Gold Open-Access
Refine
Year of publication
- 2022 (745) (remove)
Document Type
- Article (659)
- Monograph/Edited Volume (18)
- Review (12)
- Master's Thesis (10)
- Other (9)
- Conference Proceeding (8)
- Doctoral Thesis (8)
- Part of Periodical (6)
- Bachelor Thesis (5)
- Part of a Book (5)
Language
- English (610)
- German (128)
- Spanish (5)
- French (1)
- Multiple languages (1)
Keywords
- machine learning (8)
- Tolkien (7)
- climate change (7)
- COVID-19 (6)
- Germany (6)
- exercise (6)
- diabetes (5)
- permafrost (5)
- Lateinunterricht (4)
- SARS-CoV-2 (4)
Institute
- Institut für Biochemie und Biologie (131)
- Extern (122)
- Institut für Physik und Astronomie (75)
- Institut für Geowissenschaften (71)
- Institut für Umweltwissenschaften und Geographie (50)
- Historisches Institut (41)
- Department Sport- und Gesundheitswissenschaften (38)
- Strukturbereich Kognitionswissenschaften (35)
- Institut für Ernährungswissenschaft (29)
- Hasso-Plattner-Institut für Digital Engineering GmbH (27)
We have directly resolved in the present work the interfacial composition during and after the interactions of a saturated atmosphere of oil vapor with soluble surfactant solutions at a planar water/air interface for the first time. Experiments were conducted on interactions of hexane vapor with solutions of alkyltrimethylammonium bromides and sodium dodecyl sulfate to observe the balance between cooperativity and competition of the components at the interface.
In all cases, hexane adsorption was strongly enhanced by the presence of the surfactant, even at bulk surfactant concentrations four orders of magnitude below the critical micelle concentration. Cooperativity of the surfactant adsorption was observed only for sodium dodecyl sulfate at intermediate bulk concentrations, yet for all four systems, competition set in at higher concentrations, as hexane adsorption reduced the surfactant surface excess. The data fully supported the complete removal of hexane from the interface following venting of the system to remove the saturated atmosphere of oil vapor.
These results help to identify future experiments that would elaborate and could explain the cooperativity of surfactant adsorption, such as on cationic surfactants with short alkyl chains and a broader series of anionic surfactants. This work holds relevance for oil recovery applications with foam, where there is a gas phase saturated with oil vapor.
The human language processing mechanism assigns a structure to the incoming materials as they unfold. There is evidence that the parser prefers some attachment types over others; however, theories of sentence processing are still in dispute over the stage at which each source of information contributes to the parsing system. The present study aims to identify the nature of initial parsing decisions during sentence processing through manipulating attachment type and verbs' argument structure. To this end, we designed a self-paced reading task using globally ambiguous constructions in Dutch. The structures included double locative prepositional phrases (PPs) where the first PP could attach both to the verb (high attachment) and the noun preceding it (low attachment). To disambiguate the structures, we presented a visual context in the form of short animation clips prior to each reading task. Furthermore, we manipulated the argument structure of the sentences using 2- and 3-argument verbs. The results showed that parsing decisions were influenced by contextual cues depending on the argument structure of the verb. That is, the visual context overcame the preference for high attachment only in the case of 2-argument verbs, while this preference persisted in structures including 3-argument verbs as represented by longer reading times for the low attachment interpretations. These findings can be taken as evidence that our language processing system actively integrates information from linguistic and non-linguistic sources from the initial stages of analysis to build up meaning. We discuss our findings in light of serial and parallel models of sentence processing.
Describing the heterogeneous structure of forests is often challenging.
One possibility is to analyze forest biomass in different plots and to derive plot-based frequency distributions.
However, these frequency distributions depend on the plot size and thus are scale dependent.
This study provides insights about transferring them between scales. Understanding the effects of scale on distributions of biomass is particularly important for comparing information from different sources such as inventories, remote sensing and modeling, all of which can operate at different spatial resolutions. Reliable methods to compare results of vegetation models at a grid scale with field data collected at smaller scales are still missing.
The scaling of biomass and variables, which determine the forest biomass, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared.
Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process was tested.
Scaling exponents of about 0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of 0.3. Lognormal and gamma distribution functions fitted with the moment matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived scaling relationships.
The study demonstrates a way of how to approach the scaling problem in model-data comparisons by providing a transfer relationship. Further research is needed for a better understanding of the mechanisms that shape the frequency distributions at the different scales.
The current study examined the impact of the Good Behavior Game (GBG) on the academic engagement (AE) and disruptive behavior (DB) of at-risk students' in a German inclusive primary school sample using behavioral progress monitoring.
A multiple baseline design across participants was employed to evaluate the effects of the GBG on 35 primary school students in seven classrooms from grade 1 to 3 (M-age = 8.01 years, SDage = 0.81 years).
The implementation of the GBG was randomly staggered by 2 weeks across classrooms. Teacher-completed Direct Behavior Rating (DBR) was applied to measure AE and DB. We used piecewise regression and a multilevel extension to estimate the individual case-specific treatment effects as well as the generalized effects across cases.
Piecewise regressions for each case showed significant immediate treatment effects for the majority of participants (82.86%) for one or both outcome measures.
The multilevel approach revealed that the GBG improved at-risk students' classroom behaviors generally with a significant immediate treatment effect across cases (for AE, B = 0.74, p < 0.001; for DB, B = -1.29, p < 0.001).
The moderation between intervention effectiveness and teacher ratings of students' risks for externalizing psychosocial problems was significant for DB (B = -0.07, p = 0.047) but not for AE.
Findings are consistent with previous studies indicating that the GBG is an appropriate classroom-based intervention for at-risk students and expand the literature regarding differential effects for affected students.
In addition, the study supports the relevance of behavioral progress monitoring and data-based decision-making in inclusive schools in order to evaluate the effectiveness of the GBG and, if necessary, to modify the intervention for individual students or the whole group.
The desiccation of the Aral Sea represents one of the largest human-made environmental regional disasters. The salt- and toxin-enriched dried-out basin provides a natural laboratory for studying ecosystem functioning and rhizosphere assembly under extreme anthropogenic conditions.
Here, we investigated the prokaryotic rhizosphere communities of the native pioneer plant Suaeda acuminata (C.A.Mey.) Moq. in comparison to bulk soil across a gradient of desiccation (5, 10, and 40 years) by metagenome and amplicon sequencing combined with quantitative PCR (qPCR) analyses. The rhizosphere effect was evident due to significantly higher bacterial abundances but less diversity in the rhizosphere compared to bulk soil. Interestingly, in the highest salinity (5 years of desiccation), rhizosphere functions were mainly provided by archaeal communities.
Along the desiccation gradient, we observed a significant change in the rhizosphere microbiota, which was reflected by (i) a decreasing archaeon-bacterium ratio, (ii) replacement of halophilic archaea by specific plant-associated bacteria, i.e., Alphaproteobacteria and Actinobacteria, and (iii) an adaptation of specific, potentially plant-beneficial biosynthetic pathways.
In general, both bacteria and archaea were found to be involved in carbon cycling and fixation, as well as methane and nitrogen metabolism.
Analysis of metagenome-assembled genomes (MAGs) showed specific signatures for production of osmoprotectants, assimilatory nitrate reduction, and transport system induction.
