extfieldsofstudy: Medicine Biology

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Long-term imaging reveals behavioral plasticity during C. elegans dauer exit

Background During their lifetime, animals must adapt their behavior to survive in changing environments. This ability requires the nervous system to undergo adjustments at distinct temporal scales, from short-term dynamic changes in expression of neurotransmitters and receptors to longer-term growth, spatial and connectivity reorganization, while integrating external stimuli. The nematode Caenorhabditis elegans provides a model of nervous system plasticity, in particular its dauer exit decision. Under unfavorable conditions, larvae will enter the non-feeding and non-reproductive stress-resistant dauer stage and adapt their behavior to cope with the harsh new environment, with active reversal under improved conditions leading to resumption of reproductive development. However, how different environmental stimuli regulate the exit decision mechanism and thereby drive the larva’s behavioral change is unknown. To fill this gap and provide insights on behavioral changes over extended periods of time, we developed a new open hardware method for long-term imaging (12h) of C. elegans larvae. Results Our WormObserver platform comprises open hardware and software components for video acquisition, automated processing of large image data (> 80k images/experiment) and data analysis. We identified dauer-specific behavioral motifs and characterized the behavioral trajectory of dauer exit in different environments and genetic backgrounds to identify key decision points and stimuli promoting dauer exit. Combining long-term behavioral imaging with transcriptomics data, we find that bacterial ingestion triggers a change in neuropeptide gene expression to establish post-dauer behavior. Conclusions Taken together, we show how a developing nervous system can robustly integrate environmental changes activate a developmental switch and adapt the organism’s behavior to a new environment. WormObserver is generally applicable to other research questions within and beyond the C. elegans field, having a modular and customizable character and allowing assessment of behavioral plasticity over longer periods. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01471-4.

tractability, i.e., the possibility to identify, trace, and compare neuronal circuits across genetic backgrounds and conditions. While this is challenging in complex mammalian circuits, the nematode Caenorhabditis elegans (C. elegans) has emerged as a suitable model organism for studying the relationship between nervous system plasticity, sensory inputs, and behavior by possessing a stereotyped and fully mapped [2], yet flexible [3] nervous system.
The C. elegans dauer diapause is a bona fide example of a developmental switch that requires neuronal plasticity (Fig. 1a). When encountering an unfavorable environment, e.g., that is lacking a food source, C. elegans L1 larvae can actively deviate from the canonical reproductive life cycle and develop into the so-called dauer larva [4]. This alternative developmental stage is particularly stress resistant and geared for survival, notably by possessing a thickened, closed cuticle. Dauer larvae cease the uptake of food and can remain in this dormant state for several months [5]. Importantly, once environmental conditions improve, dauer larvae will sense and integrate that changed environment and resume reproductive development.
Previously, receptors, signaling molecules, and transcription factors involved in dauer formation have been studied extensively (for review see [6] and [7]). At the neuronal level, individual remodeling events specific to the dauer larva have been identified [8][9][10][11][12]. However, a quantitative and temporally resolved description of the behavioral adaptation process is missing but needed to contextualize molecular and structural changes. Ultimately, linking behavioral adaptation to specific molecular programs (e.g., changes in gene expression) will improve our understanding how a developing nervous system is able to remain plastic and integrate external stimuli.
Here, we introduce a new open-hardware and opensource platform for quantitative behavioral analysis of C. Fig. 1 Overview. a Dauer exit is a developmental decision to resume reproductive development, which takes more than 12 h, highlighting the need for long-term behavioral imaging. b Overview of the assay used in this study. Dauer larvae are transferred to a new environment (i.e., a fresh plate) and their change in behavior is recorded across several hours. Time resolved measurements will be relative to the time point when worms were transferred to the new environment (hours after transfer or h.a.t.). c Sketch of the open hardware; a 3D printed solution for acquiring timelapse videos through the eyepiece of a stereoscope. Camera and acquisition parameters are controlled by a Raspberry Pi, which streams the data to a workstation for data processing. d Summary of the software, which integrates KNIME and the TrackMate framework. By integrating data from thousands of tracks and worm postures, users can build robust, quantitative models of C. elegans behavior Preusser et al. BMC Biology (2022) 20:277 elegans. Although solutions for imaging C. elegans behavior have been previously developed [13][14][15][16][17], performing long-term imaging of hundreds of larvae over several hours to extract population dynamics remains challenging. Our WormObserver platform combines long-term population imaging of C. elegans larvae and open source analysis software in a modular, end-to-end solution that can be adapted to other research questions ( Fig. 1b-d).
We use the platform to describe the behavioral trajectory of dauer exit and characterize dauer-specific behavioral motifs. Moreover, we identify environmental signals that initiate the exit decision and correlate the resulting changes in behavior with gene expression dynamics.

WormObserver: an open hardware solution for long-term behavioral recordings
To capture behavioral changes during the dauer exit decision, a process previously shown to take more than 12 hours [4], we developed the WormObserver, a lightweight, low-cost, open hardware imaging solution that allows parallel tracking of hundreds of worms on culture plates. The WormObserver platform is based on a standard stereoscope, making use of high-quality optical components already available in most laboratories. We designed and 3D printed a two-component adapter that fits the eyepiece of a stereoscope (Fig. 1c, Methods) and holds an attached camera. The camera is connected to a Raspberry Pi, a commercially available, credit-card sized computer, which enables automated video acquisition [18]. To provide users with a ready-to-use solution, we developed a graphical user interface (GUI) for easily adapting imaging parameters like acquisition frame rate, resolution, and timing of the videos to be acquired. To capture behavioral motifs at high temporal resolution over more than 12 h, we split the timelapse in short video sequences (8 min), thereby facilitating tracking and analysis. Acquired videos are streamed directly to a workstation PC applying a custom, open source image processing workflow that uses KNIME [19], ImgLib2 [20], and the TrackMate framework [21] (Methods). Taken together, combining a 3D printed adapter and off-the shelf hardware components with a standard stereoscope and custom software, we envision our tool to be broadly applicable for a large group of users, not only to specialists in microscopy or image analysis. The WormObserver platform is documented [22] and image acquisition and image feature extraction workflows can be executed through graphical user interfaces.

Characterizing behavioral adaptation during dauer exit
Dauer larvae have been described to be distinct in their behavioral repertoire and previous studies have improved our understanding of the underlying neuronal plasticity [9,11,23,24]. Dauers exhibit increased bouts of prolonged locomotor quiescence but can be highly mobile and are able to nictate [25], a dauer-specific behavior that increases the chances to hitch-hike on other animals to escape an unfavorable environment in natural contexts [26,27]. Moreover, dauers moving on a plate generally appear less bent and stiffer compared to well-fed larvae of the same age [28]. Nevertheless, a time-resolved characterization of dauer-exit specific changes in behavior is missing but needed to answer three key questions: First, it is unclear for how long the worm is integrating the changed environment before adapting its behavior. Second, which behavioral motifs are changing during this adaptation process and are all behavioral adaptations occurring at once or in a stepwise manner? And third, are the different environmental stimuli known to regulate dauer exit (i.e., food stimulus, pheromone) all affecting the same or different aspects of behavior?
To answer these questions, we focused on the first 12 h of dauer exit for characterizing behavioral changes and used the condition lacking a food source as a negative control in which dauers cannot successfully exit. We reasoned that behavioral adaptation related to the exit decision would occur before the onset of growth, previously described to resume after ca. 14 h of dauer exit [4]. We induced the dauer stage by a combination of standardized starvation, crowding, and high-temperature conditions (see Methods). Next, extracted dauer larvae were shifted from starved to fresh plates with or without a bacterial food source and monitored for 12 h. To compare behavior across conditions, we refer to each time point as hours after transfer (h.a.t.) to a fresh plate (Fig. 1b).
In a new environment, dauers will switch from behavioral quiescence to a highly mobile state We first asked the question whether the speed by which the worms move on the plate was changing during the course of dauer exit. As expected, dauers that were transferred to a new environment initially exhibited very slow movement or no movement at all, characterized by high angular speed (i.e., path curvature) and low speed. The previously described dauer specific locomotion quiescence [23] was observed for dauers in all tested conditions and is independent of the presence of bacteria on the plate (  Figure S2). However, many larvae remained highly mobile (high speed) and moved straight in one direction (low angular velocity). This initial division into a quiescent and a highly mobile regime appeared in all our experiments and is also recapitulated when quantifying behavior of daf-2 dauer larvae that cannot exit the dauer stage (Fig. 2b, Additional file 1: Supplemental Figure S1). We therefore conclude that dauer larvae, when put onto fresh plates, remain quiescent but can explore the environment in a specific dispersal mode characterized by fast movement into one direction. This is also consistent with the observation that dauers can be highly mobile [28]. Moreover, when extending our analysis beyond the first hours after transfer, we noticed that the quiescent fraction disappeared 3 h.a.t. At this time point, all animals displayed a strong dispersal phenotype characterized by high speed and low . e Schematic summarizing how the dispersal phenotype was defined, based on thresholds for speed and angular velocity (see Methods). f Relative abundance of dispersal behavior as defined in e. Error bars depict standard deviation of at least 2 experiments. g Percentage of dauers that survive SDS treatment at different time points during dauer exit. This assay can be used as a proxy for inferring onset of food ingestion. Dots represent experimental replicates angular velocity (Fig. 2c, d). This was similar across the tested conditions, although the exact timing varied, with the notable exception of daf-2 dauer larvae, where a significant fraction remained in the quiescent phenotype (Fig. 2b, Additional file 1: Supplemental Figure S1). Taken together, we observe that independent of a bacterial food source, 3 h after being exposed to a new environment, wild type dauer larvae exhibit a first behavioral switch from quiescence to a highly mobile dispersal-like behavior. Importantly, this first behavioral switch is independent of the presence of a food source and was observed in all tested conditions.

