Species Act , California red‐legged frogs (Rana draytonii), and Pacific newts (Taricha spp.). San Francisco gartersnake populations have become more isolated as urbanization in the species’ range has altered wetlands and surrounding upland habitats , bulrushes (Schoenoplectus spp.), cattails (Typha spp.), and willows , which provided foraging habitat for San Francisco gartersnakes. Drought from 2012 to 2015 caused the two permanent water bodies to dry in the late summer of 2014. Upland habitat was primarily coastal scrub and grazed grassland, with Douglas‐fir (Pseudotsuga menziesii) forest at higher elevations. Native amphibian prey species for San Francisco gartersnakes at Site C included Pacific newts, Sierran treefrogs, and California red‐legged frogs.Site C was an open‐space preserve in coastal southern San Mateo County  that was off‐limits for recreation but subject to cattle grazing during the 20th century until 1998, and again from 2015 to 2020. Site C is regularly covered in coastal fog  co‐occurred up until 2015; the bullfrogs were culled by site management between 2014 and 2015  that was a private cattle ranch until 2008. Diurnal temperature at Site I ranged between 9 and 30°C  during our sampling. Since 2008, the site has been managed to limit public access and enhance habitat for sensitive wildlife species. Management activities at Site I include controlling vegetation by removing invasive weeds and reintroducing grazing in 2015 to keep vegetation at an early‐successional stage. Aquatic habitat at Site I comprised three ephemeral wetlands and one permanent lake. As in Site C, the permanent lake at Site I dried in the late summer of 2014 for the first time in decades. The vegetation in aquatic and upland habitat at Site I was comparable to that at Site C, with the addition of mixed evergreen forest at higher elevations. Native amphibian prey taxa at Site I were similar to Site C, but invasive American bullfrogs  of all captured snakes. We determined snake sex by cloacal probing and marked snakes by branding ventral scales with a unique code  is drawn from a normal distribution centered on iEL,t with random variation around the expected size at time t (tε) that encompasses both measurement error and individual variation around expected size  growth model based on Armstrong and Brooks  to snake k Wang, , we foll k Wang,  and subsEquation .(2)Li,k) as a function of sex (fixed effect) and random effects of individual (i), site (s), and year (t) (Equation We modeled log(Equation .(3)logfµ is the mean value of k for a female snake, mβ is the effect of maleness on k, im is a binary indicator of whether an individual is male (1) or not (0), and sexα,s, tζ, and iι are site (by sex), year, and individual random effects, respectively.The variable ia) as a function of sex and site  sampling in JAGS Plummer,  accessed2.4ϕ), probability of being available for capture (γ), and daily recapture probability (p) at each site, while accounting for variation due to individual size. Our robust‐design model conditioned on initial capture and is a multistate version of a Cormack–Jolly–Seber model  each composed of multiple secondary periods (j) separated by small temporal intervals; populations are assumed closed between secondary periods , while populations are assumed to be open  during the larger intervals between primary periods , availability (γ), and capture probability (p). The key innovation in the model of Riecke et al. , relative humidity, and day of year on p, where the covariate matrix is W, and the vector of coefficients is pδ  on capture probability to be linear functions because our two‐month sampling period did not cover the full seasonal variation in weather at our study sites. Although we believe that using linear functions was a reasonable assumption for this short time period, it is likely that some of these covariates might have more complex relationships with capture probability when evaluated over a wider range of values .We included potential effects of snake SVL on nes Eqs. ,6. Givennes Eqs. , and basEquation . In equaG = 5 knots  and fit spline functions (degree = 3) to each knot + represents max, and d is the degree of the spline. The splines are penalized because each b is drawn from a normal distribution (N) with a shared standard deviation parameter (bσ), making each b parameter analogous to a random effect , we used a shared spline function, f(x) among sites and both sexes. In other words, although the intercept could differ among sexes and sites, we assumed that the size–survival function would be the same for each site‐sex group, likewise for the size–capture probability function. We also assumed a shared slope among sites and sexes for the size–availability functions. We made the assumption of shared shape of size effects among sites because most of our CMR data came from Site C, and we lacked sufficient data to estimate independent functions for snakes from Site I. We tested for potential correlation among annual growth and survival rates by using a covariance matrix to define annual random effects for these parameters , and data to reproduce occupancy analyses area are available at Rose et al. (https://doi.org/10.5066/P9SC36I8).We