Our results provide evidence that rhizosphere assembly by cofiltering specific taxa with distinct traits is a mechanism which allows plants to thrive under extreme conditions. Overall, our findings highlight a function-based rhizosphere assembly, the importance of plant-microbe interactions in salinated soils, and their exploitation potential for ecosystem restoration approaches.IMPORTANCE
The desertification of the Aral Sea basin in Uzbekistan and Kazakhstan represents one of the most serious anthropogenic environmental disasters of the last century. Since the 1960s, the world's fourth-largest inland body of water has been constantly shrinking, which has resulted in an extreme increase of salinity accompanied by accumulation of many hazardous and carcinogenic substances, as well as heavy metals, in the dried-out basin.
Here, we investigated bacterial and archaeal communities in the rhizosphere of pioneer plants by combining classic molecular methods with amplicon sequencing as well as metagenomics for functional insights.
By implementing a desiccation gradient, we observed (i) remarkable differences in the archaeon-bacterium ratio of plant rhizosphere samples, (ii) replacement of archaeal indicator taxa during succession, and (iii) the presence of specific, potentially plant-beneficial biosynthetic pathways in archaea present during the early stages.
In addition, our results provide hitherto-undescribed insights into the functional redundancy between plant-associated archaea and bacteria.
The desertification of the Aral Sea basin in Uzbekistan and Kazakhstan represents one of the most serious anthropogenic environmental disasters of the last century.
Since the 1960s, the world's fourth-largest inland body of water has been constantly shrinking, which has resulted in an extreme increase of salinity accompanied by accumulation of many hazardous and carcinogenic substances, as well as heavy metals, in the dried-out basin.
The administrative language used in imperial and city chanceries illustrates formal language use in the Early Modern period, as most evident in its syntactic complexity. Since administrative language was considered prestigious by the literate people of the time, the syntactic features in question are increasingly found in other text types as well (Lötscher 1995, Schwitalla 2002). The present paper investigates early newspapers published in the seventeenth and eighteenth centuries to evalute their degree of syntactic complexity and hence the extent of formal language used. Contrary to common belief (Admoni 1980, von Polenz 2013), it will be shown that early newspapers do not allow a uniform assessment in terms of their syntactic complexity, when they emerge as a new genre in the seventeenth century: some news segments display a fairly simple syntax, whereas others are of high syntactic complexity. By the end of the eighteenth century, the growing conventionalization of the new genre as well as the impact of standardization processes render newspapers much more balanced in terms of syntactic complexity. Unlike previous work on the syntactic complexity of newspaper language, the measurement of syntactic complexity takes into account not only sentence length and the relationship between independent and dependent clauses, but also the placement of adverbial clauses in relation to their associated clause.
Mental health of Japanese workers: amotivation mediates self-compassion on mental health problems
(2022)
Workplace mental health is a cause for concern in many countries. Globally, 78% of the workforce experienced impairment of their mental health in 2020. In Japan, more than half of employees are mentally distressed.
Previously, research has identified that self-compassion (i.e., being kind and understanding towards oneself) and work motivation were important to their mental health.
However, how these three components relate to each other remains to be elucidated. Accordingly, this study aimed to examine the relationship between mental health problems, self-compassion and work motivation (i.e., intrinsic motivation, extrinsic motivation and amotivation).
A cross-sectional design was employed, where 165 Japanese workers completed self-report scales regarding those three components. A correlation and path analyses were conducted.
Mental health problems were positively associated with amotivation and negatively associated with age and self-compassion. While intrinsic motivation and extrinsic motivation did not mediate the impact of self-compassion on mental health problems, amotivation did.
The findings can help managers and organizational psychologists help identify effective approaches to improving work mental health.
Fungal biotransformation is an attractive synthetic strategy to produce highly specific compounds with chemical functionality in regions of the carbon skeleton that are not easily activated by conventional organic chemistry methods.
In this work, Cladosporium antarcticum isolated from sediments of Glacier Collins in Antarctica was used to obtain novel drimane sesquiterpenoids alcohols with activity against Candida yeast from drimendiol and epidrimendiol. These compounds were produced by the high-yield reduction of polygodial and isotadeonal with NaBH4 in methanol.
Cladosporium antarcticum produced two major products from drimendiol, identified as 9 alpha-hydroxydrimendiol (1, 41.4 mg, 19.4% yield) and 3 beta-hydroxydrimendiol (2, 74.8 mg, 35% yield), whereas the biotransformation of epidrimendiol yielded only one product, 9 beta-hydroxyepidrimendiol (3, 86.6 mg, 41.6% yield).
The products were purified by column chromatography and their structure elucidated by NMR and MS. The antifungal activity of compounds 1-3 was analyzed against Candida albicans, C. krusei and C. parapsilosis, showing that compound 2 has a MIC lower than 15 mu g/mL against the three-pathogenic yeast.
In silico studies suggest that a possible mechanism of action for the novel compounds is the inhibition of the enzyme lanosterol 14 alpha-demethylase, affecting the ergosterol synthesis.
Materials realizing the XY model in two dimensions are sparse.
Here we use neutron triple-axis spectroscopy to investigate the critical static and dynamical magnetic fluctuations in the square-lattice antiferromagnets Ca2RuO4 and Ca3Ru2O7.
We probe the temperature dependence of the antiferromagnetic Bragg intensity, the Q width, the amplitude, and the energy width of the magnetic diffuse scattering in the vicinity of the Neel temperature T-N to determine the critical behavior of the magnetic order parameter M, correlation length xi, susceptibility chi, and the characteristic energy Gamma with the corresponding critical exponents beta, nu, gamma, and z, respectively.
We find that the critical behaviors of the single-layer compound Ca2RuO4 follow universal scaling laws that are compatible with predictions of the two-dimensional (2D) XY model.
The bilayer compound Ca3Ru2O7 is only partly consistent with the 2D XY theory and best described by the three-dimensional (3D) Ising model, which is likely a consequence of the intrabilayer exchange interactions in combination with an orthorhombic single-ion anisotropy.
Hence, our results suggest that layered ruthenates are promising solid-state platforms for research on the 2D XY model and the effects of 3D interactions and additional spin-space anisotropies on the magnetic fluctuations.
Mitochondrial stress-induced GFRAL signaling controls diurnal food intake and anxiety-like behavior
(2022)
Growth differentiation factor 15 (GDF15) is a mitochondrial stressinduced cytokine that modulates energy balance in an endocrine manner.
However, the importance of its brainstem-restricted receptor GDNF family receptor alpha-like (GFRAL) to mediate endocrine GDF15 signaling to the brain uponmitochondrial dysfunction is still unknown. Using a mouse model with muscle-specific mitochondrial dysfunction, we here show that GFRAL is required for activation of systemic energy metabolism via daytime-restricted anorexia but not responsible for muscle wasting.
We further find that muscle mitochondrial stress response involves a GFRAL-dependent induction of hypothalamic corticotropin-releasing hormone, without elevated corticosterone levels.
Finally, we identify that GFRAL signaling governs an anxiety-like behavior in male mice with muscle mitochondrial dysfunction, with females showing a less robust GFRAL-dependent anxiety-like phenotype.
Together, we here provide novel evidence of a mitochondrial stress-induced muscle-brain crosstalk via the GDF15-GFRAL axis to modulate food intake and anxiogenic behavior.
A key in controlling the SARS-CoV-2 pandemic is the assessment of the immune status of the population. We explored the utility of SARS-CoV-2 virus-like particles (VLPs) as antigens to detect specific humoral immune reactions in an enzyme-linked immunosorbent assay (ELISA).
For this purpose, SARS-CoV-2 VLPs were produced from an engineered cell line and characterized by Western blot, ELISA, and nanoparticle tracking analysis.