A second behavioral switch depends on a food stimulus
When extending our analysis beyond the first three hours, we noticed a second behavioral adaptation that was dependent on the presence of an edible food source. After being exposed to the new environment for approximately 6 h, larvae that were placed on a high quality food source decreased speed but increased turning frequency, resembling canonical behavior of adult worms [29] (Fig. 2a). After 12 h, dauer specific dispersal behavior was completely abolished (Fig. 2c, d). In contrast, dauers placed on plates lacking food continued moving at high speed and low angular velocity and did not exit dispersal behavior (Fig. 2a, d). When quantifying the relative fraction of dispersal behavior across conditions and time points (Fig. 2e), we noticed that the behavioral switch from dispersal to dwelling started to occur with high quality (HB101) and low quality food (OP50), in both cases after 6 h.a.t. (Fig. 2f ). Given this independence of the behavioral adaptation process on food source quality, we performed all remaining experiments on OP50. Interestingly, larvae placed on bacteria that were previously treated with an antibiotic, making them too large to be ingested by the worm, did not exit the dauer-specific dispersal behavior (Fig. 2f ). This finding suggested that the ingestion of bacteria is crucial for inducing the behavioral switch. However, when developing into the dauer stage, larvae cease pharyngeal pumping and develop a buccal plug that prevents them from uptake of external components (e.g., a bacterial food source) [4]. To further investigate this, we confirmed that the ability to ingest external components is restored prior to 6 h.a.t. during dauer exit, however with delayed timing if a food source is lacking (Fig. 2g). Taken together, we have characterized a second behavioral switch that consists in exiting the dauerspecific dispersal phenotype and that is, in contrast to the first switch, dependent on the presence of a bacterial food source and therefore indicative of successful dauer exit. Furthermore, we note that it is the ingestion (and not the presence) of bacteria that drives this behavioral change, matching the observation that by the time the switch occurs, dauers will have resumed food ingestion.

Posture adaptation is independent of the presence of a food source
C. elegans posture, i.e., the worm's shape, has been previously established as a robust measure for inferring behavioral motifs based on environmental stimuli or genetic differences [30][31][32][33][34][35]. Specifically, we hypothesized that the dauer-specific stiff posture characterized by shallower bends would disappear during dauer exit. Moreover, we asked the question whether the dauer-specific dispersal phenotype would be reflected at the posture level. As previously described for adult worms [35], segmented worm shapes extracted from moving worms across all time points have low complexity and can be explained by a combination of eigenvectors obtained from the covariance matrix of intersegment angles (Fig. 3a), so-called Eigenworms (Additional file 1: Supplemental Figure S3). We created a dauer-specific posture library (i.e., a postural syntax) for each condition [34] and plotted relative posture abundances across all time points (Fig. 3b, Methods). Posture abundances during dauer exit were consistent across experimental repeats (Additional file 1: Supplemental Figure S4). Interestingly, we detected a shift in postural syntax at about 6 h.a.t when dauers were shifted to a bacterial food source (Fig. 3b). We next aimed at further characterizing postural syntax dynamics across conditions and performed a second round of clustering on the previously defined posture libraries to identify the 3 most diverging posture groups (i.e., meta postures, p 1 , p 2 , p 3 ) and compare their relative abundance across conditions (Additional file 1: Supplemental Figure  S5; Supplemental Figure S6). While skeletons of the first meta posture p 1 resemble the dauer-specific constricted posture, p 2 and p 3 contain more bent postures (Fig. 3c). Consistently, intersegment angle distributions differ significantly between meta postures (Fig. 3d). Next, we quantified the relative abundance of the dauer-specific p 1 postures across time points and noticed a decrease in relative abundance within the first 6 h.a.t. in all tested conditions except for daf-2 mutants that exhibited an impaired transition ( Fig. 3e and Additional file 1: Supplemental Figure S6). It is important to note that due to our skeletonization implementation (Methods), our posture libraries did not contain overlapping postures of turning worms. Hence, the observed decrease in dauer specific postures could be the result of an increase in turn frequency as observed in starved worms [36]. To test this possibility, we trained a random forest classification model to identify turn postures (Methods; Additional file 1: Supplemental Figure S7) and quantified the fraction of turning worms during dauer exit. While the fraction of turns increases in the +OP50 NGM condition ("with bacteria"), turn frequency remains low on 2% Agar plates ("no bacteria") ( Fig. 3f ). We conclude that the measured posture switch detected under conditions with and without a bacterial food source cannot be solely explained by an increase in turn frequency. Importantly, "with bacteria" and "no bacteria" assay plates differed not only by bacteria concentration but also salt and additive concentrations that might also impact dauer behavior (see Methods). However, a posture switch was detected in all tested conditions early during dauer exit, highlighting the robustness of the detected food-independent behavioral switch. Taken together, we detect a new example of behavioral adaptation that consists in switching from a dauer-specific stiff and thin posture to a more bent, canonical C. elegans posture. This change in postural syntax happens within the first 6 h.a.t., but is independent of the presence of a food source, does not explain the dauer-specific dispersal behavior and might be driven by other environmental factors (e.g., pheromone concentration).

Genes driving behavioral changes during dauer exit
Next, we asked the question whether the two main behavioral switches quantified by imaging (Fig. 4a) would also be reflected at the level of gene expression, potentially through the expression of specific neuropeptides, receptors, and ion channels in neurons, previously described to be sufficient to cause drastic behavioral changes in C. elegans [37][38][39] and dauers specifically [9,40]. For instance, insulin-like peptides (ILPs), which are expressed in the head of the worm [41], have been previously shown to be implicated in regulating dauer arrest  [41][42][43]. We therefore performed a low-input RNA sequencing method [44] on C. elegans heads specifically at 4 different time points during dauer exit, exposing recovering dauers to an environment with or without a food source, thereby mirroring the conditions used for behavioral analysis (Fig. 4b, Materials and Methods). Next, we asked the question whether the behavioral switches were recapitulated at the level of the transcriptome by plotting gene expression in PCA space across all conditions and time points. Strikingly, for the condition lacking a food source, head-specific transcriptomes of 3 h, 6 h, and 9 h time points were similar as indicated by PCA clustering (Fig. 4c). Analogous to the behavioral data, if bacteria were provided, the time points 6 h and 9 h separated as well (Fig. 4d), indicating a two-step process driving the exit decision. We next wanted to characterize both behavioral adaptation switches in greater detail by identifying differentially expressed genes between the two conditions at different time points. Here, we focus on transcripts that are relevant for the behavioral adaptation process, but we offer an interactive online resource [45] for exploring the whole dataset. To characterize differential expression during the first food-independent behavioral switch, we computed a list of genes that were differentially expressed in both conditions when comparing the 0 h and 3 h time points. This revealed a list of genes, which contained all members of the arcasoid beta-oxidation pathway [46], all being downregulated in the first 3 h.a.t. (Additional file 1: Supplemental Figure S8). Additionally, we noticed significant upregulation of the insulin-like peptide daf-28, described to be transcriptionally repressed by increased pheromone concentration [43,47] and required for dauer exit [48]. Taken together, we note that early in dauer exit, potentially driven by changes in pheromone concentration (e.g., through downregulated beta-oxidation pathway activity), daf-28 expression goes up. This change in daf-28 expression is independent of the presence of a food source and therefore indicative of an early, intermediate behavioral state that takes place while the exit process is still ongoing.
Next, we sought to investigate the second food-dependent behavioral switch that depends on a bacterial food source, which was already apparent at the transcriptome (Fig. 4c). To identify biological pathways that are differentially regulated between with and without bacteria conditions, we performed functional enrichment analysis using differentially expressed genes as input (Fig. 4e, Methods). At 6 h.a.t, coinciding with the second behavioral switch to post-dauer behavior, the most significantly differentially expressed gene ontology (GO) term was "neuropeptide signaling pathway" (p-value: 1.66x10 -8 ) (Fig. 3f ). The differentially expressed genes within this GO term revealed numerous neuropeptides, comprising both FMRFamide peptides (FLPs) and neuropeptide-like proteins (NLPs), previously described to modulate behavior in C. elegans [49] as well as some of the corresponding receptors (Additional file 1: Supplemental Figure S8). We found that almost all (23/24) neuropeptides were downregulated during dauer exit. Strikingly, one neuropeptide, nlp-24, was upregulated (Fig. 4f ). Besides NLPs and FLPs, we also note differential regulation of ILPs, the third class of neuropeptides in C. elegans, at 6 h.a.t when comparing environmental conditions (Additional file 1: Supplemental Figure S8). Consistent with insulin signaling driving successful dauer exit, ins-17, a DAF-2 antagonist [50], is downregulated, while the counteracting ins-4, a DAF-2 agonist [47], is upregulated. Changes in neuropeptide gene expression are followed by other downstream programs initiated during dauer exit like for instance molting as revealed by GO-term analysis between 6 h and 9 h time points (Additional file 1: Supplemental Figure S9). In summary, we find that dauers that have been placed in an environment containing a food source will exhibit a behavioral change after 6 h.a.t., which coincides with a drastic change in neuropeptide gene expression.