ran the robust‐design model using Markov chain Monte Carlo (MCMC) sampling in JAGS Plummer,  accesseda Figure . The Bay33.1n = 91 individuals), followed by Site I (41), Site N (20), Site S (7), and Site P (4). Of the 163 San Francisco gartersnakes with growth data, 148 were caught twice and 15 were caught three times, resulting in 178 growth increments. The average interval between captures was 507.0 days (SD = 284.7), and the majority of growth measurements (126/178) were taken in consecutive years, with a further 41 growth increments separated by 2 years, and 11 increments separated by 3 or more years (max = 6 years). Most recaptured snakes (154/163) were >300 mm SVL at first capture, so minimal data were available to estimate growth of neonates.We captured 1090 individual San Francisco gartersnakes at the five study sites. The largest captured male measured 635 mm SVL, and the largest female measured 825 mm SVL. Of these 1090 San Francisco gartersnakes, 163 individuals  were captured and measured more than once, and data from these snakes were used to estimate San Francisco gartersnake growth. Most of the growth data came from Site C  captured at Site C and Site I. At Site C, 603 snakes  were captured a total of 829 times (including multiple captures within the same year). Of the 603 snakes captured at Site C, 510 were captured in only one year, 84 were captured in two years, and 9 were captured in 3 years. At Site I, 142 snakes  were captured a total of 262 times. Of the 142 snakes captured at Site I, 101 were captured in only one year, 35 were captured in two years, and 6 were captured in 3 years.p) was positively related to air temperature, and negatively related to relative humidity and day of year. Capture probability was lower if there was precipitation in the 24 h before trap‐checking than if there was no precipitation , but the difference between female snakes at the two sites was smaller . At Site I, mean p was higher for male snakes than for female snakes ; at Site C, the two sexes had nearly equal mean values for p , given average survey conditions and 52 days of sampling (the average survey period per year), was 0.48 (0.20–0.88) and 0.59 (0.29–0.94) for female and male snakes, respectively, at Site I, and 0.39 (0.21–0.69) for female and 0.40 (0.23–0.68) male snakes at Site C.Daily recapture probability (ϕ) at Site C exhibited a flat relationship with snake SVL for individuals between 300 mm to 550 mm, followed by a decline in survival for snakes between 550 mm and 700 mm SVL . At Site I, patterns for the posterior mean were similar, but sample sizes were smaller and uncertainty in ϕ was greater for all sizes. Mean annual survival for a snake of average SVL (463 mm) was higher for females than for males at Site C  and survival within our sample .Annual apparent survival  increased linearly with snake SVL  than females , there was a pattern of nearly equal survival for snakes between 300 and 550 mm SVL, and a decrease in survival for female snakes as SVL increased from 550 mm to 700 mm. Whether survival further decreased or plateaued for the largest San Francisco gartersnakes was unclear due to our small sample of snakes >700 mm SVL. Reduced survival for larger female San Francisco gartersnakes could be a sign of senescence  in Canada, with females from eastern populations reaching sexual maturity at a smaller size and producing larger litters of smaller neonates compared with western populations , and potentially greater uncertainty in the response variable where data are sparse. Overall, our recapture rates of San Francisco gartersnakes were low, with only 18% of snakes recaptured in at least one year following their initial capture and marking. This led to high uncertainty in our survival estimates, particularly for the youngest, smallest snakes and the largest snakes, which were rarely recaptured. For elusive snake species, complex statistical models alone cannot resolve the issue of low capture rates Steen, . IncreasReducing uncertainty in our estimates of survival for the smallest and largest San Francisco gartersnakes could require a combination of increased sampling effort, greater duration of sampling within a year, and complementary approaches to studying the demography of these snakes. For reptiles, early life stages are notoriously difficult to capture and recapture ; Data curation ; Formal analysis (lead); Methodology ; Software (lead); Writing – original draft (lead); Writing – review & editing . Richard Kim: Data curation ; Formal analysis (supporting); Investigation ; Methodology ; Software (supporting); Writing – original draft (supporting); Writing – review & editing . Elliot J Schoenig: Conceptualization (supporting); Data curation ; Investigation ; Writing – review & editing (supporting). Patrick C Lien: Conceptualization (supporting); Data curation ; Investigation ; Writing – review & editing (supporting). Brian J. Halstead: Conceptualization ; Formal analysis (supporting); Funding acquisition (lead); Methodology ; Project administration (lead); Supervision (lead); Writing – review & editing .Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Supplementary MaterialClick here for additional data file.


