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Porcine Cytomegalovirus/Porcine Roseolovirus, Previously Transmitted During Xenotransplantation, Does Not Infect Human 293T and Mouse Cells with Impaired Antiviral Defense
No full textPorcine cytomegalovirus, more accurately classified as porcine roseolovirus (PCMV/PRV), was shown to be pathogenic in the context of xenotransplantation. Transmission of PCMV/PRV to non-human primates receiving hearts or kidneys from virus-positive pigs significantly reduced the survival time of the recipients. PCMV/PRV was also transmitted to the first human recipient of a pig heart transplant and contributed to the patient’s death. Although PCMV/PRV is highly prevalent in all pig breeds and wild boars, including slaughterhouse pigs, no infections or diseases have been reported in healthy, ill, or immunocompromised humans, suggesting that this virus is not zoonotic and should therefore be classified as xenozoonotic. This indicates that this virus is not zoonotic and must be classified as xenozoonotic. Moreover, it remains unclear whether PCMV/PRV is capable of infecting human cells in vitro. To address this question, human 293T cells resistant to hygromycin were co-cultured with porcine fallopian tube (PFT) cells producing PCMV/PRV. After hygromycin selection, the remaining human cells showed no evidence of infection. Because herpesviruses are generally considered to be species-specific—a notion that has been shown to be not entirely correct—it was also investigated whether PCMV/PRV can infect mouse cells using the same approach. Similarly, no infection was observed. Since the target cells employed in both assays had a reduced capacity to resist viral infection, the findings strongly suggest that PCMV/PRV is unable to infect human or mouse cells, which are equipped with functional antiviral mechanisms. This is supported by findings from the patient who received the first pig heart transplantatio
Immunoassays for the detection and differentiation of Paenibacillus larvae, the etiological agent of American foulbrood (AFB) in honey bees
No full textThe Western honey bee ( Apis mellifera ) is among the most important commercial pollinators in agriculture, but also plays a central role as pollinator in natural ecosystems. The globally occurring brood disease American foulbrood (AFB), caused by the gram-positive, spore-forming bacterium Paenibacillus larvae , poses a serious threat to colony health and productivity. Early and accurate diagnosis is therefore essential to effectively contain disease outbreaks. This study aimed to develop a rapid, sensitive point-of-care test in lateral flow format for the simultaneous detection and differentiation of the clinically relevant P. larvae genotypes ERIC I and II. To achieve this aim, two target antigens were selected, produced recombinantly or purified, and used to generate monoclonal antibodies. These antibodies formed the basis for the development of two sandwich ELISAs and, building on this, a duplex lateral flow assay. Both immunoassay formats were evaluated with infected larvae samples. The results showed high specificity, sensitivity, and accuracy, allowing reliable detection of P. larvae and differentiation between the genotypes ERIC I and II, thereby providing a valuable addition to existing diagnostic methods. In particular, the lateral flow assay enables rapid on-site diagnosis, facilitating timely intervention, and thus supports effective measures to control the spread of the disease
Breaking the Barriers of Molecular Dynamics With Deep-Learning: Opportunities, Pitfalls, and How to Navigate Them
No full textMolecular Dynamics (MD) has established itself as a pivotal computational tool across various scientific domains, including chemistry, biology, and materials science. Despite its widespread utility, MD faces inherent challenges, such as accuracy limitations, computational speed, and sampling efficiency. In recent years, machine learning, particularly deep learning, has seen significant advancements and is increasingly being integrated into MD processes. This review explores how deep learning can mitigate the issues associated with MD by addressing them from multiple angles. However, deep learning techniques introduce their own set of hurdles, including the need for extensive data, issues of interpretability, high computational costs, and concerns regarding transferability. Here, we discuss recent progress in the field of deep learning to overcome these obstacles. Ultimately, our goal is to demonstrate that, by leveraging the advancements made in both the MD and the machine learning community, deep learning has the potential to significantly enhance the capabilities of MD, paving the way to new scientific discovery
Survey of German veterinarians' approaches to pain assessment and management of perioperative pain in pet rabbits
No full textBackground
This study aimed to assess veterinary practices in pain recognition and perioperative analgesic therapy in pet rabbits.
Methods
An online questionnaire was distributed German veterinarians from July to October 2022, containing questions on the frequency with which they treated rabbits and the methods they used to evaluate pain in this species. Additionally, participants were asked detailed questions about their approach to perioperative pain management when performing soft-tissue surgery, specifically an ovariohysterectomy, in rabbits.