Discussion
In this work, we present the WormObserver as a new, easy-to-use, and cost-effective open hardware solution for the analysis of C. elegans behavior. We envision our method to be useful for many labs independent of computational expertise to perform long-term acquisitions and analysis of C. elegans behavior. A description of the workflow, including detailed instructions, can be accessed under [22]. The modular nature of our system allows for the integration of more advanced opto-electronics or software components (e.g., optogenetics [51] (See figure on next page.) Fig. 4 Transcriptome profiling reveals dynamic neuropeptide signaling during dauer exit. a Schematic summarizing the two main behavioral switches identified by behavioral imaging. b Schematic of the head-specific low-input mRNA sequencing method. For each time point and each condition, 10 heads were sequenced. c PCA of all samples, using the log transformed and normalized counts of the 500 most variable genes as input. d Normalized counts of daf-28 across both conditions and all time points. Depicted is the mean ± standard deviation. e Dotplot depicting the result of the GO-term analysis. For each time point, differentially expressed genes between the two conditions were determined, and the result was used for GO-term analysis. Size of the dot scales with gene ratio. Gene ratio refers to the proportion of genes in the input list that are annotated to that GO-term. Shown are the top 3 GO-terms for each time point, and p-values are indicated next to each dot. f Heatmap of genes contained within the "neuropeptide signaling pathway" GO-term, comparing both conditions at 6 h.a.t. One column shows one experimental repeat Preusser et al. BMC Biology (2022) 20:277 or other tracking software [52,53]). Moreover, given its cost (< 150$) and straightforward assembly, the platform can be considered for larger screens involving several microscopes running in parallel. We also note that in the future, one might consider running parts of the analysis directly at the microscope to reduce data footprint and analysis time, for instance by only saving worm centroid locations. As cost-effective GPU-equipped minicomputers similar to the RaspberryPi are emerging (e.g., NVIDIA Jetson), application of custom deep learning models to perform these tasks directly at the microscope becomes feasible.
Using the WormObserver, our behavioral analysis of dauer exit revealed two behavioral adaptations that have not been described previously (Additional file 1: Supplemental Figure S10): (1) When placed into a new environment, dauer larvae will exit locomotion quiescence and move into a highly mobile, dispersal state. In parallel, dauers will abandon the dauer-specific narrow posture. This first behavioral adaptation happens within 6 h.a.t and is independent of the presence of food. In the wild, dauers can overcome long distances and disperse to more favorable environments by phoresy, the ability to be carried on by another animal [26,27]. However, an intermediate, highly mobile behavioral state occurring early in the exit decision process would enable fast dispersal to more favorable environments across smaller distances. This hypothesis is supported by the fact that C. elegans' "boom-and-bust" lifecycle in the wild crucially depends on active migration during the dauer stage [54]. Interestingly, dauers exhibit distinct alterations in muscle structure, notably increased muscle arm extension, which may facilitate dispersion [55]. Importantly, in this work, we are measuring behavioral phenotypes within a population of dauer larvae. Hence, we are potentially missing minimal variability in terms of exit behavior at the level of individual specimens. It will be very interesting to further assess this variability for characterizing underlying robustness in exit-decision making (e.g., with single worm imaging in multi well set-ups [56,57]). In summary, we hypothesize that the ability to switch to a highly mobile dispersal phenotype is likely to have evolved as an additional behavioral strategy to increase the dauer larva's ability to efficiently disperse to more favorable environments. Strikingly, co-occurring with this first behavioral adaptation, we notice the upregulation of the only insulin-like peptide that is required for dauer exit, DAF-28. Moreover, daf-28 is expressed in ASJ neurons [43], the critical sensory neuron for dauer exit [58]. Interestingly, during C. elegans L1 arrest exit, food perception triggers daf-28 upregulation [59]. Moreover, similar to what we observe during dauer exit, daf-28 upregulation primes L1 larvae for food update but is not sufficient for successful exit. Here, we show that during dauer exit, daf-28 expression increases independently of the presence of a bacterial food source (Fig. 4d). Taken together, it will be interesting to further explore daf-28's role as a transcriptional output of environmental cues and its contribution to exit decisions in both L1 and dauer larvae. More generally, using the WormObserver as a cost-effective tool for long-term behavioral experiments, it will be interesting to obtain behavioral data for all mutants of the dauer pathway (e.g., daf-7, daf-9, and daf-12). (2) If the new environment contains a food source, at 6 h.a.t., dauers, which have resumed food ingestion at this point, start to ingest bacteria and eventually leave the transient dispersal-like state to adapt post-dauer behavior. Strikingly, we find that the two transitions observed at the behavioral level are both reflected at the transcriptome level as well.
We also discovered that neuropeptide transcription is regulated in response to food during dauer exit, potentially initiating behavioral adaptation. Neuropeptides are well-conserved, short amino acid sequences that can directly act as neurotransmitters but also have neuromodulator capacity [60]. In C. elegans, the dynamic expression of neuropeptides has been described to control several fundamental behavioral programs, e.g., mating [61], feeding [38], sleep [62], and others [49]. More specifically, in the context of dauer larvae, previous work has identified increased neuropeptide signaling as an important neuromodulatory event for promoting the dauer entry decision [63,64]. Hence, adjusting the neural transcriptome at the level of diffusible signaling-molecules, i.e., neuropeptides, increases the larva's behavioral repertoire. However, we also find that some neuropeptides are specifically upregulated during dauer exit on a food source (e.g., nlp-24, ins-4), highlighting the complexity of neuropeptide signaling. Given its previously reported role in mediating pharyngeal pumping in starved worms [65], upregulation of nlp-24 could regulate onset of pharyngeal pumping during dauer exit but further experiments will be needed to confirm this hypothesis. Previously, NAD+, which is contained within the bacterial food source, has been shown to promote pharyngeal pumping in dauer-exiting larvae [66]. Here, we show that only ingestible bacteria will later in the process promote another behavioral switch affecting locomotion. It will be interesting to further characterize which components of the ingested bacteria are required for this process, how other environmental factors (e.g., salts and organic material) influence dauer exit and how these com-ponents are sensed and processed. Furthermore, in this study, we do not perform gene expression analysis at the level of individual neurons although gene expression changes in single neurons have been described to initiate changes in C. elegans' developmental trajectory. For instance, the DAF-2 agonist ins-6, has been shown to promote dauer exit by switching between the two sensory neurons ASI and ASJ [42]. Thus, we note that, in the future, resolving transcriptional dynamics of neuropeptides at the level of individual neurons (e.g., through single-cell RNA sequencing [67,68]) will further improve our understanding of the underlying circuit adaptations.

Conclusion
In summary, while comprehensive studies of the combined effect of neuropeptide gene expression changes are missing, our study sheds light on the role of neuropeptides in establishing a developmental decision in response to environmental stimuli. By correlating differential expression with long-term behavior data obtained in the same conditions, we associate gene expression changes to behavioral switches during dauer exit, making a first step towards answering the question how nervous system plasticity can be achieved by dynamic expression of neuromodulators. Thus, our work also highlights how the combination of long-term behavioral imaging and gene expression analysis can improve our understanding of developmental and neuronal plasticity.

C. elegans strains and handling
Worms were maintained at 15 °C on nematode growth medium (NGM) with Escherichia Coli OP50 as food source as previously described. Dauer formation was induced by transferring a mixed population of ca. 50-100 worms to 6cm NGM plates seeded with E. coli OP50. Plates were then incubated for at least 7-10 days at 25 °C. Next, worms were washed off with M9, washed twice to eliminate remaining bacteria, and 1% SDS solution was added. The mixture was incubated for 30 min at room temperature [4]. After an additional washing step with M9 to eliminate SDS, worms were transferred to an unseeded NGM plate. After 30 min, once the drop of M9 dried out, dauers, which can survive SDS treatment (unlike all other C. elegans stages), crawled out of the mix of dead worms and were collected. All experiments were carried out with the N2 Bristol strain (obtained from CGC) except for experiments involving a daf-2 insulin mutant. Animals carrying the daf-2 (e1370) mutation were incubated at 25 °C for at least 7 days to obtain pure dauers and subjected to the same SDS-selection procedure prior to imaging.

Assay plates
We used commercially available freeze-dried E.coli OP50 (+OP50, available from LabTIE) or HB101 (+HB101, available from CGC) bacteria for conditions involving a bacterial food source. Dead OP50 bacteria were prepared according to the manufacturer's instructions (final concentration: 3.6mg/ml). HB101 were cultured overnight at 37 °C. Both cultures were diluted 1:10, added to 3cm NGM plates, UV irradiated to abolish bacterial growth and kept for storage at 4 °C. Inedible bacteria were prepared by incubating E.coli OP50-GFP (available from CGC) with Aztreonam (Cayman Chemical, CAY19784-1) as previously described [69]. The resulting elongated bacteria were visually inspected under a fluorescence microscope, seeded on NGM medium, UV irradiated and kept for storage at 4 °C. For the "no bacteria" condition, plates contained only 2% Agar.

SDS-resistance assay
N2 dauers were obtained as described above and placed on assay plates with or without bacteria (see above). Every hour, ca. 50-100 worms were washed once in M9, added to 1% SDS, and incubated for 15 min at room temperature. Next, an additional wash in M9 was performed and worms were checked for viability on an unseeded NGM plate.

WormObserver and imaging
For each imaging experiment, ca. 250 dauers were transferred in a small drop of M9 and added to 3 cm plates (for preparation see above). We used a Zeiss Discovery V8 stereoscope with illumination from below the plate and a Plan S 1x objective with 3.2x zoom. To filter out blue light from the illumination source, a red filter (Edmon Optics, #53-699) was placed in between the light source and the assay plate. The WormObserver hardware consists of two parts: a custom 3D-printed eyepiece sleeve with a fixed 8 Megapixel Raspberry Pi camera and an attached Raspberry Pi model 3 with a touchscreen display for defining acquisition parameters. The Raspberry Pi camera is an 8 Megapixel Raspberry Pi V2 camera module with a maximum resolution of 3280 × 2464 pixels. All videos analyzed in this paper were acquired at a resolution of 1024 × 1024 pixels. Given our magnification (that is defined by the stereoscope's objective and the eyepiece), we obtain a pixel size of 6.25 μm/px. Field of view is 6400 μm × 6400 μm. We provide all details for fabricating the hardware components together with the required software and descriptions under https:// github. com/ Fritze/ WormO bserv er/. For all experiments shown here, we recorded at least 100 consecutive videos of 8 min (> 13 hours). Worms are allowed to move in and out of the field of view and the same worm might be captured multiple times. Therefore, our measurements should be considered a population average rather than "single worm" measurements. All timelapse data was acquired at room temperature (20 °C). Experimental repeats were performed on different days.