Subsequently, we collected 42 serum samples from before the pandemic (2014), 89 samples from healthy subjects, and 38 samples from vaccinated subjects. Seventeen samples were collected less than three weeks after infection, and forty-four samples more than three weeks after infection.
All serum samples were characterized for their reactivity with VLPs and the SARS-CoV-2 N- and S-protein.
Finally, we compared the performance of the VLP-based ELISA with a certified in vitro diagnostic device (IVD). In the applied set of samples, we determined a sensitivity of 95.5% and a specificity of 100% for the certified IVD.
There were seven samples with an uncertain outcome. Our VLP-ELISA demonstrated a superior performance, with a sensitivity of 97.5%, a specificity of 100%, and only three uncertain outcomes.
This result warrants further research to develop a certified IVD based on SARS-CoV-2 VLPs as an antigen.
The magnitude of earthquakes on continental normal faults rarely exceeds 7.0 Mw. However, because of their vicinity to large population centers they can be highly destructive.
Long recurrence time, relatively small deformations, and limited observations hinder our understanding of the deformation patterns and mechanisms controlling the magnitude of events.
Here, this problem is addressed with 2D thermomechanical modeling of normal fault seismic cycles.
The 2020 Samos, Greece Mw7.0 earthquake is used as an example as it is one of the largest and most studied continental normal fault earthquakes. The modeling approach employs visco-elasto-plastic rheology, compressibility, free surface, and a rate-and-state friction law for the fault.
Modeling of the Samos earthquake suggests the pore fluid pressure ratio on the fault ranges from 0 to 0.7. The model demonstrates that most of the deformation during interseismic and coseismic periods, besides on the fault, occurs in the hanging wall and footwall below the seismogenic part of the fault. The largest vertical surface displacement during the earthquake is the subsidence of the hanging wall in the vicinity of the fault, while the uplift of the footwall and remote part of the hanging wall is significantly smaller.
Modeling of the seismic cycles on normal faults with different setups shows the dependency of the magnitude on the thermal profile and dipping angle of the fault; low heat flow and low dipping angle are favorable conditions for the largest events, while steep normal faults in the areas of high heat flow tend to have the smallest magnitudes.
Background Mass gatherings (MGs) such as music festivals and sports events have been associated with a high risk of SARS-CoV-2 transmission. On-site research can foster knowledge of risk factors for infections and improve risk assessments and precautionary measures at future MGs. We tested a web-based participatory disease surveillance tool to detect COVID-19 infections at and after an outdoor MG by collecting self-reported COVID-19 symptoms and tests. Methods We conducted a digital prospective observational cohort study among fully immunized attendees of a sports festival that took place from September 2 to 5, 2021 in Saxony-Anhalt, Germany. Participants used our study app to report demographic data, COVID-19 tests, symptoms, and their contact behavior. This self-reported data was used to define probable and confirmed COVID-19 cases for the full "study period" (08/12/2021 - 10/31/2021) and within the 14-day "surveillance period" during and after the MG, with the highest likelihood of an MG-related COVID-19 outbreak (09/04/2021 - 09/17/2021). Results A total of 2,808 of 9,242 (30.4%) event attendees participated in the study. Within the study period, 776 individual symptoms and 5,255 COVID-19 tests were reported. During the 14-day surveillance period around and after the MG, seven probable and seven PCR-confirmed COVID-19 cases were detected. The confirmed cases translated to an estimated seven-day incidence of 125 per 100,000 participants (95% CI [67.7/100,000, 223/100,000]), which was comparable to the average age-matched incidence in Germany during this time. Overall, weekly numbers of COVID-19 cases were fluctuating over the study period, with another increase at the end of the study period. Conclusion COVID-19 cases attributable to the mass gathering were comparable to the Germany-wide age-matched incidence, implicating that our active participatory disease surveillance tool was able to detect MG-related infections. Further studies are needed to evaluate and apply our participatory disease surveillance tool in other mass gathering settings.
Magma-filled dikes may feed erupting fissures that lead to alignments of craters developing at the surface, yet the details of activity and migrating eruptions at the crater row are difficult to monitor and are hardly understood.
The 2021 Tajogaite eruption at the Cumbre Vieja, La Palma (Spain), lasted 85 days and developed a pronounced alignment of craters that may be related to changes within the volcano edifice.
Here, we use COSMO-SkyMed satellite radar data and ground-based time-lapse photographs, offering a high-resolution dataset to explore the locations and characteristics of evolving craters.
Our results show that the craters evolve both gradually and suddenly and can be divided into three main phases. Phase 1, lasting the first 6 weeks of the eruption, was characterized by a NW-SE linear evolution of up to seven craters emerging on the growing cone.
Following two partial collapses of the cone to the northwest and a seismicity increase at depth, Phase 2 started and caused a propagation of the main activity toward the southeastern side, together with the presence of up to 11 craters along this main NW-SE trend. Associated with strong deep and shallow earthquakes, Phase 3 was initiated and continued for the final 2 weeks of the eruption, expressed by the development of up to 18 craters, which became dominant and clustered in the southeastern sector in early December 2021. In Phase 3, a second and oblique alignment and surface fracture was identified.
Our findings that crater and eruption changes coincide together with an increase in seismic activity at depth point to a deep driver leading to crater and morphology changes at the surface.
These also suggest that crater distributions might allow for improved monitoring of changes occurring at depth, and vice versa, such that strong seismicity changes at depth may herald the migration and new formation of craters, which have major implications for the assessment of tephra and lava flow hazards on volcanoes.
Drimane sesquiterpene aldehydes control Candida yeast isolated from candidemia in Chilean patients
(2022)
Drimys winteri J.R. (Winteraceae) produce drimane sesquiterpenoids with activity against Candida yeast.
In this work, drimenol, polygodial (1), isotadeonal (2), and a new drimane alpha,beta-unsaturated 1,4-dialdehyde, named winterdial (4), were purified from barks of D. winteri. The oxidation of drimenol produced the monoaldehyde drimenal (3).
These four aldehyde sesquiterpenoids were evaluated against six Candida species isolated from candidemia patients in Chilean hospitals.
Results showed that 1 displays fungistatic activity against all yeasts (3.75 to 15.0 mu g/mL), but irritant effects on eyes and skin, whereas its non-pungent epimer 2 has fungistatic and fungicide activities at 1.9 and 15.0 mu g/mL, respectively.
On the other hand, compounds 3 and 4 were less active.
Molecular dynamics simulations suggested that compounds 1-4 are capable of binding to the catalytic pocket of lanosterol 14-alpha demethylase with similar binding free energies, thus suggesting a potential mechanism of action through the inhibition of ergosterol synthesis. According to our findings, compound 2 appears as a valuable molecular scaffold to pursue the future development of more potent drugs against candidiasis with fewer side effects than polygodial.
These outcomes are significant to broaden the alternatives to treat fungal infections with increasing prevalence worldwide using natural compounds as a primary source for active compounds.
Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods.
So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap.
So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet.
However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC.
Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal.
Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling.
We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68.
It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution.
However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNSbased SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression.
Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site.
When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types.
Background
In cystic fibrosis (CF), acute respiratory exacerbations critically enhance pulmonary destruction. Since these mainly occur outside regular appointments, they remain unexplored. We previously elaborated a protocol for home-based upper airway (UAW) sampling obtaining nasal-lavage fluid (NLF), which, in contrast to sputum, does not require immediate processing. The aim of this study was to compare UAW inflammation and pathogen colonization during stable phases and exacerbations in CF patients and healthy controls.