Recruitment hotspots are locations where organisms are added to populations at high rates. On tropical reefs where coral abundance has declined, recruitment hotspots are important because they have the potential to promote population recovery. Around St. John, US Virgin Islands, coral recruitment at five sites revealed a hotspot that has persistent for 14 years. Recruitment created a hotspot in density of juvenile corals that was 600 m southeast of the recruitment hotspot. Neither hotspot led to increased coral cover, thus revealing the stringency of the demographic bottleneck impeding progression of recruits to adult sizes and preventing population growth. Recruitment hotspots in low-density coral populations are valuable targets for conservation and sources of corals for restoration. They haet al. [Coral reefs are infamous for the large extent to which they have changed –20, and et al.  found geet al. , on the et al.  and in tet al. , and witet al. . Other set al. ,28.This study evaluates recruitment of corals having mostly a brooding life-history strategy. Using three decades of surveys from St. John, US Virgin Islands, I tested for hotspots in densities of coral recruits and juvenile corals (≤4 cm diameter) at scales of tens to hundreds of metres. Hotspots for recruitment and juvenile corals were detected, but their ecological impacts were truncated by processes preventing small corals from growing into adult colonies, uncoupling the two types of hotspots and preventing them from increasing coral cover. These processes create demographic bottlenecks constricting the transition of recruits to older size classes, and impeding population growth .. 2 (a)a), where they were approximately 2–100 cm apart as dictated by attachment locations. The first tiles were deployed in August 2006, immersed for approximately 6 months, and replaced in 1/2007, 8/2007, 1/2008 and 8/2008; thereafter, they were immersed for approximately 12 months and replaced in July/August. Ten tiles site−1 were deployed per site in the first year, with 15 tiles site−1 in other years. Deployments in the first two years were part of a separate project [Coral settlement was measured using terracotta tiles (15 × 15 × 1 cm) that were individually attached to the reef at approximately 5 m depth at five sites . Since tiles were immersed for approximately 6 months over the first 2 years, mean settlement by deployments was summed within year by site, which precluded calculating within-site variability. Tiles were independent in the 12 months deployments, and means (±s.e.) were calculated by year with sites as replicates (n = 5).Freshly collected tiles were bleached, dried and inspected for recruits (40× magnification). Corals were identified to family  and expressed as corals tile (b)a). Juveniles were unlikely to be sexually mature and were identified assuming maturity at approximately 4 cm diameter [Favia fragum and Siderastrea radians probably achieved maturity at less than 4 cm [Juvenile corals were ≤4 cm diameter and were surveyed from 1994 to 2020 at six sites (blue points in figure 1diameter . Favia fhan 4 cm  and weren = 40) randomly placed along one 40 m transect site−1, with surveys conducted in May (1995–1995) or July/August (1994 and 1998–2020). Corals were identified to species or genus, and the results pooled among taxa. In another study [Juvenile corals were counted in quadrats The density of recruits (pooled taxa and Poritidae) over 2007–2020 was compared among sites using Friedman's test, in which sites were replicates. The density of juvenile corals was summarized by year and site and Friedman's test was used for 1999–2020, in which all sites were sampled. Densities of juveniles were summarized by decade to test for differences using a two-factor ANOVA in which site and decade were fixed effects; assumptions were tested through analyses of residuals. To detect hotspots in densities of juveniles over metres, densities by quadrat were qualitatively compared along transects and among years at each site. Quadrats were randomly placed, but their relative positions along transects were similar among years. Annual mortality of juvenile corals was compared among sites using one-way ANOVA and logit-transformed data. Concordance in hotspots for recruits and juveniles was tested using Pearson correlations and a Mantel test comparing dissimilarity (by Bray Curtis) matrices based on mean densities for the four sites at which recruits and juveniles were measured. Statistical analyses were completed using Systat 13 and XLSTAT software.. 3 (a)−1 . Poritids were the most common recruit , with densities by site within years ≥0.07 corals tile−1. Mean densities of five other genera ranged from 0.02 ± 0.01 corals tile−1 (Acroporidae) to 0.76 ± 0.09 corals tile−1 (Siderastreidae), and families ranked in mean abundance as Poritidae > Siderastreidae > Agaricidae > Faviidae > Acroporidae; abundance (±s.e.) of unidentifiable corals was 0.51 ± 0.07 corals tile−1.Recruits were found on most tiles, with a grand mean (pooled taxa) of 3.01 ± 0.22 corals tileF4,52 = 26.014, p < 0.001) and was lowest at Little Lameshur Bay  and greatest at Yawzi Point  (b). Recruitment was higher at Yawzi Point than at other sites (p ≤ 0.001), lowest at White Point and Little Lameshur Bay  and intermediate at West Tektite and Cabritte Horn . The results for poritids (c) were similar, with densities differing among sites . Mean (±s.e.) poritid recruitment was lowest at Little Lameshur Bay  and greatest at Yawzi Point .Recruitment varied among sites over 2007–2020 ( tile−1) b. Recruiporitids c were si (b)Porites, which accounted for 62 ± 2% (mean ± s.e.) of corals. Mean densities  varied from 1.26 ± 0.11 colonies quadrat−1 at White Point to 3.22 ± 0.18 colonies quadrat−1 at East Tektite, and differed among sites , with relative differences that were unlike those of the