Image analysis Imaging and tracking
The custom, open-source KNIME workflow that is designed for automated processing of the data can be assessed under https:// github. com/ Fritze/ WormO bserv er/ tree/ master/ KNIME. Users only need to define the location of the incoming videos as sent by the Rasp-berryPi, and all image processing steps are performed automatically with no user intervention needed. H264-compressed videos are automatically converted into images using ffmpeg and downsampled from 5 fps to 2 fps for tracking. This downsampling step is optional and can be omitted if larger frame rates are required. Briefly, worms are segmented after background subtraction, filtering, and size selection of segmented connected components. Tracks are identified using the TrackMate [21] node and worm skeletons extracted using the "skeletonize" ImageJ command in KNIME. In summary, each video of a given timelapse experiment is summarized in a result table containing an ID, the relative position, track statistics, turn prediction score, the segmented shape, and the skeleton for all detected and tracked worms. A workstation (Intel 2.6 GHz CPU, 64 GB RAM) was used for processing all datasets. Processing 100 time points took about 17 h per dataset, which is only 4 h longer than the runtime of the timelapse, highlighting the value of parallelizing acquisition and analysis for long timelapse experiments.

Tracking data analysis
We wrote custom code in R to process result files as given out by the KNIME workflow. All analysis steps presented in this paper can be performed by calling R-scripts from the command line. All scripts can be accessed under https:// github. com/ Fritze/ WormO bserv er/ tree/ master/ code/ track ing. For all analysis, we discard the first 30 min of the timelapse to avoid artifacts introduced by worm handling. Briefly, we calculate speed and angular velocity per track over 10 s bins. Only tracks without gaps and of minimum length of 20 frames (i.e., 10 s at 2 fps) were considered. We additionally exclude non-moving tracks to avoid segmentation artifacts. Dispersal behavior was defined as tracks having a minimum speed of 0.01 mm/s and angular velocity below 15 °/s.

Skeletonization
For every frame and segmented worm, a skeleton was extracted and saved from within the KNIME workflow.
All subsequent analysis steps were performed in R and relevant source code can be accessed under https:// github. com/ Fritze/ WormO bserv er/ tree/ master/ code/ track ing/. Briefly, for each skeleton, we first estimate the worm's head position based on the direction of movement. Next, only skeletons that can be unambiguously identified (i.e., no branches) and of a minimum length of 26 pixels are retained. Finally, for skeletons where the first step could not assign head position on the direction of movement, information from adjacent frames was used. If adjacent frames did not contain head position information, the skeleton was discarded. We manually confirmed this approach to be a good conservative estimate of the worm's skeleton and head position. For calculating Eigenworms, we focused on moving worms and only included tracks with a minimum track duration of 5 s, speed > 0.01 mm/s, and angular velocity < 50°/s.

Posture analysis
For each skeleton, we then fit 26 equally spaced segments on the midline of the worm and calculate a vector of 24 intersegment angles, previously shown to be sufficient for describing C. elegans posture [32]. To cluster postures based on intersegment angles obtained after skeletonization, we used an approach adapted from [34]. For this analysis, we only considered tracks that were successfully tracked and skeletonized for at least 5 s without interruption. Since we do not follow individual worms and therefore image at lower magnification to capture a larger number of larvae within the field of view, we could not distinguish dorsal and ventral turns in our data. After kmeans clustering with k = 200, we next identified mirrored posture pairs and treated each pair as if it was the same posture for further analysis. This reduced the complexity of our posture library by ca. 50% depending on the analyzed condition. This first round of clustering was done individually for each condition but across experimental repeats. Similarly, the z-score depicted in Fig. 3b was calculated using the relative abundance of each posture (column in the heatmap) across all experimental repeats and time points (rows in the heatmap). For this calculation, all experimental repeats were combined and treated as if they were one experiment. To exclude batch effects, Z-score calculations of separated experimental repeats were performed as well (Additional file 1: Supplemental Figure S5). To compare relative abundances of certain posture types (i.e., meta postures) across conditions, we subjected all previously identified postures to a second round of k-means clustering (k = 3) and plotted the relative abundance of the three meta postures across conditions and experimental repeats. We note that the resulting posture libraries did not contain any self-occluded shapes (i.e., turns) as these did not pass our criteria for successful skeletonization. To be able to measure the abundance of turns in our datasets across time points and conditions, we used 17 image features extracted from segmented worm bitmasks and trained a random forest model for detecting turns. When training the random forest model, worm postures were annotated as "turn" when head or tail were touching the worm's body or if both head and tail were clearly pointing in the same direction (U-shape). The model (incl. Bitmasks used for training) is available under https:// github. com/ Fritze/ WormO bserv er/ tree/ master/ KNIME and can be loaded into KNIME for inspection. The classification is integrated within the image processing workflow in KNIME and flags every frame of a track with either 0 or 1 if a turn was detected. Next, we bin all turn occurrences over a period of 10 s to avoid over counting still animals (i.e., if more than one frame is flagged as turn within the 10 s window, it will be counted only once).

RNA sequencing Library preparation
N2 dauer larvae were cultured and extracted as described above. For time point 0, dauers were directly collected from the unseeded NGM plate. Dauers assigned to "no bacteria" condition were always collected first and "with bacteria" condition second (10-20 min later). Assay conditions were the same as for the timelapse imaging experiments and both conditions (no bacteria and + OP50) were processed in parallel. Every hour, a fraction of dauers was washed off the assay plates and anesthetized with 1mM tetramisole. Under a dissecting microscope, the worm's head was cut off just below the terminal bulb of the pharynx. For each time point, 10 heads were pooled into a pre-cooled Eppendorf tube on dry ice, containing 500 μl trizol and 1 μl GlycoBlue. Samples were frozen at -80 °C for storage and later subjected to RNA sequencing according to the Tomo-Seq protocol [70] with minor modifications. Briefly, after thawing, samples were mixed thoroughly and incubated for 5 min at room temperature. Subsequently, 100 μl of chloroform were added to each sample, mixed well, and incubated for 5 min. Samples were then centrifuged at 12,000 g for 15 min at 4 °C. Next, the aqueous phase was carefully transferred to an Eppendorf LoBind containing 250 μl isopropanol and precipitated overnight at − 20°C. Samples were centrifuged for 10 min at 12,000 g and at 4 °C. Next, the supernatant was removed and the RNA pellet was washed with 75% freshly prepared ethanol. After washing, the pellet was dried and, once completely dry, resuspended in 2 μl molecular grade water. One microliter of the sample was transferred to a fresh low binding PCR tube containing barcoded oligo (dT) primer. Reverse transcription was performed with the Superscript II kit according to [70]. First strand synthesis was performed for 1 h at 42 °C. Second strand synthesis was performed for 1 h at 16 °C. Next, samples were pooled and purified with Agencourt AMPureXP beads. IVT was performed for 16 h at 37 °C. After fragmentation and purification, RNA was reverse transcribed with superscript II and amplified by PCR. Concentration and size distribution of the resulting cDNA were measured with Qubit and Tapestation. mRNA Libraries were sequenced on a NextSeq500 in paired end mode with 150 cycles.

Differential expression analysis
Raw sequencing basecalls were demultiplexed and converted to FASTQ format using custom python scripts. RNA-seq reads were mapped to the ce11/WBcel235 genome assembly using STAR_2.7.3a [71], annotated using Rsubread [72], and count matrices were generated using custom python scripts. PCA was done on normalized log transformed read counts of the 500 most variable genes across conditions and time points. Differential expression changes were determined with DESeq2 [73], with lfc shrinkage turned on. Differentially expressed genes were called with a significance cut-off of 0.05 (p-value adjusted) and a minimum log2 fold change of 1 or − 1. For GO term analysis, the gpro-filer2 package [74] was used with a significance cut-off of p-value 0.01 and a custom background model that contained all genes that we detected with our method across all conditions and time points with at least 10 reads. The dataset can be interactively explored under https:// www. bit. ly/ dauer_ exit.


s2fieldsofstudy: Biology Medicine

text:
Induction of Human Oral Squamous Carcinoma Apoptosis by Derris scandens Benth and Elephantopus scaber Linn Extracts

D scandens (Derris scandens Benth.) and E scaber (Elephantopus scaber Linn.) contain flavonoids and phenolic acids, which have antitumor activity in various cancer cell lines. Oral cancer is among the most common cancers in Southeast Asia, and the survival rate remains low. Thus, this study screened 2 ethanolic plant extracts for cytotoxicity on the oral human squamous carcinoma cell line (HSC-2), and compared the mechanisms of action. Extracts of D scandens and E scaber showed cytotoxicity against HSC-2 cells in a dose-dependent manner. Observation of nuclear morphology by Hoechst 33342 staining revealed chromatin condensation. Apoptosis was confirmed by Annexin V-FITC staining and cell sorting (fluorescence-activated cell sorting) analysis. We demonstrated that cancer apoptosis was accompanied by changes in the expression of procaspase 3 and that D scandens-mediated apoptosis in HSC-2 cells was potentiated by protein kinase B (Akt) and B-cell lymphoma-2 (Bcl-2), while E scaber apoptosis was mediated by mitogen-activated protein kinase (MAPK) pathways, involving stress-activated protein kinases/jun amino-terminal kinase (SAPK/JNK) and p38-MAPK. Further investigation into targets for apoptosis induction by these plant extracts may have potential in oral cancer therapy.