Methods
Initially, we obtained NLF by rinsing 10 ml of isotonic saline/nostril during stable phases. During exacerbations, subjects regularly collected NLF at home. CF patients directly submitted one aliquot for microbiological cultures. The remaining samples were immediately frozen until transfer on ice to our clinic, where PCR analyses were performed and interleukin (IL)-1 beta/IL-6/IL-8, neutrophil elastase (NE), matrix metalloproteinase (MMP)-9, and tissue inhibitor of metalloproteinase (TIMP)-1 were assessed.
Results
Altogether, 49 CF patients and 38 healthy controls (HCs) completed the study, and 214 NLF samples were analyzed. Of the 49 CF patients, 20 were at least intermittently colonized with P. aeruginosa and received azithromycin and/or inhaled antibiotics as standard therapy. At baseline, IL-6 and IL-8 tended to be elevated in CF compared to controls. During infection, inflammatory mediators increased in both cohorts, reaching significance only for IL-6 in controls (p=0.047). Inflammatory responses tended to be higher in controls [1.6-fold (NE) to 4.4-fold (MMP-9)], while in CF, mediators increased only moderately [1.2-1.5-fold (IL-6/IL-8/NE/TIMP-1/MMP-9)]. Patients receiving inhalative antibiotics or azithromycin (n=20 and n=15, respectively) revealed lower levels of IL-1 beta/IL-6/IL-8 and NE during exacerbation compared to CF patients not receiving those antibiotics. In addition, CF patients receiving azithromycin showed MMP-9 levels significantly lower than CF patients not receiving azithromycin at stable phase and exacerbation. Altogether, rhinoviruses were the most frequently detected virus, detected at least once in n=24 (49.0%) of the 49 included pwCF and in n=26 (68.4%) of the 38 healthy controls over the 13-month duration of the study. Remarkably, during exacerbation, rhinovirus detection rates were significantly higher in the HC group compared to those in CF patients (65.8% vs. 22.4%; p<0.0001).
Conclusion
Non-invasive and partially home-based UAW sampling opens new windows for the assessment of inflammation and pathogen colonization in the unified airway system.
Simple Summary
Urokinase-type plasminogen activator (urokinase, uPA) is a widely discussed biomarker for cancer prognosis and diagnosis. The gold standard for the determination of protein biomarkers in physiological samples is the enzyme-linked immunosorbent assay (ELISA). Here, antibodies are used to detect the specific protein.
In our study, recently published urokinase aptamers were tested for their use in a sandwich assay format as alternative specific recognition elements. Different aptamer combinations were used for the detection of uPA in a sandwich-assay format and a combination of aptamers and antibodies additionally allowed the differentiation of human high and low molecular weight- (HMW- and LMW-) uPA. Hence, uPA aptamers offer a valuable alternative as specific recognition elements for analytical purposes. Since aptamers are easy to synthesize and modify, they can be used as a cost-effective alternative in sandwich assay formats for the detection of uPA in physiological samples.
Abstract
Urokinase-type plasminogen activator (urokinase, uPA) is a frequently discussed biomarker for prognosis, diagnosis, and recurrence of cancer.
In a previous study, we developed ssDNA aptamers that bind to different forms of human urokinase, which are therefore assumed to have different binding regions.
In this study, we demonstrate the development of aptamer-based sandwich assays that use different combinations of these aptamers to detect high molecular weight- (HMW-) uPA in a micro titer plate format.
By combining aptamers and antibodies, it was possible to distinguish between HMW-uPA and low molecular weight- (LMW-) uPA.
For the best performing aptamer combination, we calculated the limit of detection (LOD) and limit of quantification (LOQ) in spiked buffer and urine samples with an LOD up to 50 ng/mL and 138 ng/mL, respectively.
To show the specificity and sequence dependence of the reporter aptamer uPAapt-02-FR, we have identified key nucleotides within the sequence that are important for specific folding and binding to uPA using a fluorescent dye-linked aptamer assay (FLAA). Since uPA is a much-discussed marker for prognosis and diagnosis in various types of cancers, these aptamers and their use in a micro titer plate assay format represent a novel, promising tool for the detection of uPA and for possible diagnostic applications.
Rapid innovation and proliferation of software as a medical device have accelerated the clinical use of digital technologies across a wide array of medical conditions.
Current regulatory pathways were developed for traditional (hardware) medical devices and offer a useful structure, but the evolution of digital devices requires concomitant innovation in regulatory approaches to maximize the potential benefits of these emerging technologies.
A number of specific adaptations could strengthen current regulatory oversight while promoting ongoing innovation.
The BEEHAVE model simulates the population dynamics and foraging activity of a single honey bee colony (Apis mellifera) in great detail. Although it still makes numerous simplifying assumptions, it appears to capture a wide range of empirical observations.
It could, therefore, in principle, also be used as a tool in beekeeper education, as it allows the implementation and comparison of different management options.
Here, we focus on treatments aimed at controlling the mite Varroa destructor. However, since BEEHAVE was developed in the UK, mite treatment includes the use of a synthetic acaricide, which is not part of Good Beekeeping Practice in Germany.
A practice that consists of drone brood removal from April to June, treatment with formic acid in August/September, and treatment with oxalic acid in November/December. We implemented these measures, focusing on the timing, frequency, and spacing between drone brood removals.
The effect of drone brood removal and acid treatment, individually or in combination, on a mite-infested colony was examined. We quantify the efficacy of Varroa mite control as the reduction of mites in treated bee colonies compared to untreated bee colonies. We found that drone brood removal was very effective, reducing mites by 90% at the end of the first simulation year after the introduction of mites. This value was significantly higher than the 50-67% reduction expected by bee experts and confirmed by empirical studies.
However, literature reports varying percent reductions in mite numbers from 10 to 85% after drone brood removal. The discrepancy between model results, empirical data, and expert estimates indicate that these three sources should be reviewed and refined, as all are based on simplifying assumptions.
These results and the adaptation of BEEHAVE to the Good Beekeeping Practice are a decisive step forward for the future use of BEEHAVE in beekeeper education in Germany and anywhere where organic acids and drone brood removal are utilized.
The optical properties, chemical composition, and potential chromophores of brown carbon (BrC) aerosol particles were studied during typical summertime and wintertime at a kerbside in downtown Karl-sruhe, a city in central Europe.
The average absorption coefficient and mass absorption efficiency at 365 nm (Abs(365) and MAE(365)) of methanol-soluble BrC (MS-BrC) were lower in the summer period (1.6 +/- 0.5 Mm(-1), 0.5 +/- 0.2 m(2) g(-1)) than in the winter period (2.8 +/- 1.9 Mm(-1), 1.1 +/- 0.3 m(2) g(-1)). Using a parallel factor (PARAFAC) analysis to identify chromophores, two different groups of highly oxygenated humic-like substances (HO-HULIS) dominated in summer and contributed 96 +/- 6 % of the total fluorescence intensity.
In contrast, less-oxygenated HULIS (LO-HULIS) dominated the total fluorescence intensity in winter with 57 +/- 12 %, followed by HO-HULIS with 31 +/- 18 %. Positive matrix factorization (PMF) analysis of organic compounds detected in real time by an online aerosol mass spectrometer (AMS) led to five characteristic organic compound classes.
The statistical analysis of PARAFAC components and PMF factors showed that LO-HULIS chromophores were most likely emitted from biomass burning in winter. HO-HULIS chromophores could be low-volatility oxy-genated organic aerosol from regional transport and oxidation of biogenic volatile organic compounds (VOCs) in summer.