recruits (p < 0.001), and lower at White Point than other sites (p < 0.001). Instead of a recruitment hotspot at Yawzi Point, the hotspot for juveniles was at East Tektite, 600 m southeast . Similarities among sites by mean densities of recruits and juveniles were unrelated for all corals (r(AB) = −0.190, p = 0.613) and for poritids (r(AB) = −0.488, p = 0.265).The density of juveniles varied among sites , and therecruits . Densitioutheast . For WhiF10,136 = 5.263, p < 0.001), with densities higher at East Tektite in the 2000s and 2010s, but at Donkey Bite and West Tektite in the 1990s, and declines over time at most sites .The among-site differences in density of juveniles remained evident by decade, but densities declined over time. Relative to the decade with the highest density (1990s or 2000s), mean density in the 2010s was depressed by 11% (Yawzi Point) to 52% (Cabritte Horn). Density was affected by a site × decade interaction .For tagged juveniles, sample sizes ranged from 1  to 104 , with mean sample sizes (±s.e.) varying from 13.6 ± 1.7 at Donkey Bite to 53.8 ± 5.6 at West Tektite. Annual mortality varied from 19.9 ± 3.5% (Donkey Bite) to 28.1 ± 4.4% (East Tektite), but did not vary among sites . Decadal-scale reductions in growth rate of juvenile corals in this location  have eroper se  is not  se (cf. ). The pr se (cf. . These f se (cf. ,55, and  se (cf. , they maspawning , also respawning . The detspawning . For corspawning , or for spawning .


Twin-screw wet granulation is a crucial unit operation in shifting from pharmaceutical batch to continuous processes, but granulation kinetics as well as residence times are yet poorly understood. Experimental findings are highly dependent on screw configuration as well as formulation, and thus have limited universal validity. In this study, an experimental design with a repetitive screw setup was conducted to measure the effect of specific feed load (SFL), liquid-to-solid ratio (L/S), and inclusion of a distributive feed screw on particle size distribution (PSD) and shape as well as residence time distribution of a hydrophilic lactose/microcrystalline cellulose based formulation. An intermediate sampling point was obtained by changing inlet ports along the screw axis. Camera-based particle size analysis (QICPIC) indicated no significant change of PSD between the first and second kneading section, except for low L/S and low SFL where fines increase. Mean residence time was approximated as a bilinear fit of L/S and SFL. Moreover, large mass flow pulsations were observed by continuous camera measurements of residence time distribution and correlated to hold-up of the twin-screw granulator. These findings indicate fast granulation kinetics and process instabilities for high mean residence times, questioning current standards of two kneading compartments for wet granulation. The present study further underlines the necessity of developing a multiscale simulation approach including particle dynamics in the future. Industrial wet granulation is presently mainly carried out by batch processing. Recently, the possibility of inline quality testing, real-time release, batch-size flexibility, and reduced footprint, among other advantages, has sparked a growing interest in continuous processing ,2, especExperimental studies have been carried out by many researchers to develop a deeper understanding of the TSG process. Many studies use a design of experiments (DoEs) approach ,8. The ie, e.g., ,13. Two e, e.g.,  and the e, e.g., . In the TSG simulations were primarily developed based on these specific DoEs. Alongside, neural-network ,17 and cThe focus of this paper is on executing a DoE that is not only designed to optimize a specific screw setup or to get further process insight. Furthermore, it is designed to allow calibration and validation of the proposed simulation framework specifically regarding residence time and particle size predictions. A lactose/microcrystalline cellulose (MCC) based formulation is used, which is known to be very robust in drug development . To utilThe investigated lactose/MCC active pharmaceutical ingredient free formulation is listed in ® T2F mixer . A Thermo Fisher Pharma 11 extruder system with a TSG conversion kit  was utilized for the wet granulation experiments. The 11 mm co-rotating continuous granulator has a length to diameter ratio (L/D) of 40:1. The heatable jacket was held constant at 30 °C by a cooler thermostat . A gravimetric twin-screw feeder (MiniTwin (DDW-) MT, Brabender, Duisburg, Germany) fed the powder into the TSG at a constant solid feed rate of 1 kg/h. Prior to the first kneading section, water was fed into the TSG via a peristaltic pump  using Marprene tubes with a diameter of 4.8 mm. The screw comprised conveying elements (CEs), kneading elements (KEs), and a distributive feed screw (DFS). The kneading elements are of length 0.25D, with a staggering angle of 60°. The TSG setup without the DFS consisted of the following elements, from engine to outlet (left to right):Prior to granulation, the premix was blended in a TurbulaIn experiments where the DFS element was included, it replaces the last CE of the TSG. The above formula lists the length and element type of each section, with the pitch of CEs appended in square brackets. The curly bracket indicates a repetitive part of the screw setup and its power represents the number of repetitions. The first 0.75D KE section served as a spacer. Two pairs of liquid and solid inlets were positioned, one shortly after the engine for long-screw operation see 3, , the othA two-level full factorial DoE was carried out to measure the effect of two quantitative parameters, namely SFL and L/S, together with two qualitative parameters, the number of repetitive screw segments  and the presence of a DFS element at the outlet; see Pre-studies were performed to identify suitable