Introduction
Oral cancer is among the 5 most common cancers in Thailand with an age-standardized rate incidence of 15.7 and 10.7 per 100 000 males and females, respectively. 1 This is mainly caused by betel and areca nut chewing, 2 tobacco use, 3 alcohol consumption, 4 and the human papillomavirus. 5 Treatment of oral cancer by conventional methods includes surgery, chemotherapy, and radiation; however, the survival rate has remained unimproved for many years. 6 Thus, the identification of novel, effective therapeutic agents targeting apoptosis continues to be an important success factor for cancer prevention. Phytochemicals in plants are a source of drug discovery due to the presence of bioactive compounds such as flavonoids and sesquiterpenes, as well as their precursors, many of which are associated with anticancer activity. [7][8][9] Research in the ability of phytochemicals in non-toxic botanicals to induce cell apoptosis for non-invasive management of oral cancer is increasing. 10 Derris scandens Benth. (D scandens) and Elephantopus scaber Linn. (E scaber) have been used in traditional Thai medicine since the 13th century AD as active ingredients in longevity concoctions, and cancers are considered an age-related disease. 11 E scaber is mainly used in longevity 1 concoctions arising from central Thailand, while D scandens is a more popular ingredient in traditional medicine from Northeast Thailand. Comparisons between the efficacy of these 2 extracts have not been conducted. Extracts of E scaber have been found to inhibit tumor necrosis factor alpha (TNF-α) and interleukin-1β (IL-1β) in lipopolysaccharide-stimulated monocytes, 12 while deoxyelephantopin in E scaber has been found to promote wound healing in rats, 13 and antitumor activity has been reported for the leaves of the plant against Dalton's ascitic lymphoma 14 in mice. 15 Meanwhile there is evidence that D scandens can enhance radiation therapy in human colon cancer cell line HT-29 cells, and a decrease in Erk1/2 activation may be involved. 16 Mitogen-activated protein kinases (MAPKs) regulate cell proliferation, differentiation and survival pathways, including apoptosis, 17 and along with the phosphatidylinositol-3-kinase/ protein kinase B (PI3K/Akt) cascade, are highly involved in oncogenic pathways. 18 Thus, research into bioactive phytochemicals targeting key modulators of downstream MAPK/PI3K signaling are beneficial for cancer therapy. Studies have shown that activation of downstream constituents of the MAPK cascade are important mediators of apoptosis in various cancer cell lines. [19][20][21] Another pathway that regulates apoptosis and cell survival involves the anti-apoptotic protein B-cell lymphoma 2 (Bcl-2) and its family member proteins such as Bax (a proapoptotic protein), which regulate the mitochondria-dependent apoptotic pathway and are often observed alongside caspase 3 activation. [22][23][24] Interaction between Bcl-2 family member proteins and MAPK cascades is known to regulate apoptosis. [25][26][27] Dysregulation of the PI3K/Akt pathway is also well-associated with cancer pathological processes, and Akt and Bcl-2 downregulation have been observed in phytochemical-induced cytotoxicity. 28,29 We investigated the role of phytochemical-induced apoptosis in the human squamous carcinoma (HSC-2) cell line, and compared the mechanisms through which D scandens and E scaber exert antitumoral or proapoptotic effects, focusing on the modulation of components of the MAPK and/or PI3K/ Akt pathways, which have not been reported in previous literature.

HSC-2 Cell Apoptosis by Extracts of E scaber and D scandens
To investigate the nature of E scaber and D scandens-induced cell death, a double staining method using FITC-labelled Annexin V and propidium iodide (PI) was used to detect apoptotic cells using fluorescence-activated cell sorting (FACS) analysis ( Figure 2A). Apoptotic cells were recognized by Annexin V and PI-positive staining, while viable cells were negative for both Annexin V-FITC and PI. No difference in cytotoxicity was found in cells treated with 0.75 mg/mL of either extract of E scaber or D scandens compared to the control (data not shown). Treatment with 1.5 mg/mL E scaber extract for 24 h caused significant percentage of apoptosis in HSC-2 cells compared to control (35.7% ± 3.8 vs 10.4% ± 4.7, P < .05), however this was not observed after treatment with 1.5 mg/mL D scandens extract ( Figure 2B). At 3 mg/mL, higher apoptotic events were significantly observed compared to control upon treatment with extracts of E scaber (52.3% ± 12.1 vs 10.4% ± 4.7, P < .01) and D scandens (69.2% ± 7.3 vs 12.4% ± 4.6, P < .01).
Hoechst 33342 staining was conducted to detect the presence of chromatin condensation, nuclear fragmentation, and apoptotic bodies as these are characteristics of cellular apoptosis. 30 Cells in the control group with intact DNA had weaker Figure 1. HSC-2 cytotoxicity was measured using MTT assay. Cell viability in response to varying concentrations of extracts, D scandens and E scaber, are displayed as the percentage of viable cells after treatment compared to control. Data expressed as means ± SD; n = 3 *P < .05, **P < .01. fluorescence compared to brighter, visible fluorescence in cells treated with extracts of E scaber and/or D scandens due to chromatin condensation ( Figure 2C). Treatment with 1.5 and 3 mg/mL E scaber extract for 24 h caused a ∼10-fold and a ∼50-fold increase in apoptotic bodies and chromatin condensation compared to the control, respectively (4.3 ± 0.6 vs 0.4 ± 1.3, P < .01; 21 ± 2.6 vs 0.4 ± 1.3, P < .01) ( Figure 2D). Treatment with 1.5 and 3 mg/mL D scandens extract also caused significant increase in percentage apoptosis compared to the control (12.3 ± 0.6 vs 0.4 ± 1.3, P < .01; 31 ± 1 vs 0.4 ± 1.3, P < .01). The pattern of HSC-2 apoptosis observed with Annexin V-FITC and PI staining and Hoechst 33342 after treatment with 1.5 and 3 mg/mL E scaber and D scandens extracts were similar.

D scandens-Mediated HSC-2 Cytotoxicity is by Apoptosis Induction Involving Procaspase 3
Activation of the effector caspase 3 through cleavage at specific internal residues is important for initiating the apoptosis cascade, 31,32 thus the detection of procaspase 3 cleavage by western blotting indicates apoptotic events. Here we show that treatment with E scaber for 24 h produced no changes in procaspase 3, however treatment with 1.5 and 3 mg/mL D scandens revealed significant activation of procaspase 3 (0.4 ± 0.06 vs 1 ± 0.07, P < .01; 0.5 ± 0.02 vs 1 ± 0.07; P < .01, respectively; Figure 3A). Densitometric analysis of protein bands revealed over 1.5-fold decrease in procaspase 3 after treatment with 3 mg/mL D scandens. Peak activation was observed at a concentration of 1.5 mg/mL, which caused approximately a 2.5-fold change in procaspase 3 expression ( Figure 3B). Cleavage of caspase 3 by D scandens is concentration-dependent, whereas treatment with E scaber did not produce significant change in protein expression.

Extract-Induced Apoptosis Involves MAPK and Activation of Akt
MAPKs and PI3K/Akt pathways have roles in apoptosis and cell survival mechanisms. 21,33 Thus, we performed gel electrophoresis experiments to observe fold changes and posttranslational modifications of proteins involved in these cascades, namely Akt1, Bcl-2, p44/42 (Erk1/2), p54/46 (SAPK/JNK), and p38-MAPK, in response to treatment with 3 mg/mL E scaber and D scandens ( Figure 4A). We found a steady decrease in Akt1 phosphorylation after 15 min of treatment with E scaber ( Figure 4C). A similar pattern was observed after treatment with D scandens, where activation of Akt1 was seen after 15 min, followed by a steady decline in phosphorylated protein until 120 min.

E scaber-Induced Apoptosis is Inhibited by MAPK Inhibitors
To further investigate the relationship between extract-induced apoptosis in HSC-2 carcinoma and proteins involved in the MAPK and PI3K/Akt pathways, cells were pre-treated with inhibitors to Erk1/2 (U0126), PI3K (LY294002), p38MAPK (SB203580), and JNK1/2 (SP600125) for 1 h, followed by treatment with 3 mg/mL E scaber and D scandens for 24 h, before analysis by flow cytometry and Annexin V-FITC staining. We found apoptosis inhibition with pre-treatment of SB203580, SP600125, and LY294002 followed by 1.5 mg/mL E scaber ( Figure 5). However, pre-treatment with U0126 did not inhibit extract-induced apoptosis (16.4 ± 0.2 and 21 ± 6.6; P < .05). Pre-treatment with various inhibitors followed by 1.5 mg/mL D scandens did not affect cytotoxicity and the percentages of apoptosis observed were similar to treatment with the extract alone ( Figure 5B).

