Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step.

The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.

Applications of monitoring and tracing the evolution of clustering solutions in dynamic datasets.

The clustering approach is widely accepted as the most prominent unsupervised learning problem in data mining techniques. This procedure deals with the identification of notable structures in unlabeled datasets. In modern days clustering of dynamic data, streams play a vital role in policy-making, and researchers are paying particular attention to monitoring the evolution of clustering solutions over time. The data streams evolve continually, and different sources generate data items over time. The clustering solution over this stream is not stationary and changes with the influx of new data items. This paper presents a comprehensive study of algorithms related to tracing the evolution of clusters over time in cumulative datasets. To demonstrate the applications and significance of the tracing cluster evolution, we implement the MONIC algorithm in R-software. This article illustrates how the data segmentation of dynamic streams is done and shows the applications of monitoring changes in clustering solutions with the help of real-life published datasets.

Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults.

BACKGROUND: The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score". METHODS: The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. RESULTS: From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex. CONCLUSIONS: COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

clusTransition: An R package for monitoring transition in cluster solutions of temporal datasets.

Clustering analysis' primary purpose is to divide a dataset into a finite number of segments based on the similarities between items. In recent years, a significant amount of study has focused on the spatio-temporal aspects of clustering. However, clusters are no longer regarded as static objects since changes influence them in the underlying population. This paper describes an R package implementing the MONIC framework for tracing the evolution of clusters extracted from temporal datasets. The name of the package is clusTransition, which stands for Cluster Transition. The algorithm is based on re-clustering cumulative datasets that evolve at successive time-points and monitoring the transitions experienced by the clusters in these clustering solutions. This paper's contribution is to demonstrate how the package clusTransition is developed in the R programming language, and its workflow is discussed using hypothetical and real-life datasets.

Against the "one method fits all data sets" philosophy for comparison studies in methodological research.

Many methodological comparison studies aim at identifying a single or a few "best performing" methods over a certain range of data sets. In this paper we take a different viewpoint by asking whether the research question of identifying the best performing method is what we should be striving for in the first place. We will argue that this research question implies assumptions which we do not consider warranted in methodological research, that a different research question would be more informative, and how this research question can be fruitfully investigated.

Physical and mental health impact of COVID-19 on children, adolescents, and their families: The Collaborative Outcomes study on Health and Functioning during Infection Times - Children and Adolescents (COH-FIT-C&A).

BACKGROUND: The COVID-19 pandemic has altered daily routines and family functioning, led to closing schools, and dramatically limited social interactions worldwide. Measuring its impact on mental health of vulnerable children and adolescents is crucial. METHODS: The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT - www.coh-fit.com) is an on-line anonymous survey, available in 30 languages, involving >230 investigators from 49 countries supported by national/international professional associations. COH-FIT has thee waves (until the pandemic is declared over by the WHO, and 6-18 months plus 24-36 months after its end). In addition to adults, COH-FIT also includes adolescents (age 14-17 years), and children (age 6-13 years), recruited via non-probability/snowball and representative sampling and assessed via self-rating and parental rating. Non-modifiable/modifiable risk factors/treatment targets to inform prevention/intervention programs to promote health and prevent mental and physical illness in children and adolescents will be generated by COH-FIT. Co-primary outcomes are changes in well-being (WHO-5) and a composite psychopathology P-Score. Multiple behavioral, family, coping strategy and service utilization factors are also assessed, including functioning and quality of life. RESULTS: Up to June 2021, over 13,000 children and adolescents from 59 countries have participated in the COH-FIT project, with representative samples from eleven countries. LIMITATIONS: Cross-sectional and anonymous design. CONCLUSIONS: Evidence generated by COH-FIT will provide an international estimate of the COVID-19 effect on children's, adolescents' and families', mental and physical health, well-being, functioning and quality of life, informing the formulation of present and future evidence-based interventions and policies to minimize adverse effects of the present and future pandemics on youth.

The collaborative outcomes study on health and functioning during infection times in adults (COH-FIT-Adults): Design and methods of an international online survey targeting physical and mental health effects of the COVID-19 pandemic.