factor ranges. Originally, it was planned to keep the pitch of all CEs equal. However, below the solid feed section, elements with a higher pitch were needed to allow accurate feeding into the granulator and avoid bridging at the inlet. Application of a DFS element at the outlet of the granulator was also included as factor in the DoE to evaluate its influence on the PSD of the product. Furthermore, it was investigated whether the maximum particle size can be limited by the gap size between two teeth of the DFS.2), while at the same time maintaining a high coefficient of determination (R2). See [The DoE was evaluated using the Modde 12.1 software  with the following response parameters: PSD at the in- and outflow and RTD. Multiple linear regression at a significance level of R2). See  for furtInitial state of particles at the inlet,Intermediary state of particles after one kneading zone (short screw),Final state of particles after two kneading zones (long screw).By adopting the methodology described by Verstraeten et al. , three dThe first sampling point represents the data gathered for the initial state of the powder premix. The initial PSD for each particle ingredient was measured only once and considered unchanged for all runs. The main emphasis is on the differences between the second and third sampling points. Therefore, the first sampling point was not included in the results section, but can be found in the data repository. Collection of samples was started after reaching process equilibrium, which was determined by requiring a constant torque.3) were extracted from PAQXOS and probability density distributions (q3) were obtained by derivation of Q3. A bimodal Gaussian fit  according to Equation (1) was applied to the q3 distributions.Distributions of size and shape were measured in triplicate (except for N16 in duplicate) by a QICPIC particle size analyzer  with PAQXOS 4.0 software. The EQPC diameter was used, i.e., the particle size The mode with lower particle sizes is thus assumed to represent the fines, whereas the second mode, if present, represents coarse agglomerated particles. For the initial distribution, the fines fraction is assumed to be equal to unity. The decrease in fines from initial to the end of the short screw can thus be interpreted as an extension of agglomeration.Further, two descriptors for particle shape were measured: sphericity ratio ΨA . SphericResidence time distribution was measured after reaching steady state conditions by adding 2 mg of the color-dye Tartrazine (less than 1 wt% relative to solid throughput per second) in a short-duration pulse. Camera measurements were utilized according to the method proposed by Kumar et al. . A PythoThe curve was fitted by the continuously stirred tank reactor model (nCSTR) using the rtdpy package . Two parTo validate the camera-based residence time measurement, camera measurements were compared to UV/VIS  dye concentration measurements of samples that were regularly retrieved at the outlet. During selected trials of the DoE, samples of granules were collected for a duration of 5 s at the end of the conveyor belt. Sampling points were from 10 to 60 s in intervals of 10 s. Drawn samples were weighed and diluted in 25 mL of demineralized water. A 0.22 µm filter was used to separate undissolved MCC particles from the solution and, thereby, avoid measurement distortions. The filtrate absorption was spectroscopically analyzed at 425 nm in triplicates. UV/VIS and camera measurements result in different quantities, but, owing to sample collection at fixed time points after dye insertion, the shape of RTD was compared for correlation. Plotting the probability density saturation found in the camera measurements against the mass concentration of Tartrazine found in samples by UV/VIS resulted in good correlations  of RTD shape, as indicated by In the following, the main findings of the DoE are described and discussed to investigate the effect of several critical process parameters for future validation of simulation models.50), which is in line with the findings of Verstraeten et al. [50 is small. To realize the beneficial repetitive screw design and owing to the questionable influence of DFS on d50 with the current formulation and design space, solely results with no DFS are considered. L/S had the greatest influence on d50 and L/S, whereas SFL and screw length had the greatest influence on MRT. Thus, we will focus on these parameters in the following sections. Modde model parameters for d50  and MRT  show satisfactory model significance with the mentioned factors.Adding a DFS element had little to no significant effect on the average particle size . It is important to note that the trends for high L/S are more stable than for low L/S. It seems that a higher liquid content lowers the influence of several agglomeration impeding granulation phenomena, emphasizing a process stabilizing effect of higher L/S.50 increases, which shows a superimposed effect of agglomeration and breakage or attrition. As a peristaltic pump was utilized, the liquid dripped into the barrel. Thus, it is not evident if attrition of especially large particles due to decreasing SFL or the inability to agglomerate particles due to poor liquid distribution—see Vercruysse et al. [For most of the parameter combinations, the amount of fines decreases with increasing screw length, while the mean coarse particle size remains nearly constant or increases, except for low L/S and low SFL; see e et al. —is causie et al.  about thThe shape of particles does not significantly change for different parameter combinations. On average, the particles have a spherical shape with small irregularities; see The mentioned findings on PSDs, fines, and particle shapes question the necessity of applying a second kneading zone for this formulation. However, porosity measurements would