Discussion
Natural products are increasingly being developed as a means of non-invasive anticancer therapy in conjunction with current conventional therapeutic methods such as chemotherapy and radiation. 10 Phytochemicals remain a valuable source for discovering new compounds which target downstream components of pathways regulating cancer initiation, progression, β-actin was used as loading control. Results are expressed as means ± SD; n = 3; **P < .01. and proliferation. 10 The ability of bioactive compounds to induce apoptosis in cancer remains fundamental to the success of cancer treatment. Our study reports for the first time that crude ethanolic extracts of Derris scandens Benth. (D scandens) and Elephantopus scaber Linn. (E scaber) induce apoptosis in the human squamous carcinoma cell line 2 (HSC-2) in a dosedependent manner. D scandens are an excellent source of flavonoids including genistein, luteolin, and scandinones, 35 while E scaber have a high concentration of sesquiterpene lactones such as deoxyelephantopin that display antitumor effects. 36,37 Evidence in the literature suggests that scandinones are the main bioactive compounds in D scandens which affect various components of inflammation, 35,38 and the herb has been implicated for the treatment of osteoarthritis and muscle aches and pains in Thailand. It is possible that the plant's cytotoxic effects originate from genistein and luteolin as both have been substantially implicated in apoptosis of various cancer types including of the breast and liver. [39][40][41] The presence of chromatin condensation and apoptotic bodies indicate cell death by apoptosis, and our study revealed these occurrences upon treatment with D scandens, as well as E scaber, which was in line with our cytotoxicity results.
One major event in apoptosis is characterized by a feedback loop involving cytochrome c release, which induces caspase 9 activation, caspase 3 cleavage, leading to subsequent proteolysis of Bcl-2 and resulting in more cytochrome c release causing cleavage of various downstream caspases. 22 Bcl-2 is an antiapoptotic protein and it preserves mitochondrial membrane integrity by preventing Bax/Bak oligomerization, therefore preventing the release of apoptogenic molecules from the mitochondria. 23 Our results show that treatment with D scandens induces procaspase 3 and Bcl-2 downregulation. Because activated caspase 3 is known to cause cleavage of Bcl-2, converting it into a Bax-like fragment, 24 it is possible that caspase 3 cleaved from the procaspase form may facilitate Bcl-2 proteolysis in the cytosol, causing cleaved caspase 3 itself to be used up as a substrate in a reaction, and inducing a feedback loop causing apoptosis. 42 Treatment with E scaber did not produce a change in procaspase 3 nor caspase 8 (Supplemental Figure S1), despite apoptotic events observed through flow cytometry. However, western blotting revealed upregulation of phosphorylated SAPK/JNK prior to peak p38-MAPK activation. Studies in the literature have found that p38-MAPK activation can cause phosphorylation of Bcl-2 and suppression of caspase 3 cleavage. [25][26][27] This may explain the lack of fold change observed in caspase 3 after cell exposure to E scaber. The increase in SAPK/JNK prior to peak p38-MAPK observed was similar to other studies investigating antitumor properties of nutritional components. 43 PI3K and Akt/Bcl-2 pathways play significant roles in regulating oncogenesis, 17,18 and our study has shown that crude ethanolic extracts of D scandens and E scaber altered the signaling of these pathways and caused cell apoptosis. Akt is activated by PI3K and phosphorylates Bad at Ser136, resulting in caspase 3 inhibition through (a) the maintenance of Bcl-x function and (b) prevention of cytochrome c release from the mitochondria. 29 Therefore, reduced available Akt would cause increased apoptotic events. Our study found that the amount of available Akt isoform, Akt1, was significantly reduced after treatment with E scaber and D scandens in a dose-dependent manner, suggesting that Akt1 play a role in plant extract-induced cytotoxicity of HSC-2 cells. There is evidence in the literature that phytochemicals are able to regulate Akt directly. One such example is genistein, a bioactive component in D scandens, which was found to downregulate Akt and causes inactivation of NFκB 44 ; a key regulator of genes involved in cell activation and proliferation. 45 This direct interaction between D scandens and Akt may be independent of MAPK/PI3K pathways as pretreatment with MAPK/PI3K inhibitors did not prevent extract-induced cell death. Because Akt is activated by PI3K to inhibit apoptosis, 29 our results suggest that D scandens causes cytotoxicity by direct Akt1 downregulation. Existing literature also demonstrates the targeting of Akt by a compound found in E scaber, deoxy-elephantopin, to induce apoptosis or autophagy in many cell types, including microglia, 46 SiHa (cervical carcinoma) cells, 47 and HCT116 (colorectal cancer) cells. 48 Our results show phosphorylation of Erk1/2 after treatment with E scaber. Erk1/2 activation via phosphorylation causes nuclear translocation of the molecule after it is dissociated from Mek1/2, and this translocation determines cell proliferation or apoptosis depending on the intensity and duration of pro-and anti-apoptotic signals. 49,50 Akt phosphorylates and stabilizes astrocytic phosphoprotein (PEA-15), a protein which sequesters Erk1/2 into the nucleus, and causes cell proliferation. 51 Our results show decreased Akt, causing Erk1/2 to be retained in the cytosol and augmenting apoptosis, which is similarly reported in existing literature. 49,51,52 Pre-treatment of the Erk1/2 inhibitor, U0126, did not directly inhibit E scaber-induced cytotoxicity, suggesting that Erk1/2 plays a supportive role downstream of Akt: once Akt1 is decreased, Erk1/2 is retained in the cytosol and prevents cell proliferation. In contrast, pre-treatment of inhibitors involved in the MAPK and PI3K/Akt pathways were able to inhibit cytotoxicity induced by E scaber, further suggesting their crucial involvement as main mediators of E scaber-induced HSC-2 apoptosis.
Our results suggest that D scandens-induced apoptosis is mediated by direct Akt activation, while Akt1 downregulation and p38-MAPK activation seem to mediate E scaber-induced HSC-2 apoptosis. We thereby propose a mechanism of action for cytotoxic effects of E scaber on human squamous carcinoma 2 (HSC-2) cells, firstly by decreasing Akt1, and secondly, by direct activation of SAPK/JNK, causing phosphorylation of p38-MAPK and the anti-apoptotic protein Bcl-2, which suppresses caspase 3 cleavage. Similar findings have been reported with other flavonoids such as curcumin activating p38-MAPK, Bcl-2, and Akt, as well as other signaling proteins involved. 28 Based on our findings, we propose a possible mechanism in which D scandens and E scaber cause HSC-2 apoptosis ( Figure 6). The regulatory scope of apoptosis induction by phytochemicals is increasingly being recognized as a means for cancer treatment, thus further studies to elucidate the exact mechanism of action of these plant extracts on oral cancer cell lines are warranted.

Cell Culture
Human squamous carcinoma (HSC-2) cell line was purchased from the National Institute of Biomedical Innovation, Health and Nutrition (Japanese Collection of Research Bioresources Cell Bank [JCRB]; Osaka, Japan) and cultured in 1X Eagle's minimum essential medium with Earle's salts, without L-glutamine and phenol red (Corning, USA), containing 10% fetal bovine serum (FBS) (Hyclone, USA) and 100UI/mL penicillin and streptomycin (Hyclone, UK). Cells were seeded at a density of 7 × 105 cells/cm2 before being harvested by trypsinization (Trypsin; Hyclone, USA) and counted under a microscope for subsequent experiments. Cell culture was kept at 37°C in an incubator containing 5% CO 2 . Cells were treated with varying concentrations of either E scaber or D scandens extract for 24 h (at 0.75, 1.5, or 3 mg/ml), or pre-treated with inhibitors SB203580, SP600125, LY294002, and U0126 for 1 h before plant extracts were added.

Plant Material Preparation
D scandens stems were obtained from Khao Chakan Arboretum, Sra Kaeo, Prachin Buri, and leaves of E scaber were obtained from Sakunothayan Arboretum, Wang Thong, Phitsanulok Province, Thailand. Leaves of E scaber and fresh stems of D scandens were thoroughly washed with tap water, cut into small pieces, and dried for 15 h. Extraction was performed using standard procedures. Materials were reduced to coarse powder and macerated with ethyl alcohol for 48 h, and the filtrate was evaporated using a Büchi B-490 rotary evaporator (Büchi Labortechnik AG, Flawil, Switzerland) and lyophilized with a freeze dryer (Labconco Corp., Kansas City, MO, USA) to obtain dried extract using a vacuum evaporator. Powdered forms of plant samples were stored at room temperature. Before each experiment, dried powder was dissolved in ethanol to obtain stock solutions of 300 mg/mL and diluted to varying concentrations. All chemicals were of analytical grade purity. Extracts were prepared fresh prior to treatment and filtered twice before use, once through a70 μm cell strainer (Corning, USA) and once through a0.45 μm sterileand endotoxin-free filter (Whatman, UK).

MTT Cytotoxicity Assay
MTT colorimetric assay was used to determine cell viability, as previously reported. 55 Cells were seeded at a density of 1 × 10 4 with fresh medium before treatment for a 24 h period with varying plant extracts prepared to concentrations of 0.375, 0.75, 1.5, and 3 mg/mL. MTT solution were added to each well for 2 h before dimethyl sulfoxide was added. Absorbance was measured at 570, 550, and 690 nm (blank).

FACS Analysis
Briefly, cells were trypsinized and washed with phosphatebuffered saline (Sigma Chemicals, USA), before washing with binding buffer, each time centrifuging for 10 min at 300×g. 10 μL Annexin V-FITC and 5 μL PI (1 μg/mL) per 1 × 10 6 cells were added according to the manufacturer's recommendations (cat. no. 130-092-052; Miltenyi Biotec, Germany) and mixed thoroughly before incubation. PI was added immediately before analysis with flow cytometry, as per previously reported. 56 Analysis was done by FACSDiva Version 6.1.3 (BD Biosciences, New Jersey, USA).

Nuclear Morphological Staining
Cells were seeded at 4 × 10 3 cells/chamber (Lab-Tek II Chamber Slide System, Sigma-Aldrich, USA) and were stained with 5 μg/mL Hoechst 33342 (cat. no. H3570; Thermo-Fisher Scientific, Waltham, MA, USA) for 30 min at 37˚C and fixed onto a microscope slide and examined under a fluorescence microscope as previously reported. 56 The number of cells exhibiting condensed nucleus was determined from random sampling of 3 × 10 2 cells per experiment.

