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The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states.
A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98).
The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method.
Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.
Objective: To assess the influence of biphasic calcium phosphate materials with different surface topographies on bone formation and osseointegration of titanium implants in standardized alveolar ridge defects.
Materials and methods: Standardized alveolar ridge defects (6 × 6 mm) were created in the mandible of 8 minipigs and filled with three biphasic calcium phosphate materials (BCP1–3, 90% tricalcium phosphate/10% hydroxyapatite) with different surface properties (micro- and macroporosities) as well as a bovine-derived natural bone mineral (NBM) as a control. At 12 weeks, implants were placed into the augmented defects. After further 8 weeks of healing, dissected blocks were processed for histological analysis (e.g., mineralized (MT), residual bone graft material (BS), bone-to-implant contact (BIC)).
Results: All four biomaterials showed well-integrated graft particles and new bone formation within the defect area. MT values were comparable in all groups. BS values were highest in the NBM group (21.25 ± 13.52%) and markedly reduced in the different BCP groups, reaching statistical significance at BCP1-treated sites (9.2 ± 3.28%). All test and control groups investigated revealed comparable and statistically not significant different BIC values, ranging from 73.38 ± 20.5% (BCP2) to 84.11 ± 7.84% (BCP1), respectively.
Conclusion* All bone graft materials facilitated new bone formation and osseointegration after 12 + 8 weeks of healing.
Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.
The inclusive charged particle transverse momentum distribution is measured in proton–proton collisions at s=900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (|η|<0.8) over the transverse momentum range 0.15<pT<10 GeV/c. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for |η|<0.8 is 〈pT〉INEL=0.483±0.001 (stat.)±0.007 (syst.) GeV/c and 〈pT〉NSD=0.489±0.001 (stat.)±0.007 (syst.) GeV/c, respectively. The data exhibit a slightly larger 〈pT〉 than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET.
Photolabile protecting groups are widely used to trigger oligonucleotide activity. The ON/OFF‐amplitude is a critical parameter. An experimental setup has been developed to identify protecting group derivatives with superior caging properties. Bulky rests are attached to the cage moiety via Cu‐catalyzed azide–alkyne cycloaddition post‐synthetically on DNA. Interestingly, the decrease in melting temperature upon introducing o‐nitrobenzyl‐caged (NPBY‐) and diethylaminocoumarin‐cages (DEACM‐) in DNA duplexes reaches a limiting value. NMR spectroscopy was used to characterize individual base‐pair stabilities and determine experimental structures of a selected number of photocaged DNA molecules. The experimental structures agree well with structures predicted by MD simulations. Combined, the structural data indicate that once a sterically demanding group is added to generate a tri‐substituted carbon, the sterically less demanding cage moiety points towards the neighboring nucleoside and the bulkier substituents remain in the major groove.
he most basic behavioural states of animals can be described as active or passive. While high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which a combination of automatic radiotracking and machine learning is used to distinguish between active and passive behaviour in small vertebrates fitted with lightweight transmitters (<0.4 g).
We used a dataset containing >3 million signals from very-high-frequency (VHF) telemetry from two forest-dwelling bat species (Myotis bechsteinii [n = 52] and Nyctalus leisleri [n = 20]) to train and test a random forest model in assigning either active or passive behaviour to VHF-tagged individuals. The generalisability of the model was demonstrated by recording and classifying the behaviour of tagged birds and by simulating the effect of different activity levels with the help of humans carrying transmitters. The model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F1 0.96–0.98).
We provide an ecological case-study demonstrating the potential of this automated monitoring tool. We used the trained models to compare differences in the daily activity patterns of two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that subtle differences in the timing of species' activity can be distinguished using our method.
Our approach can classify VHF-signal patterns into fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radiotracking method, we provide the trained random forest models together with an R package that includes all necessary data processing functionalities. In combination with state-of-the-art open-source automated radiotracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.
The integrated luminosities of data samples collected in the BESIII experiment in 2016–2017 at center-of-mass energies between 4.19 and 4.28 GeV are measured with a precision better than 1% by analyzing large-angle Bhabha scattering events. The integrated luminosities of old datasets collected in 2010–2014 are updated by considering corrections related to detector performance, offsetting the effect of newly discovered readout errors in the electromagnetic calorimeter, which can haphazardly occur.
The activity of the Salt inducible kinase 2 (SIK2), a member of the AMP-activated protein kinase (AMPK)-related kinase family, has been linked to several biological processes that maintain cellular and energetic homeostasis. SIK2 is overexpressed in several cancers, including ovarian cancer, where it promotes the proliferation of metastases. Furthermore, as a centrosome kinase, SIK2 has been shown to regulate the G2/M transition, and its depletion sensitizes ovarian cancer to paclitaxel-based chemotherapy. Here, we report the consequences of SIK2 inhibition on mitosis and synergies with paclitaxel in ovarian cancer using a novel and selective inhibitor, MRIA9. We show that MRIA9-induced inhibition of SIK2 blocks the centrosome disjunction, impairs the centrosome alignment, and causes spindle mispositioning during mitosis. Furthermore, the inhibition of SIK2 using MRIA9 increases chromosomal instability, revealing the role of SIK2 in maintaining genomic stability. Finally, MRIA9 treatment enhances the sensitivity to paclitaxel in 3D-spheroids derived from ovarian cancer cell lines and ovarian cancer patients. Our study suggests selective targeting of SIK2 in ovarian cancer as a therapeutic strategy for overcoming paclitaxel resistance.