Results
One hundred and fifty-four questionnaires were considered for final analysis. The most commonly reported indicators to detect pain in rabbits were assessment of food intake and behavioural observation. A total of 23.4% of the participants stated that they used the Rabbit Grimace Scale. Overall, 24.5% of the 110 veterinarians who performed ovariohysterectomies in rabbits reported no use of preoperative analgesia; however, 95.5% administered multimodal analgesia in the pre- and intraoperative phases combined and 60.0% administered analgesia postoperatively. The most commonly mentioned drug combination pre- and postoperatively was metamizole and meloxicam. Opioids and local anaesthetics were used less frequently.
Limitations
The survey had a small sample size and notable selection bias, with 90.3% female participants.
Conclusion
Respondents used different indicators to detect pain in rabbits. Analgesic therapy in ovariohysterectomies, as reported by participants, can be considered suboptimal in several cases
Chemically Induced Resistance to Pathogen Infection in Arabidopsis by Cytokinin (Trans‐Zeatin) and an Aromatic Cytokinin Arabinoside
No full textThis study compares the ability of the cytokinin (CK) trans ‐zeatin ( t Z) and the CK sugar conjugate 6‐(3‐methoxybenzylamino)purine‐9‐arabinoside (BAPA) to induce resistance against the bacterial pathogen Pseudomonas syringae in Arabidopsis thaliana . Treatment with either t Z or BAPA significantly reduced bacterial growth after a later infection. This chemically induced resistance (IR) required the CK receptor AHK3, highlighting its critical role in mediating resistance by t Z and BAPA. This is remarkable as these compounds show either high or no affinity for this CK receptor, respectively. Surprisingly, t Z, but not BAPA, induced the expression of CK response genes, including ARR5 , suggesting divergent mechanisms of action. Resistance caused by both compounds was abolished in the npr1 mutant, underpinning the functional relevance of the salicylic acid (SA) signalling pathway. Transcriptomic analysis showed that both BAPA and t Z triggered the expression of distinct sets of genes associated with SA and reactive oxygen species (ROS) but not with jasmonic acid (JA) signalling. BAPA and, to a lesser extent, also t Z activated pattern‐triggered immunity (PTI) signalling genes, including genes responsible for PTI signal amplification ( PREPIP2 ) and pathogen‐associated molecular pattern (PAMP) signalling ( PH1 , IDL6 ). This supported the hypothesis that the PTI pathway mediates the protective effect. Similarities and differences of chemically triggered IR by t Z and BAPA, as well as their potential for application, are discussed
Determining essential dimensions for the clinical approximation of personality disorder severity: multi-method study
No full textBackground
Decades of research on the dimensional nature of personality disorder have led to the replacement of categorical personality disorder diagnoses by a dimensional assessment of personality disorder severity (PDS) in ICD-11, which essentially corresponds to personality functioning in the alternative DSM-5 model for personality disorders. Besides advancing the focus in the diagnosis of PD on impairments in self- and interpersonal functioning, this shift also urges clinicians and researchers worldwide to get familiar with new diagnostic approaches.
Aims
This study investigated which PDS dimensions among different assessment methods and conceptualisations have the most predictive value for overall PDS.
Method
Using semi-structured interviews and self-reports of personality functioning, personality organisation and personality structure in clinical samples of different settings in Switzerland and Germany (n = 534), we calculated a latent general factor for PDS (g-PDS) by applying a correlated trait correlated (method – 1) model (CTC(M–1)).
Results
Our results showed that four interview-assessed PDS dimensions: defence mechanisms, desire and capacity for closeness, sense of self, and comprehension and appreciation of others’ experiences and motivations account for 91.1% of variance of g-PDS, with a combination of either two of these four dimensions already explaining between 81.8 and 91.3%. Regarding self-reports, the dimensions depth and duration of connections, self-perception, object perception and attachment capacity to internal objects predicted 61.3% of the variance of a latent interview-based score, with all investigated self-reported dimensions together adding up to 65.2% variance explanation.