Five nitro-aromatic compounds (NACs) were identified by a chemical ionization mass spectrometer (C7H7O3N, C7H7O4N, C6H5O5N, C6H5O4N, and C6H5O3N), which contributed 0.03 +/- 0.01 % to the total organic mass but can explain 0.3 +/- 0.1 % of the total absorption of MS-BrC at 365 nm in winter.
Furthermore, we identified 316 potential brown carbon molecules which accounted for 2.5 +/- 0.6 % of the organic aerosol mass. Using an average mass absorption efficiency (MAE(365)) of 9.5 m(2)g(-1) for these compounds, we can es-timate their mean light absorption to be 1.2 +/- 0.2 Mm(-1), accounting for 32 +/- 15 % of the total absorption of MS-BrC at 365 nm.
This indicates that a small fraction of brown carbon molecules dominates the overall ab-sorption. The potential BrC molecules assigned to the LO-HULIS component had a higher average molecular weight (265 +/- 2 Da) and more nitrogen-containing molecules (62 +/- 1 %) than the molecules assigned to the HOHULIS components.
Our analysis shows that the LO-HULIS, with a high contribution of nitrogen-containing molecules originating from biomass burning, dominates aerosol fluorescence in winter, and HO-HULIS, with fewer nitrogen-containing molecules as low-volatility oxygenated organic aerosol from regional transport and oxidation of biogenic volatile organic compounds (VOC), dominates in summer.
Adapting to a changing environment: inspiration for planetary health from east African communities
(2022)
Protist grazing pressure plays a major role in controlling aquatic bacterial populations, affecting energy flow through the microbial loop and biogeochemical cycles. Predator-escape mechanisms might play a crucial role in energy flow through the microbial loop, but are yet understudied. For example, some bacteria can use planktonic as well as surface-associated habitats, providing a potential escape mechanism to habitat-specific grazers.
We investigated the escape response of the marine bacterium Marinobacter adhaerens in the presence of either planktonic (nanoflagellate: Cafeteria roenbergensis) or surface-associated (amoeba: Vannella anglica) protist predators, following population dynamics over time.
In the presence of V. anglica, M. adhaerens cell density increased in the water, but decreased on solid surfaces, indicating an escape response towards the planktonic habitat. In contrast, the planktonic predator C. roenbergensis induced bacterial escape to the surface habitat. While C. roenbergensis cell numbers dropped substantially after a sharp initial increase, V. anglica exhibited a slow, but constant growth throughout the entire experiment.
In the presence of C. roenbergensis, M. adhaerens rapidly formed cell clumps in the water habitat, which likely prevented consumption of the planktonic M. adhaerens by the flagellate, resulting in a strong decline in the predator population.
Our results indicate an active escape of M. adhaerens via phenotypic plasticity (i.e., behavioral and morphological changes) against predator ingestion.
This study highlights the potentially important role of behavioral escape mechanisms for community composition and energy flow in pelagic environments, especially with globally rising particle loads in aquatic systems through human activities and extreme weather events.
We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.
In recurrence analysis, the tau-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations.
However, its Fourier decomposition does not have a clean interpretation. Thus, there is no satisfactory analogue to the power spectrum in recurrence analysis.
We introduce a novel method to decompose the tau-recurrence rate using an over-complete basis of Dirac combs together with sparsity regularization.
We show that this decomposition, the inter-spike spectrum, naturally provides an analogue to the power spectrum for recurrence analysis in the sense that it reveals the dominant periodicities of the underlying time series.
We show that the inter-spike spectrum correctly identifies patterns and transitions in the underlying system in a wide variety of examples and is robust to measurement noise.
Anger, indignation, guilt, rumination, victim compensation, and perpetrator punishment are considered primary responses associated with justice sensitivity (JS).
However, injustice and high JS may predispose to further responses.
We had N = 293 adults rate their JS, 17 potential responses toward 12 unjust scenarios from the victim's, observer's, beneficiary's, and perpetrator's perspectives, and several control variables.
Unjust situations generally elicited many affective, cognitive, and behavioral responses. JS generally predisposed to strong affective responses toward injustice, including sadness, pity, disappointment, and helplessness. It impaired trivialization, victim-blaming, or justification, which may otherwise help cope with injustice.
It predisposed to conflict solutions and victim compensation. Particularly victim and beneficiary JS had stronger effects in unjust situations from the corresponding perspective.
These findings add to a better understanding of the main and interaction effects of unjust situations from different perspectives and the JS facets, differences between the JS facets, as well as the links between JS and behavior and well-being.
Leishmaniasis is a vector-borne disease caused by protozoal Leishmania parasites. Previous studies have shown that endoperoxides (EP) can selectively kill Leishmania in host cells.
Therefore, we studied in this work a set of new anthracene-derived EP (AcEP) together with their non-endoperoxidic analogs in model systems of Leishmania tarentolae promastigotes (LtP) and J774 macrophages for their antileishmanial activity and selectivity.
The mechanism of effective compounds was explored by studying their reaction with iron (II) in chemical systems and in Leishmania. The correlation of structural parameters with activity demonstrated that in this compound set, active compounds had a LogP(OW) larger than 3.5 and a polar surface area smaller than 100 angstrom(2).
The most effective compounds (IC50 in LtP < 2 mu M) with the highest selectivity (SI > 30) were pyridyl-/tert-butyl-substituted AcEP.
Interestingly, also their analogs demonstrated activity and selectivity. In mechanistic studies, it was shown that EP were activated by iron in chemical systems and in LtP due to their EP group.
However, the molecular structure beyond the EP group significantly contributed to their differential mitochondrial inhibition in Leishmania.
The identified compound pairs are a good starting point for subsequent experiments in pathogenic Leishmania in vitro and in animal models.
We present SURFER, a novel reduced model for estimating the impact of CO2 emissions and solar radiation modification options on sea level rise and ocean acidification over timescales of several thousands of years.
SURFER has been designed for the analysis of CO2 emission and solar radiation modification policies, for supporting the computation of optimal (CO2 emission and solar radiation modification) policies and for the study of commitment and responsibility under uncertainty.
The model is based on a combination of conservation laws for the masses of atmospheric and oceanic carbon and for the oceanic temperature anomalies, and of adhoc parameterisations for the different sea level rise contributors: ice sheets, glaciers and ocean thermal expansion. It consists of 9 loosely coupled ordinary differential equations, is understandable, fast and easy to modify and calibrate.
It reproduces the results of more sophisticated, high-dimensional earth system models on timescales up to millennia.
Carl Bergmann was an astute naturalist and physiologist. His ideas about animal size and shape were important advances in the pre-Darwinian nineteenth century. Bergmann's rule claims that that in cold climates, large body mass increases the ratio of volume-to-surface area and provides for maximum metabolic heat retention in mammals and birds. Conversely, in warmer temperatures, smaller body mass increases surface area relative to volume and allows for greater heat loss. For humans, we now know that body size and shape are regulated more by social-economic-political-emotional (SEPE) factors as well as nutrition-infection interactions. Temperature has virtually no effect. Bergmann's rule is a "just-so" story and should be relegated to teaching and scholarship about the history of science. That "rule" is no longer acceptable science and has nothing to tell us about physiological anthropology.
Since the first reported case of COVID-19 in 2019 in China and the official declaration from the World Health Organization in March 2021 as a pandemic, fast and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has played a major role worldwide. For this reason, various methods have been developed, comprising reverse transcriptase-polymerase chain reaction (RT-PCR), immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and bio(mimetic)sensors. Among the developed methods, RT-PCR is so far the gold standard. Herein, we give an overview of the MIP-based sensors utilized since the beginning of the pandemic.