BACKGROUND: . High-quality comprehensive data on short-/long-term physical/mental health effects of the COVID-19 pandemic are needed. METHODS: . The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is an international, multi-language (n=30) project involving >230 investigators from 49 countries/territories/regions, endorsed by national/international professional associations. COH-FIT is a multi-wave, on-line anonymous, cross-sectional survey [wave 1: 04/2020 until the end of the pandemic, 12 months waves 2/3 starting 6/24 months threreafter] for adults, adolescents (14-17), and children (6-13), utilizing non-probability/snowball and representative sampling. COH-FIT aims to identify non-modifiable/modifiable risk factors/treatment targets to inform prevention/intervention programs to improve social/health outcomes in the general population/vulnerable subgrous during/after COVID-19. In adults, co-primary outcomes are change from pre-COVID-19 to intra-COVID-19 in well-being (WHO-5) and a composite psychopathology P-Score. Key secondary outcomes are a P-extended score, global mental and physical health. Secondary outcomes include health-service utilization/functioning, treatment adherence, functioning, symptoms/behaviors/emotions, substance use, violence, among others. RESULTS: . Starting 04/26/2020, up to 14/07/2021 >151,000 people from 155 countries/territories/regions and six continents have participated. Representative samples of ≥1,000 adults have been collected in 15 countries. Overall, 43.0% had prior physical disorders, 16.3% had prior mental disorders, 26.5% were health care workers, 8.2% were aged ≥65 years, 19.3% were exposed to someone infected with COVID-19, 76.1% had been in quarantine, and 2.1% had been COVID 19-positive. LIMITATIONS: . Cross-sectional survey, preponderance of non-representative participants. CONCLUSIONS: . Results from COH-FIT will comprehensively quantify the impact of COVID-19, seeking to identify high-risk groups in need for acute and long-term intervention, and inform evidence-based health policies/strategies during this/future pandemics.

Experimental indications of gardeners' anecdotes that snails interfere with invasive slugs.

The invasive Spanish slug (Arion vulgaris) is an important pest species in agriculture and horticulture in Europe. In the last decades it has spread across the continent where it outcompetes native slug and snail species, thus posing a threat for biodiversity. A popular anecdote suggests to promote Roman snails (Helix pomatia) in gardens because they are able to control A. vulgaris. We examined a potential interrelationship between these two species using a mesocosm experiment with lettuce plants. 13C-15N stable isotope labelling of lettuce allowed us to investigate interactions between Helix and Arion on weight gain/loss and herbivory. Additionally, we wanted to know whether different watering regimes (daily vs. every 3rd day watering of weekly amount) and earthworms alter these interactions. Egg predation of Helix on Arion eggs was further tested in a food-choice experiment. Arion showed a five times higher herbivory per body mass than Helix in a single-species setting. However, in mesocosms containing both species percentage of herbivory per body mass was significantly lower than in Arion-only mesocosms, especially when watered every three days. Overall isotope uptake via eaten lettuce was unaffected by the presence of the other species. Only very little predation (three out of 200 eggs) of Helix on Arion eggs was observed. Our results provide no evidence for a clear dismissal or confirmation of the popular gardener's anecdote that Helix snails have a negative effect on Arion abundance or herbivory.

Temporal patterns of roe deer traffic accidents: Effects of season, daytime and lunar phase.

Wildlife-related accidents, especially deer-vehicle accidents, pose a serious problem for road safety and animal protection in many countries. Knowledge of spatial and temporal patterns of deer-vehicle accidents is inevitable for accident analysis and mitigation efforts with temporal deer-vehicle accident data being much more difficult to obtain in sufficient data quality. We described the temporal patterns of roe deer (Capreolus capreolus) roadkills occurring in the period 2002-2006 in southeastern Austria. Using a comprehensive dataset, consisting of 11.771 data points, we examined the influence of different time units (i.e. season, month, day of week, day of year), illumination categories (coarse and fine temporal resolution) and lunar phases on deer-vehicle accidents by performing linear and generalized additive models. Thereby, we identified peak accident periods within the analyzed time units. Highest frequencies of deer-vehicle accidents occurred in November, May and October, on Fridays, and during nights. Relationships between lunar phases and roe deer-vehicle accidents were analysed, providing evidence for high frequencies of deer-vehicle accidents during full moon phases. We suggest that deer-vehicle accidents are dependent both on human activity in traffic and wildlife activity, which is in turn affected by phenology, intra- and interspecific competition, climatic and astronomical events. Our results highlight, that short-term mitigation measures (e.g. traffic controls and speed limits) can be highly effective to reduce deer-vehicle accidents, but should be flexibly adapted to specific temporal periods.

Contamination of wounds with fecal bacteria in immuno-suppressed mice.

Immunocompromised patients are predisposed to chronically infected wounds. Especially ulcers in the dorsal region often experience secondary polymicrobial infections. However, current wound infection models mostly use single-strain bacteria. To mimic clinically occurring infections caused by fecal contamination in immunocompromised/immobile patients, which differ significantly from single-strain infections, the present study aimed at the establishment of a new mouse model using infection by fecal bacteria. Dorsal circular excision wounds in immunosuppressed mice were infected with fecal slurry solution in several dilutions up to 1:8,000. Impact of immunosuppressor, bacterial load and timing on development of wound infections was investigated. Wounds were analyzed by scoring, 3D imaging and swab analyses. Autofluorescence imaging was not successful. Dose-finding of cyclophosphamide-induced immunosuppression was necessary for establishment of bacterial wound infections. Infection with fecal slurry diluted 1:166 to 1:400 induced significantly delayed wound healing (p < 0.05) without systemic reactions. Swab analyses post-infection matched the initial polymicrobial suspension. The customized wound score confirmed significant differences between the groups (p < 0.05). Here we report the establishment of a simple, new mouse model for clinically occurring wound infections by fecal bacteria and the evaluation of appropriate wound analysis methods. In the future, this model will provide a suitable tool for the investigation of complex microbiological interactions and evaluation of new therapeutic approaches.