help to support the assumptions of the underlying effects. Analyses on tabletability and tablet properties might reveal practical relevance for continuous manufacturing of tablets.All RTDs show a significant initial peak. After reaching peak saturation, the dye concentration in the particles, and thus also the visible color saturation, exponentially decrease. For varying SFL and L/S, the MRT changes; see For low L/S, the RTD is smooth and does not oscillate, while for high L/S, the signal exhibits a high frequency oscillation that gets dampened as SFL increases. Meier et al.  recently2 increases, Q2 decreases when applying a quadratic fit, which indicates over-fitting. For low SFL and L/S, the number of screw segments has less influence on MRT. Deviations between long and short screws are small. Thus, conveying of powder for low SFL and L/S seems to be dominant over viscous, kneading, and mixing effects. It is of note that a hydrophobic formulation might behave differently as opposed to the current hydrophilic formulation. Measurement errors of continuous camera measurements also have an impact on the accuracy of results.The nCSTR fit parameter n, which describes the skewness of the RTD curve, varies in a narrow range between 1.14 and 2.3 with a low standard deviation of 0.65, and thus is negligibly affected by design factors. RTD fits, therefore, primarily vary regarding In RTD of color saturation probability, peaks were found well beyond initial peaks, e.g., in r can be quantified as the relative height of the second to the first peak in the RTD. A logistic function according to Equation (8) shows good correlation to quantified data; see As MRT is a function of L/S and SFL and screw length is also considered with this parameter, we can characterize the mass flow pulsations solely by hold-up. Moreover, the pulsation strength PHere, L, k, and Hold-up increases with higher MRT and so does the pulsation strength. For the present design, short screws as well as low L/S and SFL seem to be beneficial in terms of process stability. However, SFL should not be decreased to a level where accumulation and attrition in kneading sections would take place. Moreover, hold-up could be interpreted as a metric for powder flowability. Highest pulsations occur for large SFL and L/S. Flowability is lowered by both compaction due to higher pressure and high apparent viscosity of the wet bulk. It remains to be seen if other formulations show identical behavior. If not, material hold-up and thus the probability to encounter mass flow pulsations might be correlated with the Hausner-ratio of different formulations.If these pulsations occur by mass flow fluctuations, a continuous catch scale measurement will outperform continuous RTD measurements. Both measurement oscillations and mass flow pulsations can be underlined by combining continuous RTD and mass flow measurements in future research. It is also not evident whether only very high fluctuations where measured and lower frequency fluctuations are not visible by camera measurements as merely the mean saturation in the region of interest is calculated for every frame. For continuous pharmaceutical manufacturing, these large mass flow pulsations could be problematic as a certain process instability and, thereby, deviating product quality could be expected, e.g., negatively effecting mass and content uniformity of the generated dosage forms. To avoid the described kinds of process disturbances, granulation parameters should be carefully chosen, preferring a smaller hold-up.A two-level full factorial design of experiments (DoEs) with a repetitive screw setup was carried out to measure the effect of specific feed load (SFL), liquid-to-solid ratio (L/S), and inclusion of a distributive feed screw (DFS) on particle size and shape as well as residence time distribution (RTD). An intermediate sampling point was obtained by changing inlet ports from a long screw with two kneading sections to a short screw with one kneading section. To measure RTDs, the color saturation of granules was continuously monitored using a UV/VIS validated camera measurement. Particle size analysis was performed by dynamic image analysis (QICPIC).DFS elements had little to no effect on particle size distributions (PSDs) and were thus omitted from the results. Particle size analysis indicated fast granulation kinetics. Neither in particle size nor in shape was a significant change observed when changing from short to long screw configuration. Moreover, a large increase in fines was found for low L/S and SFL, which indicates increased breakage and attrition with two kneading sections. Mean residence time was found to correlate well with L/S and SFL and a bilinear fit was considered suitable. Furthermore, mass flow pulsations, undesirable for continuous manufacturing, were identified in RTD measurements by observing a second peak. The magnitude of mass flow pulsations was found to correlate with a logistic function of material hold-up. In summary, fast granulation kinetics and process instabilities for long screws indicate that, contrary to current standards, one kneading section might be sufficient for granulation of hydrophilic lactose/MCC-based formulations. However, RTD should be combined with continuous mass flow measurements to validate findings in future research and to evaluate process stability.Still, DoEs are prone to contradictory findings as they are dependent on screw configurations and formulations. As mentioned earlier, a comprehensive multiscale model to account for particle interactions in twin-screw wet granulation is still missing. The performed DoE is beneficial for simulation purposes and thus should be utilized to further develop the proposed simulation framework to include both formulation as well as configuration dependency and possibly transition from real to virtual design spaces.