Tropical cyclones range among the costliest of all meteorological events worldwide and planetary scale warming provides more energy and moisture to these storms. Modelling the national and global economic repercussions of 2017's Hurricane Harvey, we find a qualitative change in the global economic response in an increasingly warmer world.
While the United States were able to balance regional production failures by the original 2017 hurricane, this option becomes less viable under future warming.
In our simulations of over 7000 regional economic sectors with more than 1.8 million supply chain connections, the US are not able to offset the losses by use of national efforts with intensifying hurricanes under unabated warming.
At a certain warming level other countries have to step in to supply the necessary goods for production, which gives US economic sectors a competitive disadvantage. In the highly localized mining and quarrying sector-which here also comprises the oil and gas production industry-this disadvantage emerges already with the original Hurricane Harvey and intensifies under warming.
Eventually, also other regions reach their limit of what they can offset.
While we chose the example of a specific hurricane impacting a specific region, the mechanism is likely applicable to other climate-related events in other regions and other sectors.
It is thus likely that the regional economic sectors that are best adapted to climate change gain significant advantage over their competitors under future warming.
Background:
The medical care of patients with myositis is a great challenge in clinical practice. This is due to the rarity of these disease, the complexity of diagnosis and management as well as the lack of systematic analyses.
Objectives:
Therefore, the aim of this project was to obtain an overview of the current care of myositis patients in Germany and to evaluate epidemiological trends in recent years.
Methods:
In collaboration with BARMER Insurance, retrospective analysis of outpatient and inpatient data from an average of approximately 8.7 million insured patients between January 2005 and December 2019 was performed using ICD-10 codes for myositis for identification of relevant data.
In addition, a comparative analysis was performed between myositis patients and an age-matched comparison group from other populations insured by BARMER.
Results:
45,800 BARMER-insured individuals received a diagnosis of myositis during the observation period, with a relatively stable prevalence throughout. With regard to comorbidities, a significantly higher rate of cardiovascular disease as well as neoplasm was observed compared to the control group within the BARMER-insured population. In addition, myositis patients suffer more frequently from psychiatric disorders, such as depression and somatoform disorders.
However, the ICD-10 catalogue only includes the specific coding of "dermatomyositis" and "polymyositis" and thus does not allow for a sufficient analysis of all idiopathic inflammatory myopathies subtypes.
Conclusion:
The current data provide a comprehensive epidemiological analysis of myositis in Germany, highlighting the multimorbidity of myositis patients. This underlines the need for multidisciplinary management. However, the ICD-10 codes currently still in use do not allow for specific analysis of the subtypes of myositis.
The upcoming ICD-11 coding may improve future analyses in this regard.
Background:
Polycystic ovary syndrome (PCOS) is an endocrine disease in which related to obesity, metabolic disorders and is considered as one of the main causes of infertility in women. This trial was investigated the effects of green cardamom on the expression of genes implicated in obesity and diabetes among obese women with PCOS.
Methods:
One hundred ninety-four PCOS women were randomly divided two groups: intervention (n = 99; 3 g/day green cardamom) and control groups (n = 95). All of them were given low calorie diet. Anthropometric, glycemic and androgen hormones were assessed before and after 16-week intervention. The reverse transcription-polymerase chain reaction (RT-PCR) method was used to measure fat mass and obesity-associated (FTO), peroxisome proliferative activating receptor- (PPAR-), carnitine palmitoyltransferase 1A (CPT1A), acetyl-CoA carboxylase beta (ACAB), leptin receptor (LEPR), ghrelin, and lamin A/C (LAMIN) genes expression in each group.
Results:
Anthropometric indices were significantly decreased after intervention in both two studied groups. Glycemic indices and androgen hormones were significantly improved in the intervention group compared to the control group. The expression levels of FTO, CPT1A, LEPR, and LAMIN were significantly downregulated compared to control group (P < 0.001), as well as, PPAR-y was significantly upregulated in the intervention group after intervention with green cardamom compared to control group (P < 0.001).
Conclusion:
This current study showed that the administration of green cardamom is a beneficial approach for improving anthropometric, glycemic, and androgen hormones, as well as obesity and diabetes genes expression in PCOS women under the low-calorie diet.
Photosynthetic activity in both algae and cyanobacteria changes in response to cues of predation
(2022)
A plethora of adaptive responses to predation has been described in microscopic aquatic producers.
Although the energetic costs of these responses are expected, with their consequences going far beyond an individual, their underlying molecular and metabolic mechanisms are not fully known.
One, so far hardly considered, is if and how the photosynthetic efficiency of phytoplankton might change in response to the predation cues. Our main aim was to identify such responses in phytoplankton and to detect if they are taxon-specific.
We exposed seven algae and seven cyanobacteria species to the chemical cues of an efficient consumer, Daphnia magna, which was fed either a green alga, Acutodesmus obliquus, or a cyanobacterium, Synechococcus elongatus (kairomone and alarm cues), or was not fed (kairomone alone).
In most algal and cyanobacterial species studied, the quantum yield of photosystem II increased in response to predator fed cyanobacterium, whereas in most of these species the yield did not change in response to predator fed alga.
Also, cyanobacteria tended not to respond to a non-feeding predator. The modal qualitative responses of the electron transport rate were similar to those of the quantum yield.
To our best knowledge, the results presented here are the broadest scan of photosystem II responses in the predation context so far.
The history of the Messiah in Judaism is a history of disappointed hopes. Again and again, there were salvation fi gures to whom this role was ascribed. But redemption from occupation and foreign rule, exile, oppression and persecution failed to materialize. Therefore, the expectation of the Messiah fell to the periphery of Jewish theology. This article examinesin what ways the messianic concept plays a role in modern times and what it contributes to describing the relationship between God and humanity in Judaism. The author intends to show the development from the abandonment of a personal Messiah towards the affi rmation of the prophets’ hope for a universal messianic age in which the duty of all people to participate in the healing of the world becomes central. What becomes also clear is: The messiah idea cannot be a bridge between Christianity and Judaism.
Trade-offs between traits are present across different levels of biological systems and ultimately reflect constraints imposed by physicochemical laws and the structure of underlying biochemical networks. Yet, mechanistic explanation of how trade-offs between molecular traits arise and how they relate to optimization of fitness-related traits remains elusive. Here, we introduce the concept of relative flux trade-offs and propose a constraint-based approach, termed FluTOr, to identify metabolic reactions whose fluxes are in relative trade-off with respect to an optimized fitness-related cellular task, like growth. We then employed FluTOr to identify relative flux trade-offs in the genome-scale metabolic networks of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana. For the metabolic models of E. coli and S. cerevisiae we showed that: (i) the identified relative flux trade-offs depend on the carbon source used and that (ii) reactions that participated in relative trade-offs in both species were implicated in cofactor biosynthesis. In contrast to the two microorganisms, the relative flux trade-offs for the metabolic model of A. thaliana did not depend on the available nitrogen sources, reflecting the differences in the underlying metabolic network as well as the considered environments. Lastly, the established connection between relative flux trade-offs allowed us to identify overexpression targets that can be used to optimize fitness-related traits. Altogether, our computational approach and findings demonstrate how relative flux trade-offs can shape optimization of metabolic tasks, important in biotechnological applications.