Conclusions
Taken together, our data suggest that focusing on specific dimensions, such as intimacy and identity, in time-limited settings might be viable in determining PDS efficiently
D[X]IM—the Dynamic Intermediary Model of communicative transaction on digital platforms in a networked public sphere
No full textThis study introduces the Dynamic Intermediary Model (D[X]IM) to address how knowledge processes have evolved with digital platforms by shifting from a dyadic to a triadic communication model of content flow with a potential intermediary. This intermediary, which can be a journalist, influencer, artificial agent, or another platform actor, provides services to the source and recipient of a message, thereby transforming traditional direct communication. It aims to better understand information diffusion in the networked public sphere by recognizing the intermediary’s role in altering source-recipient dynamics. The D[X]IM applies across different communication levels (macro, meso, and micro) and is designed for empirical research using diverse methodologies. It focuses on single instances of platform communication to explore the impact of intermediated communication. The article concludes with a research agenda and examples of how D[X]IM can be applied in empirical research
Innovative application of a traffic-prediction spatio-temporal graph convolutional network for dengue disease forecasting
No full textDengue fever, a vector-borne disease, is a major public health challenge. Accurate prediction methods that can better reflect the complexity of the outbreak are essential for dengue prediction and vector control. In this study, we introduce an adapted Spatio-Temporal Graph Convolutional Network (STGCN), originally developed for traffic forecasting, to predict weekly dengue cases in nine countries in South and Central America from 2014 to 2022. In this approach, we use environmental and socio-economic data in addition to climate data and historical dengue case information to capture complex transmission dynamics. We evaluate the STGCN against a Random Forest (RF) model using the same predictors. The evaluation results show that the STGCN model effectively captures outbreak dynamics and short-term trends. This was especially evident in cases where early transmission patterns are critical. In most of the countries analyzed, STGCN outperformed the baseline random forest model, especially in short-term forecasts, and achieved lower forecast errors in most settings. Forecasting performance varied across regions, with R-2 values ranging from 0.78 to 0.98 and RRMSE between 0.14 and 0.43 in short-term forecasts. The strength of the STGCN algorithm lies in its ability to capture spatio-temporal dependencies and handle heterogeneous data sources. This has been particularly valuable in areas with a high dengue burden. Although performance of the model varied slightly across countries, our overall findings highlight the robustness and adaptability of STGCN as a graph-based deep learning framework for dengue surveillance and early detection of its outbreaks
Framework for Quantifying the Efficiency of Competing Signal Transmission Modes in Proteins
No full textOn the microscopic level, biological signal transmission relies on coordinated transient structural changes in allosteric proteins that involve sensor and effector modules. The time scales and microscopic details of signal transmission in proteins are often unclear, despite a plethora of structural information on signaling proteins. Based on linear-response theory, we develop the theoretical framework to define frequency-dependent force and displacement transmit functions through proteins and, more generally, viscoelastic media. Transmit functions quantify the fraction of a local time-dependent perturbation at one site, be it a deformation, a force or a combination thereof, that survives at a distant site. They are defined in terms of equilibrium fluctuations from simulations or experimental observations. We apply the framework to our all-atom molecular dynamics simulations of a bacterial histidine kinase protein extensively studied in experiments. For the isolated coiled-coil (CC) motif that connects sensor and effector modules, our analysis reveals that signal propagation through the CC is possible via shift, splay, and twist deformation modes, which is confirmed by simulations of the entire protein. Based on mutation experiments, we infer that the most relevant mode for the biological function of the histidine kinase is the splay deformation. For the β2-adrenergic receptor, a transmembrane protein involved in the G-protein signaling pathway, we compare signal transmission across its different structural domains involved in receptor activation
Multivariate change estimation for a stochastic heat equation from local measurements
No full textWe study a stochastic heat equation with piecewise constant diffusivity having a jump at a
hypersurface that splits the underlying space [0, 1] , ≥ 2, into two disjoint sets − ∪ +.
Based on multiple spatially localized measurement observations on a regular -grid of [0, 1] ,
we propose a joint M-estimator for the diffusivity values and the set + that is inspired by
statistical image reconstruction methods. We study convergence of the domain estimator ̂ +
in the vanishing resolution level regime → 0 and with respect to the expected symmetric
difference pseudometric. As a first main finding we give a characterization of the convergence
rate for ̂ + in terms of the complexity of measured by the number of intersecting hypercubes
from the regular -grid. Furthermore, for the special case of domains + that are built from
hypercubes from the -grid, we demonstrate that perfect identification with probability tending
to one is possible with a slight modification of the estimation approach. Implications of our
general results are discussed under two specific structural assumptions on +. For a -Hölder
smooth boundary fragment , the set + is estimated with rate . If we assume + to be
convex, we obtain a -rate. While our approach only aims at optimal domain estimation rates,
we also demonstrate consistency of our diffusivity estimators, which is strengthened to a CLT
at minimax optimal rate for sets + anchored on the -grid