Rapid detection and isolation of coronavirus disease 2019 (COVID-19) patients is the only means of reducing hospital transmission. We describe the impact of implementation of on-site severe acute respiratory coronavirus virus 2 (SARS-CoV-2) reverse-transcription polymerase chain reaction (RT-PCR) testing on reducing turnaround time, isolation duration, pathology test ordering, and antibiotic use in patients who do not have COVID-19. Herein we report our experiences of how on-site compared to off-site SARS-CoV-2 testing impacted the duration of time patients who did not have a diagnosis of COVID-19 spent in isolation precautions and subsequent healthcare outcomes.On January 24, 2020, the first confirmed case of coronavirus disease 2019 (COVID-19) in Australia was admitted to our health network, Monash Health.We examined SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) results on respiratory specimens  conducted during March 1–27, 2020, on hospitalized patients at Monash Health, a tertiary metropolitan healthcare network of 4 acute-care hospitals that serves a catchment area of 1.3 million people within Victoria, Australia.7 Patients were excluded if the SARS-CoV-2 RT-PCR was positive or if results of testing did not affect isolation duration . The study period captured 3 phases of SARS-CoV-2 testing. In phase 1 , all tests were sent to the reference laboratory. In phase 2 , on-site testing began; however, some samples were still sent to the reference laboratory due to reagent shortages or for assay validation. Only tests performed on-site in this period were included. In phase 3 , all tests were analyzed on site.We included all hospitalized patients on isolation precautions as determined by SARS-CoV-2 RT-PCR testing conducted during the study period. Indications for testing were adopted from the national guidelines at that time.6 On-site analysis occurred at the main hospital’s laboratory and was performed with AusDiagnostics  8-well coronavirus assays (cat. no. 20081) or AusDiagnostics Respiratory Pathogens 12-well assays (cat. no. 80618). Samples taken at peripheral sites in our network were transported to the central laboratory hourly.Off-site tests were transported ~30 km to the Victorian Infectious Diseases Reference Laboratory via 2 scheduled courier runs per day. The assay used has been described previously.Isolation time was defined as the time from when patients were recorded to be in isolation to the time that isolation was recorded as ceased. Result TAT was defined as the interval between test collection and result validation by microbiology staff. Time to notification was defined as the interval between test collection and when a clinician recorded notification of the result.2 test. Continuous variables were compared with the Kruskal-Wallis rank test. P < .05 was considered statistically significant. Statistical analyses were performed using Stata version 13 software .Categorical variables were compared using the χ1 (online). In total, 242 RT-PCR tests from 220 patients resulted in 224 isolation periods. Of tests linked to isolation periods, 55 (25%) tests were sent off site, 51 (23%) were early in on-site testing, and 118 (53%) took place after on-site testing was established. Groups were matched for age and sex. Higher rates of ICU admission (P < .001) and mechanical ventilation (P = .024) were observed in the off-site group.Baseline demographics and clinical characteristics of the tested population are reported in Supplementary Table P < .001). The median time to notification of established on-site RT-PCR results was lower than that for both early on-site and off-site RT-PCR results: 19.7 versus 37.6 versus 47.3 hours (P < .001). In the early group who had on-stie tests, there were lower rates of fever being a reason for testing compared to the other two2 cohorts (P = .003). To assess the change in TAT within each study phase, data were plotted, and a linear regression line was fitted . The TAT over the duration of the off-site phase slowed, which was not seen in the on-site phases.Characteristics of testing are reported in Table Isolation-based outcomes are presented in Table We investigated the impact of on-site SARS-CoV-2 RT-PCR testing compared to off-site testing on the time patients who did not have a diagnosis of COVID-19 spent in isolation precautions. We found that the use of on-site RT-PCR testing was associated with reduced isolation time compared to off-site testing for patients who tested negative for SARS-CoV-2.8 In a 2015 Spanish study, rapid influenza RT-PCR testing of hospitalized patients decreased isolation time by 23.7 hours and reduced hospitalization costs by US$70 per patient.9Although this result is unsurprising, we believe is important. To our knowledge, no other study has assessed the impact of SARS-CoV-2 RT-PCR TATs on healthcare resource utilization. Our results suggest that by performing testing on-site, TATs are reduced and with it, the associated costs of isolation requirements likely also decrease. Reduced respiratory virus RT-PCR TATs themselves have been demonstrated to reduce length of stay and healthcare costs.Early on-site testing was also associated with reduced isolation times compared to off-site testing. We originally hypothesized that TATs during the early phase of on-site RT-PCR implementation would be longer than established off-site testing due to the labor required for implementing new testing protocols