Statistical Analysis
All experiments were performed in triplicate. Results were expressed as mean ± SD. The differences between multiple groups were assessed using one-way analysis of variance 58 followed by Tukey-Kramer method, and differences between 2 groups were assessed using Student's t-test. The level of significance was set at P < .05. All analyses were performed using SPSS 20.0 (SPSS Institute, Cary, NC, USA).


Femoral Bone Strength Prediction Using Isotopological B-Spline-Transformed Meshes

Finite element (FE) analysis can predict proximal human femoral strength. Automated meshing and identifying subregions with high relevance for strength prediction could reduce the laborious modeling process. Mesh morphing based on free-form registration provides a high level of automation and inherently creates isotopological meshes. The goals of this study were to investigate if FE models based on free-form transformed meshes predict experimental femoral strength as well as manually created FE models and to identify regions and parameters with highest correlation to femoral strength. Subject-specific meshes and FE models were created from a set of quantitative CT images (QCT) using a B-Spline registration-based algorithm. Correlation of FE-predicted bone strength and local parameters with experimental bone strength were investigated. FE models based on transformed meshes closely resembled manually created counterparts, with equally strong correlations with experimental bone strength (R2 = 0.81 vs. R2 = 0.80). The regional analysis showed strong correlations (0.6 < R2 < 0.7) of experimental strength with local parameters. No subregion or parameter lead to stronger correlation than FE predicted bone strength. B-spline-transformed meshes can be used to create FE models, able to predict femoral bone strength and simplify FE model generation. They can be used to reveal relations of local parameters with failure load.

Introduction
The prediction of bone strength has a high potential for bone quality assessment [1]. When it comes to patient health, imaging-based methods remain the best option, with computer tomography-based finite element (FE) simulations showing the highest correlation with experimental bone strength [2]. However, creating such models manually is expensive and requires a lot of expertise. It would therefore be beneficial to reduce the cost and time required for processing. This can be achieved by automating meshing in the model generation.
Mesh morphing methods have already been in use as alternative to manual meshing methods in FE model generation [3][4][5]. Usually, a template mesh from an atlas, a mean model with respect to shape and gray values, or a specimen close to the average, is transformed to fit the respective target specimen. These transformed meshes are inherently isotopological and can therefore be used for comparisons in various subregions of the bone such as the neck, or even element-wise statistical comparisons. While landmark-and surface-based methods for subject-specific mesh morphing are well documented [4,[6][7][8][9], they require the implementation of additional algorithms and methods, e.g., to identify landmarks.
Free-form registration-based methods can be applied directly to provided images and have also already been used for mesh morphing and mesh morphing-based FE models [7,[10][11][12][13]. However, to our knowledge, no study provided data on the accuracy of FE models based on such transformed meshes with respect to experimental bone strength. In a previous work, free-form registration-based statistical deformation and texture models (SDTMs) of proximal femora were successfully used to generate 3D FE models from 2D projections with high correlation to experimental results [14]. Although the FE models in that study were based on morphed meshes, no direct 3D-3D registrations were used to calculate correspondences.
Several parameters and sections of the femur have been investigated for their ability to predict fracture risk and bone strength [15][16][17][18][19]. Especially the femoral neck has long been of higher interest and is one of the distinct regions examined in dual energy X-ray absorptiometry (DEXA) [20,21]. However, DEXA-based bone mineral density (BMD) is a projected measurement and therefore does not entirely reflect three dimensional properties [22]. The accuracy of the method with respect to fracture-prediction has also been questioned [23]. Three-dimensional methods appear more promising [24]. Other parameters investigated in the past are bone volume to total volume (BV/TV) and stress and strain-related measurements [25][26][27]. However, again, information on their relation to experimentally measured bone strength is scarce.
Therefore, the potential of FE-based and morphometric parameters using isotopological B-spline-transformed meshes to predict experimental bone strength is investigated in this work. For this purpose, experimentally acquired bone strength measurements are correlated, first, with FE-predicted bone strength and, second, with local mechanical and morphological parameters. Conventional, manually created FE models serve as a control to reveal deviations in geometry or precision of the predicted bone strength in the FE models with transformed meshes.
In summary, in this work we would like to review how accurately isotopological meshes can predict femoral strength and if other morphometric surrogates computed based on these isotopological meshes are also able to predict experimental bone strength sufficiently.

Overview
A graphical overview of the study can be seen in Figure 1. In a previous study, proximal femora from body donations were scanned to obtain quantitative CT (QCT)images (Figure 1, left) and the bones afterwards tested in compressive loading until failure for bone strength [28,29]. A set of FE models based on manually created meshes was prepared [28] as control study (Figure 1, red). For the morphed mesh-based study (Figure 1, turquoise), similarity and B-spline-based free-form registrations were used to calculate an averaged image with respect to gray values and shape from the QCT set, called mean gray-level image in the latter. This was subsequently used to create the mean mesh, which was then transformed back to the respective specimens. These isotopological transformed meshes were compared with their manually created counterparts geometrically. Finally, the experimental bone strength was compared to the FE-predicted bone strength (both for the manually created FE models and with transformed meshes), and locally to mechanical parameters from the FE-simulation and morphometric parameters from the QCT images. were obtained from proximal human femora. "Study"-B-spline transformations were used to create a mean gray-level image and subsequently subject-specific QCT finite element (FE) models. FE models and local parameters were correlated with experimental bone strength. "Control"-manually created QCT FE models are compared to results from transformed meshes and corresponding FE models.

Data
The underlying dataset consisted of 37 QCT scans, corresponding binary masks that mark trabecular and cortical bone regions, and 32 compressive loading test results of proximal human femora from previous studies. Detailed descriptions of the dataset and tests can be found in Dall'Ara et al. [30], Dall'Ara et al. [28] and Steiner et al. [14]. In brief, samples from 19 females and 18 males, aged between 46 and 96 years, were scanned with a voxel size of 0.33 × 0.33 × 1.0 mm 3 with a clinical QCT scanner. Sets of binary masks covering trabecular and cortical bone were available for each scan from another study [29]. The scans and masks were coarsened to 0.66 × 0.66 × 0.66 mm 3 and afterwards equalized to a size of 161 × 118 × 282 voxels. The samples were tested in uniaxial compression in stance-like positions until failure. Only the QCT scans, masks, and the experimental data were used in this study, no additional measurements or experiments were performed on the donor material.

Finite Element Models
All FE models were created similar to Luisier et al. [31]. Material properties of the smooth mesh elements were assigned based on bone volume to total volume (BV/TV) values, using a user-defined material model (UMAT). All meshes consist of second order tetrahedral elements for trabecular regions and second order wedge elements for cortical regions with element sizes of about 5 mm. BV/TV was calculated from gray values and mapped to the meshes using the respective algorithm in medtool (Dr. Pahr Ingenieurs e.U., Pfaffstaetten, Austria) [32,33]. The UMAT in use has been described in Dall'Ara et al. [28], and in more detail in Schwiedrzik and Zysset [34] and Luisier et al. [31], and consists of a density-based isotropic damage model with exponential hardening and rate-independent viscosity. The assigned parameters of the material model were similar to Dall'Ara et al. [2] and Steiner et al. [14], and are given in Table 1. Additional embedding meshes for load distribution at the femoral head consisted of second order wedge elements with E = 150 MPa and ν = 0.3. The fixation of distal (bottom) nodes in all directions and a stepwise displacement of a reference node, kinematically coupled to the proximal (top) nodes of the embedding, acted as boundary conditions.

Mesh Transformation
A mean gray-level image was created from all specimens, which represents the mean 3D image of all registered and transformed QCT images. This mean gray-level image and the corresponding mean mask were calculated as described in Steiner et al. ([14], Appendix A), using all QCT images in a SimpleITK [35] (v1.0.1; http://www.simpleitk.org, (accessed on 11 March 2022)) and Python-based algorithm. In brief, the algorithm to generate the mean gray-level image picks a random image from the given set as initial reference. All images are registered with a free-form B-spline registration and mapped to the respective reference. Then, a mean gray-level image with respect to shape and gray values is calculated. The process is repeated with this gray-level image as new reference to avoid specimen related biases. The mean mask is generated by applying the yielded transformations to the respective masks of the set, calculating the mean and thresholding it at 25%. After meshing the mean gray-level image, the inverses of the acquired B-spline transformations are applied to this mean mesh to attain subject specific meshes. The entire set of 37 images was only used for the calculation of the mean gray-level image. All subsequent steps were performed using those specimens for which experimental data was available.
The agreement of B-spline-based meshes with manually created meshes was controlled qualitatively via visual inspection and quantitatively using average surface distances and volume differences between the mesh types for each specimen. Surface distances were computed using Euclidean distance transforms in SciPy 1.0.0 [36] applied to mesh voxelizations (0.22 mm voxel side length). The distance transforms were used to determine the distances of the voxels of one voxelization boundary to the other, and vice versa. The morphed meshes were finally used to create the corresponding FE models. Agreement of these FE models with the ones based on the manual method were controlled using a qualitative inspection of the damage and BV/TV distribution, and the correlation of simulated bone strength with experimental bone strength.