Formal constraints on crossing dependencies have played a large role in research on the formal complexity of natural language grammars and parsing. Here we ask whether the apparent evidence for constraints on crossing dependencies in treebanks might arise because of independent constraints on trees, such as low arity and dependency length minimization. We address this question using two sets of experiments. In Experiment 1, we compare the distribution of formal properties of crossing dependencies, such as gap degree, between real trees and baseline trees matched for rate of crossing dependencies and various other properties. In Experiment 2, we model whether two dependencies cross, given certain psycholinguistic properties of the dependencies. We find surprisingly weak evidence for constraints originating from the mild context-sensitivity literature (gap degree and well-nestedness) beyond what can be explained by constraints on rate of crossing dependencies, topological properties of the trees, and dependency length. However, measures that have emerged from the parsing literature (e.g., edge degree, end-point crossings, and heads' depth difference) differ strongly between real and random trees. Modeling results show that cognitive metrics relating to information locality and working-memory limitations affect whether two dependencies cross or not, but they do not fully explain the distribution of crossing dependencies in natural languages. Together these results suggest that crossing constraints are better characterized by processing pressures than by mildly context-sensitive constraints.
Otter shrew mitogenomes (Afrotheria, Potamogalidae) reconstructed from historical museum skins
(2022)
African otter shrews (Potamogalidae) are Afrotherian mammals adapted to a semi-aquatic lifestyle. Given their rareness, genetic data on otter shrews are limited. By applying laboratory methods tuned for the recovery of archival DNA and an iterative mapping approach, we reconstructed whole mitochondrial genomes of the Giant (Potamogale velox) and Ruwenzori pygmy otter shrew (Micropotamogale ruwenzorii) from historical museum skins. Phylogenetic analyses are consistent with previous reports in recovering a sister relationship between African otter shrews and Malagasy tenrecs. The long branches separating both lineages, however, support their recognition as separate families.
Critical role of parasite-mediated energy pathway on community response to nutrient enrichment
(2022)
Parasites form an integral part of food webs, however, they are often ignored in classic food web theory or limited to the investigation of trophic transmission pathways. Specifically, direct consumption of parasites by nonhost predators is rarely considered, while it can contribute substantially to energy flow in food webs. In aquatic systems, chytrids constitute a major group of fungal parasites whose free-living infective stages (zoospores) form a highly nutritional food source to zooplankton. Thereby, the consumption of zoospores can create an energy pathway from otherwise inedible phytoplankton to zooplankton ( "mycoloop "). This parasite-mediated energy pathway might be of special importance during phytoplankton blooms dominated by inedible or toxic primary producers like cyanobacteria, which are on the rise with eutrophication and global warming. We theoretically investigated community dynamics and energy transfer in a food web consisting of an edible nonhost and an inedible host phytoplankton species, a parasitic fungus, and a zooplankton species grazing on edible phytoplankton and fungi. Food web dynamics were investigated along a nutrient gradient contrasting nonadaptive zooplankton species representative for filter feeders like cladocerans and zooplankton with the ability to actively adapt their feeding preferences like many copepod species. Overall, the importance of the mycoloop for zooplankton increases with nutrient availability. This increase is smooth for nonadaptive consumers. For adaptive consumers, we observe an abrupt shift from an almost exclusive preference for edible phytoplankton at low nutrient levels to a strong preference for parasitic fungi at high nutrient levels. The model predicts that parasitic fungi could contribute up to 50% of the zooplankton diet in nutrient-rich environments, which agrees with empirical observations on zooplankton gut content from eutrophic systems during blooms of inedible diatoms or cyanobacteria. Our findings highlight the role of parasite-mediated energy pathways for predictions of energy flow and community composition under current and future environmental change.
The acquisition of clitics still remains a highly controversial issue in Greek acquisition literature despite the bulk of studies performed.
Object clitics have been shown to be early acquired by monolingual children in terms of production rates, whereas only highly proficient bilingual children achieve target-like performance.
Crucially, errors in gender marking are persistent for monolingual and bilingual children even when adult-like production rates are achieved.
This study aims to readdress the acquisition of clitics in an innovative way, by entering the variable of gender in an experimental design targeting to assess production and processing by bilingual and monolingual children.
Moreover, we examined the role of language proficiency (in terms of general verbal intelligence and syntactic production abilities). The groups had comparable performance in both tasks (in terms of correct responses and error distribution in production and reaction times in comprehension).
However, verbal intelligence had an effect on the performance of the monolingual but not of the bilingual group in the production task, and bilingual children were overall slower in the comprehension task. Syntactic production abilities did not have any effect. We argue that gender marking affects clitic processing, and we discuss the implications of our findings for bilingual acquisition.
Large agricultural streams receive excessive inputs of nitrogen.
However, quantifying the role of these streams in nitrogen processing remains limited because continuous direct measurements of the interacting and highly time-varying nitrogen processing pathways in larger streams and rivers are very complex.
Therefore, we employed a monitoring-driven modelling approach with high-frequency in situ data and the river water quality model Water Quality Analysis Simulation Program (WASP) 7.5.2 in the 27.4 km reach of the sixth-order agricultural stream called Lower Bode (central Germany) for a 5-year period (2014-2018).
Paired high-frequency sensor data (15 min interval) of discharge, nitrate, dissolved oxygen, and chlorophyll a at upstream and downstream stations were used as model boundaries and for setting model constraints.
The WASP model simulated 15 min intervals of discharge, nitrate, and dissolved oxygen with Nash-Sutcliffe efficiency values higher than 0.9 for calibration and validation, enabling the calculation of gross and net dissolved inorganic nitrogen uptake and pathway rates on a daily, seasonal, and multiannual scale.
Results showed daily net uptake rate of dissolved inorganic nitrogen ranged from -17.4 to 553.9 mgNm(-2)d(-1). The highest daily net uptake could reach almost 30 % of the total input loading, which occurred at extreme low flow in summer 2018.
The growing season (spring and summer) accounted for 91 % of the average net annual uptake of dissolved inorganic nitrogen in the measured period. In spring, both the DIN gross and net uptake were dominated by the phytoplankton uptake pathway. In summer, benthic algae assimilation dominated the gross uptake of dissolved inorganic nitrogen.
Conversely, the reach became a net source of dissolved inorganic nitrogen with negative daily net uptake values in autumn and winter, mainly because the release from benthic algae surpassed uptake processes.
Over the 5 years, average gross and net uptake rates of dissolved inorganic nitrogen were 124.1 and 56.8 mgNm(-2)d(-1), which accounted for only 2.7 % and 1.2 % of the total loadings in the Lower Bode, respectively. The 5-year average gross DIN uptake decreased from assimilation by benthic algae through assimilation by phytoplankton to denitrification.
Our study highlights the value of combining river water quality modelling with high-frequency data to obtain a reliable budget of instream dissolved inorganic nitrogen processing which facilitates our ability to manage nitrogen in aquatic systems.
This study provides a methodology that can be applied to any large stream to quantify nitrogen processing pathway dynamics and complete our understanding of nitrogen cycling.
Temperature impacts on hate speech online: evidence from 4 billion geolocated tweets from the USA
(2022)
Background - A link between weather and aggression in the offline world has been established across a variety of societal settings. Simultaneously, the rapid digitalisation of nearly every aspect of everyday life has led to a high frequency of interpersonal conflicts online. Hate speech online has become a prevalent problem that has been shown to aggravate mental health conditions, especially among young people and marginalised groups.
We examine the effect of temperature on the occurrence of hate speech on the social media platform Twitter and interpret the results in the context of the interlinkage between climate change, human behaviour, and mental health.
Methods - In this quantitative empirical study, we used a supervised machine learning approach to identify hate speech in a dataset containing around 4 billion geolocated tweets from 773 cities across the USA between May 1, 2014 and May 1, 2020.