in a pandemic setting. Interestingly, we discovered that TATs for samples sent to the reference laboratory slowed over the 14-day period. We surmise that through this time, the testing load on the reference laboratory increased precipitously, highlighting one challenge of operating in pandemic settings.We observed a positive correlation between number of routine blood tests and isolation duration. This was not observed for other secondary investigations. Clinicians may have felt inclined to recheck routine blood tests while waiting for RT-PCR results. Furthermore, this finding may suggest that longer isolation periods serve no additional benefit to patients for number of impactful investigations received and may only add additional resource costs.By observing distinct time periods in testing protocol at our health network, we attempted to provide insight into our testing practices as it unfolded throughout the pandemic. As such, generalization of our results is limited by our specific experiences. The study population also varied over the study period. Initial guidelines recommended testing patients with severe pneumonia before the indications were broadened. This led to disproportionate testing being associated with ICU admission, mechanical ventilation, and antibiotic use in phase 1. We were also unable to measure the impact of our findings on patients with COVID-19.SARS-CoV-2 RT-PCR tests on site, both in early and established iterations, were associated with reduced TATs and subsequent isolation times. With the COVID-19 pandemic ongoing, our findings support the benefits of faster establishment of on-site SARS-CoV-2 RT-PCR testing in centers capable of doing so.


This research aimed to developing customized pedicle screw based on Indonesian vertebral anatomy and compare the insertion time, pull-out strength, and screw-media interface area of different screw design. We have developed 3 different types of pedicle screws . The thread diameter was calculated from pedicle width of Indonesian population (6 mm). We used commercially available pedicle screw as control group (6.2 mm).2). The data comparison showed that the square-thread conical-core customized pedicle screw group has comparable insertion time and has better pull-out strength than commercially available pedicle screw.The insertion time were significantly difference between v-thread cylinder-core pedicle screw (22.94 s) with commercially available pedicle screw (15.86 s) (p < 0.05). The pull-out strength was significantly difference between commercially available pedicle screw (408.60 N) with square-thread conical pedicle screw (836.60 N) (p < 0.05). The square-thread conical-core group have the highest interface area (1486.21 mm Pedicle screw is widely used in spinal surgeries. It is as a gold standard for fusion procedure of spine. The example uses of pedicle screw are in correcting scoliosis deformity, disc degenerative disease, infection in spine, tumor, and fractures .In Indonesia, the use of pedicle screw is still limited due to its cost and complexity in its instruments for application. There is no single factory in Indonesia that has been able to produce pedicle screw. The needs for pedicle screws are all being full filled by importing from aboard.Although pedicle screw has the advantage as one of the most rigid fixator , failureThe aim of this study was to develop a customized pedicle screw based on Indonesian vertebral anatomy and compare the insertion time, pull-out strength, and screw-media interface area of different screw design.This was an experimental study. We used 4 groups of pedicle screws with 5 screws each, that consist of group A (v-thread cylinder-core pedicle screw), group B , group C (square-thread cylinder-core pedicle screw), and group D . We used 6 mm of diameter for our customized pedicle screw based on study of thoracic and lumbar pedicle of Indonesian population instead of 6.2 mm of diameter of commercially available pedicle screw. Our pedicle screws were made of AISI 316L and being produced at UPT Logam in Yogyakarta city of Indonesia by using Computerized Numerical Control (CNC) machines. The human resources consist of orthopaedic surgeon, resident and technician team. Statistical calculations were carried out with the IBM SPSS Statistics ver. 22.0  .This study found that the insertion time were significantly difference between v-thread cylinder-core pedicle screw (22.94 s) with commercially available pedicle screw (15.86 s) (p < 0.05). The pull-out strength was significantly difference between commercially available pedicle screw (408.60 N) with square-thread conical pedicle screw (836.60 N) (p < 0.05). The square-thread conical-core group have the highest interface area  and thread pitch where the screw which has a larger flank overlap area and a smaller thread pitch has a higher pull-out strength value . This isThis study showed that our institution has been able to develop customized pedicle screws that the square-thread conical-core customized pedicle screw group has comparable insertion time and has better pull-out strength than commercially available pedicle screw.Our recent development of pedicle screw has only designed for lumbar segment of vertebra. Further research is mandatory to develop another pedicle screw for thoracic and cervical segment. We propose the continuation of this research by comparing designed pedicle screw of lumbar, thoracic and cervical segment.