Regional Statistics
Regional statistical analyses are straightforward for isotopological meshes, which are comparing local morphometric parameters and local FE outcomes with experimental data. BV/TV, bone mineral content (BMC), von Mises stress and maximum absolute principal strain were selected as representative morphological and mechanical parameters due to their frequent use in literature and clinical environment (BV/TV, BMC) [37][38][39][40][41], and findings regarding their significance in bone quality assessment in other studies (stress, strain [26]).
For this, BV/TV values from all elements were multiplied by the volume of the respective element to calculate BMC. Values for von Mises stress and maximal absolute principal strain were extracted for each mesh element from the first frame of the FE simulations which was still within the linear elastic range of the model. The strain values were normalized by the respective reaction force of the reference points. The isotopology of the transformed meshes then allowed to correlate bone mineral content (BMC), von Mises stress, and maximum absolute principal strain over distinctive regions, and for each element as well, with the experimentally acquired bone strengths. Weighted and unweighted averages were calculated for BV/TV and BMC, respectively, within each region. Maximal regional values were used for von Mises stress and maximum absolute principal strain [42]. The investigated regions included the whole bone, the entire neck section, superior and inferior neck sections, and cortical and trabecular variations of these regions. Finally, the element-wise correlations with the experimental bone strength were evaluated.

Computational Details
Each B-Spline registration was preceded by a similarity registration, covering translation, rotation, and scaling. The B-spline registrations were executed with a control point grid-size of 5 × 5 × 5, for a total of (5 + 3) × (5 + 3) × (5 + 3) × 3 = 1536 degrees of freedom. A multi-resolution framework, as provided by SimpleITK, with coarsening factors (4, 2 and 1) and Gaussian filtering (σ = 2, 1 and 0), was applied. Here, the registration is performed in three subsequent steps with increasing image size and quality. This is done to speed up registration overall and to avoid local minima. Gradient descent line search and correlation were used as optimizer and metric/objective function, respectively. Each registration was initialized with parameter values equal to zero (scaling 1) and the findings from each step in the multi-resolution framework passed as initial values to the next iteration. This registration setup was used in the same way in Steiner et al. [14], where it delivered good and robust results. The non-zero positivity of the Jacobi determinants were continuously controlled throughout the registrations to preserve orientations and bijectivity.
The mean gray-level image was generated on an Intel Xeon CPU E5-2697 v3 with 2.6 GHz, 14 cores, and 28 threads.
The correlations were calculated in Python, using Scipy [36].

Accuracy of Transformed Meshes
The mean gray-level image was successfully calculated, requiring about 10 h in realtime. The mean mesh was successfully generated and transformed afterwards. A qualitative visual comparison of meshes created manually with the transformed ones is shown in Figure 2. The transformed meshes were generally very similar to their manually created counterparts while lacking details. The averaged surface distance between the two mesh types was 0.7 ± 0.6 mm. Boxplots of the surface distances for all specimens are shown in Figure 3.
The average volume difference was 4.1 ± 3.0%, with the transformed meshes generally showing lower volumes. A Bland-Altman plot comparing the volumes is given in Figure 4.
There, the relative difference with respect to the mean between the volumes of each specimen are plotted. The even distribution of the datapoints, parallel to the average difference, indicates that the differences between the respective volumes are independent of their size.

FE-Predicted Bone Strength
All FE models could be created and solved. Solving required about 10 min for each model. Figures 5 and 6 show comparisons of both modeling strategies with respect to BV/TV and damage, respectively. The outcomes look visually very similar to the ones based on manual meshing with respect to the spatial distribution of BV/TV and damage.   Figure 7 shows the scatter plots of simulated ultimate forces against experimental ultimate forces. The average simulated bone strength and correlation with respect to experimental force were 4159 ± 1731 N and R 2 = 0.80 for models based on manually created meshes, and 3697 ± 1567 N and R 2 = 0.81 for those with transformed meshes. A two-tailed paired t-test yielded p = 7 × 10 −11 , rejecting the null-hypothesis of equal means for a significance level of α = 0.05.

Regional Statistics
An overview of regional correlations with experimental ultimate forces is given in Figure 8. The bone is generally divided into trabecular ("T.") and cortical ("C.") sections, as well as whole, superior ("Sup.") and inferior ("Inf.") neck. A cropped section of the bone without shaft was also analyzed. Higher correlations (R 2 ≥ 0.65) were found for BMC and maximum absolute principal strain and in the inferior neck region. Figure 8. Correlations of the respective averaged (BV/TV and bone mineral content (BMC)) and maximal (von Mises and maximum absolute principal strain) values of each specified region with experimental ultimate force. Higher values were found for BMC, and maximum absolute principal strain especially in the inferior neck region. The abbreviations Sup., Inf., C., and T. indicate superior, inferior, cortical, and trabecular sections, respectively, as well as combinations thereof.
Regional statistics based on single element-level results can be seen in Figure 9, showing mean, standard deviation (std), and correlation with experimental ultimate forces, per element, in the first three columns. The last column shows the 10% of all elements which have the highest correlation with experimental ultimate force. Again, generally high correlations (R 2 > 0.6) were found for BMC and maximum absolute principal strain, and in the inferior neck region. Additionally, high correlations can be seen in the head region for BMC and maximum absolute principal strain, and in the shaft cortex for BV/TV and von Mises stress. Higher correlations were also found in the greater trochanter region.

Discussion
This study demonstrated the potential of FE models, based on meshes morphed with B-Spline transformations, to predict bone strength in the proximal human femur. After a one-time creation of the mean gray-level image, this approach allows a fully automated and robust creation of QCT FE models. Such models show practically the same predictability as classical, manually created FE models. Furthermore, the isotopological meshes allow the analysis of local morphological and simulated mechanical parameters within similar anatomical regions without any further effort.
FE models based on transformed meshes closely matched those based on manually created meshes, both in terms of the mesh and mechanical predictions. The correlation with experimental bone strength was similarly high with both model versions. The average ultimate loads were slightly lower for the models based on transformed meshes, as seen in Figure 7. This could be attributed to their generally lower volumes. However, correlation between differences in ultimate loads and differences in volume was found to be R 2 = 0.207 in a subsequent test. The experimental bone strengths were consistently higher than the FE model predictions, which is similar to previous studies on the same dataset [28,31,43]. We found a similarly high correlation in our previous study on the same dataset [14] when estimating models from 2D projections (R 2 = 0.835). For comparison, Grassi et al. [8] found higher correlations (R 2 > 0.9) using manual selection of landmarks and radial basis functions to transform meshes for femoral FE models. However, instead of experimental and simulated bone strength, they compared strains, and had a smaller sample size of eight, limiting the comparability to this work. The similarity of manually created and transformed meshes is apparent in the low values for average surface distance and volumetric difference, with 0.7 ± 0.5 mm and 4.1 %. Similar results were found by Baldwin et al. [44] who reported a root mean square distance of 0.9 ± 0.5 mm between manually created and surface-based transformed meshes on femora. The findings suggest that this method can be used for the automatic creation of meshes for FE models with a similarly high quality as manually created ones. In contrast to the landmark-based methods of, e.g., Grassi et al. [8], the B-Spline registration-based method can be applied directly to the QCT images and might therefore further accelerate and ease model creation.
In case of local parameters, none of the investigated parameters or subregions showed higher correlation with experimental bone strength than that of QCT FE-based bone strength. However, the regional statistical analysis demonstrates the potential of this method and gives further insights into the distribution of morphometrical parameters in the femur. Correlation results for BV/TV and BMC in the neck regions (R 2 = 0.53 and R 2 = 0.62) were comparable to those found by Dall'Ara et al. [30] for dual-energy X-ray absorptiometry (DEXA)-based femoral neck BMD and BMC on the same dataset (R 2 = 0.66 and R 2 = 0.67). The differences in the results could be attributed to slight deviations of the evaluated regions and to differences in their calculation. This is especially the case for BMD, which is calculated as an areal density value using DXA, in contrast to the volumetric parameter BV/TV. Variations in the preparation of the images could contribute to the differences too. In general, the regional and elemental results showed higher correlations for BMC and maximal absolute principal strain. Higher correlations were also found in the inferior neck region. Iori et al. [27] contrarily found higher values in the superior part, however, their study was conducted on femora in a sideways fall loading scenario. Additionally, with respect to the prediction of femoral fractures, Poole et al. [45] found that women with femoral neck fractures had focal osteoporosis in the superior neck region when compared to healthy controls. This could indicate a central role of structures in compressive load for ultimate load prediction, while the condition of the superior neck region reflects the actual fracture risk. Finally, high correlations were also found in the greater trochanter region. These could be attributed to a general adaptation of the bone to its use, given that the trochanter is, among others, the onset of most gluteal muscles. DXA BMD of the greater trochanter also showed the highest correlation with femoral strength in a study by Cheng et al. [46] in a sideways-fall test.
The study has some limitations. The QCT images were already masked and preprocessed. Although in principle using unprocessed images would be possible, noise and unevenly cropped and oriented bones constitute additional challenges which were not investigated in this study. Furthermore, only proximal human femora were used. The applicability of the method to other bones needs to be proven. The femora were tested and analyzed in a stance-like loading scenario. For more general statements with respect to the relevance of parameters in different regions, several additional loading scenarios should be examined, especially those simulating sideways fall. Finally, the used boundary conditions primarily reflect the experimental conditions, but not necessarily the physiological loading conditions. Especially the regional statistical analysis of the mechanical parameters must therefore be interpreted under careful consideration of this limitation.
In summary, B-spline-transformed meshes can be used to further automate FE model generation while preserving precision in femoral strength prediction. While the investigated local parameters in various subregions of the bone could not reach the precision of FE models of the entire femur to predict bone strength, the isotopology of the transformed meshes still allowed to highlight anatomical regions of strongest correlation.  Institutional Review Board Statement: The data for this work was collected by Dall'Ara et al. [30], which was approved by the ethical committee of the Medical University Vienna (EK Nr 175/2011). No additional experiments were carried out for the present study.