We statistically evaluated the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models.
Findings - The prevalence of hate tweets was lowest at moderate temperatures (12 to 21?) and marked increases in the number of hate tweets were observed at hotter and colder temperatures, reaching up to 12middot5% (95% CI 8middot0-16middot5) for cold temperature extremes (-6 to -3?) and up to 22middot0% (95% CI 20middot5-23middot5) for hot temperature extremes (42 to 45?). Outside of the moderate temperature range, the hate tweets also increased as a proportion of total tweeting activity. The quasi-quadratic shape of the temperature-hate tweet curve was robust across varying climate zones, income quartiles, religious and political beliefs, and both city-level and state-level aggregations.
However, temperature ranges with the lowest prevalence of hate tweets were centred around the local temperature mean and the magnitude of the increases in hate tweets for hot and cold temperatures varied across the climate zones.
Interpretation - Our results highlight hate speech online as a potential channel through which temperature alters interpersonal conflict and societal aggression. We provide empirical evidence that hot and cold temperatures can aggravate aggressive tendencies online. The prevalence of the results across climatic and socioeconomic subgroups points to limitations in the ability of humans to adapt to temperature extremes.
Soil microbial communities are crucial for plant growth and are already depleted by anthropogenic activities.
The application of microbial transplants provides a strategy to restore beneficial soil traits, but less is known about the microbiota of traditional inoculants used in biodynamic agriculture.
In this study, we used amplicon sequencing and quantitative PCR to decipher microbial communities of composts, biodynamic manures, and plant preparations from Austria and France.
In addition, we investigated the effect of extracts derived from biodynamic manure and compost on the rhizosphere microbiome of apple trees. Microbiota abundance, composition, and diversity of biodynamic manures, plant preparations, and composts were distinct. Microbial abundances ranged between 1010-1011 (bacterial 16S rRNA genes) and 109-1011 (fungal ITS genes). The bacterial diversity was significantly higher in biodynamic manures compared to compost without discernible differences in abundance. Fungal diversity was not significantly different while abundance was increased in biodynamic manures. The microbial communities of biodynamic manures and plant preparations were specific for each production site, but all contain potentially plant-beneficial bacterial genera.
When applied in apple orchards, biodynamic preparations (extracts) had the non-significant effect of reducing bacterial and fungal abundance in apple rhizosphere (4 months post-application), while increasing fungal and lowering bacterial Shannon diversity.
One to four months after inoculation, individual taxa indicated differential abundance. We observed the reduction of the pathogenic fungus Alternaria, and the enrichment of potentially beneficial bacterial genera such as Pseudomonas.
Our study paves way for the science-based adaptation of empirically developed biodynamic formulations under different farming practices to restore the vitality of agricultural soils.
Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms.
With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation.
The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate.
Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff.
The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions.
However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.
It is often claimed that the entropy of a network's degree distribution is a proxy for its robustness. Here, we clarify the link between degree distribution entropy and giant component robustness to node removal by showing that the former merely sets a lower bound to the latter for randomly configured networks when no other network characteristics are specified. Furthermore, we show that, for networks of fixed expected degree that follow degree distributions of the same form, the degree distribution entropy is not indicative of robustness. By contrast, we show that the remaining degree entropy and robustness have a positive monotonic relationship and give an analytic expression for the remaining degree entropy of the log-normal distribution. We also show that degree-degree correlations are not by themselves indicative of a network's robustness for real networks. We propose an adjustment to how mutual information is measured which better encapsulates structural properties related to robustness.
Contour scanning and process gas type are process parameters typically considered achieving second order effects compared to first order factors such as laser power and scanning speed.
The present work highlights that contour scanning is crucial to ensure geometrical accuracy and thereby the high performance under uniaxial compression of complex Alloy 718 lattice structures.
Studies of X-ray computed tomography visualizations of as-built and compression-strained structures reveal the continuous and smooth bending and compression of the walls, and the earlier onset of internal contact appearance in the denser lattices printed with contour. In contrast, the effect of addition of He to the Ar process gas appears to have limited influence on the mechanical response of the lattices and their microstructure as characterized by electron backscattered diffraction.
However, the addition of He proved to significantly enhance the cooling rate and to reduce the amount of the generated spatters as evidenced by in situ monitoring of the process emissions, which is very promising for the process stability and powder reusability during laser powder bed fusion.
Understanding the assembly mechanism and function of membrane proteins is a fundamental problem in biochemical research. Among the membrane proteins, G protein-coupled receptors (GPCRs) represent the largest class in the human body and have long been considered to function as monomers.
Nowadays, the oligomeric assembly of GPCRs is widely accepted, although the functional importance and therapeutic intervention remain largely unexplored. This is partly due to difficulties in the heterologous production of membrane proteins.
Cell-free protein synthesis (CFPS) with its endogenous endoplasmic reticulum-derived structures has proven as a technique to address this issue.
In this study, we investigate for the first time the conceptual CFPS of a heteromeric GPCR, the gamma-aminobutyric acid receptor type B (GABA(B)), from its protomers BR1 and BR2 using a eukaryotic cell-free lysate. Using a fluorescence-based proximity ligation assay, we provide evidence for colocalization and thus suggesting heterodimerization.
We prove the heterodimeric assembly by a bioluminescence resonance energy transfer saturation assay providing the manufacturability of a heterodimeric GPCR by CFPS. Additionally, we show the binding of a fluorescent orthosteric antagonist, demonstrating the feasibility of combining the CFPS of GPCRs with pharmacological applications.
These results provide a simple and powerful experimental platform for the synthesis of heteromeric GPCRs and open new perspectives for the modelling of protein-protein interactions.
Accordingly, the presented technology enables the targeting of protein assemblies as a new interface for pharmacological intervention in disease-relevant dimers.
Landslide hazard models aim at mitigating landslide impact by providing probabilistic forecasting, and the accuracy of these models hinges on landslide databases for model training and testing.
Landslide databases at times lack information on the underlying triggering mechanism, making these inventories almost unusable in hazard models.
We developed a Python-based unique library, Landsifier, that contains three different machine-Learning frameworks for assessing the likely triggering mechanisms of individual landslides or entire inventories based on landslide geometry.
Two of these methods only use the 2D landslide planforms, and the third utilizes the 3D shape of landslides relying on an underlying digital elevation model (DEM). The base method extracts geometric properties of landslide polygons as a feature space for the shallow learner - random forest (RF).
An alternative method relies on landslide planform images as an input for the deep learning algorithm - convolutional neural network (CNN).
The last framework extracts topological properties of 3D landslides through topological data analysis (TDA) and then feeds these properties as a feature space to the random forest classifier.
We tested all three interchangeable methods on several inventories with known triggers spread over the Japanese archipelago. To demonstrate the effectiveness of developed methods, we used two testing configurations.
The first configuration merges all the available data for the k-fold cross-validation, whereas the second configuration excludes one inventory during the training phase to use as the sole testing inventory.
Our geometric-feature-based method performs satisfactorily, with classification accuracies varying between 67 % and 92 %. We have introduced a more straightforward but data-intensive CNN alternative, as it inputs only landslide images without manual feature selection.
CNN eases the scripting process without losing classification accuracy. Using topological features from 3D landslides (extracted through TDA) in the RF classifier improves classification accuracy by 12 % on average.
TDA also requires less training data. However, the landscape autocorrelation could easily bias TDA-based classification. Finally, we implemented the three methods on an inventory without any triggering information to showcase a real-world application.