Commercial glyphosate-based herbicides effects on springtails (Collembola) differ from those of their respective active ingredients and vary with soil organic matter content.

Glyphosate-based herbicides (GBH) are currently the most widely used agrochemicals for weed control. Environmental risk assessments (ERA) on nontarget organisms mostly consider the active ingredients (AIs) of these herbicides, while much less is known on effects of commercial GBH formulations that are actually applied in the field. Moreover, it is largely unknown to what extent different soil characteristics alter potential side effects of herbicides. We conducted a greenhouse experiment growing a model weed population of Amaranthus retroflexus in arable field soil with either 3.0 or 4.1% soil organic matter (SOM) content and treated these weeds either with GBHs (Roundup LB Plus, Touchdown Quattro, Roundup PowerFlex) or their respective AIs (isopropylammonium, diammonium or potassium salts of glyphosate) at recommended dosages. Control pots were mechanically weeded. Nontarget effects were assessed on the surface activity of the springtail species Sminthurinus niger (pitfall trapping) and litter decomposition in the soil (teabag approach). Both GBHs and AIs increased the surface activity of springtails compared to control pots; springtail activity was higher under GBHs than under corresponding AIs. Stimulation of springtail activity was much higher in soil with higher SOM content than with low SOM content (significant treatment x SOM interaction). Litter decomposition was unaffected by GBHs, AIs or SOM levels. We suggest that ERAs for pesticides should be performed with actually applied herbicides rather than only on AIs and should also consider influences of different soil properties.

A subcellular proteome atlas of the yeast Komagataella phaffii.

The compartmentalization of metabolic and regulatory pathways is a common pattern of living organisms. Eukaryotic cells are subdivided into several organelles enclosed by lipid membranes. Organelle proteomes define their functions. Yeasts, as simple eukaryotic single cell organisms, are valuable models for higher eukaryotes and frequently used for biotechnological applications. While the subcellular distribution of proteins is well studied in Saccharomyces cerevisiae, this is not the case for other yeasts like Komagataella phaffii (syn. Pichia pastoris). Different to most well-studied yeasts, K. phaffii can grow on methanol, which provides specific features for production of heterologous proteins and as a model for peroxisome biology. We isolated microsomes, very early Golgi, early Golgi, plasma membrane, vacuole, cytosol, peroxisomes and mitochondria of K. phaffii from glucose- and methanol-grown cultures, quantified their proteomes by liquid chromatography-electrospray ionization-mass spectrometry of either unlabeled or tandem mass tag-labeled samples. Classification of the proteins by their relative enrichment, allowed the separation of enriched proteins from potential contaminants in all cellular compartments except the peroxisomes. We discuss differences to S. cerevisiae, outline organelle specific findings and the major metabolic pathways and provide an interactive map of the subcellular localization of proteins in K. phaffii.

Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2.

Process analytical technology combines understanding and control of the process with real-time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi-angle light scattering, refractive index, attenuated total reflection Fourier-transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double-stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co-eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1-28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier-transform infrared spectral information was important to achieve prediction errors of 200 (2-6579 ppm) and 340 ppm (8-3773 ppm), respectively.

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time.

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid-infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real-time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time- and labor-intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.

Changes in Chromosome Counts and Patterns in CHO Cell Lines upon Generation of Recombinant Cell Lines and Subcloning.

Chinese hamster ovary (CHO) cells are the number one production system for therapeutic proteins. A pre-requirement for their use in industrial production of biopharmaceuticals is to be clonal, thus originating from a single cell in order to be phenotypically and genomically identical. In the present study it was evaluated whether standard procedures, such as the generation of a recombinant cell line in combination with selection for a specific and stable phenotype (expression of the recombinant product) or subcloning have any impact on karyotype stability or homogeneity in CHO cells. Analyses used were the distribution of chromosome counts per cell as well as chromosome painting to identify specific karyotype patterns within a population. Results indicate that subclones both of the host and the recombinant cell line are of comparable heterogeneity and (in)stability as the original pool. In contrast, the rigorous selection for a stably expressing phenotype generated cell lines with fewer variation and more stable karyotypes, both at the level of the sorted pool and derivative subclones. We conclude that the process of subcloning itself does not contribute to an improved karyotypic homogeneity of a population, while the selection for a specific cell property inherently can provide evolutionary pressure that may lead to improved chromosomal stability as well as to a more homogenous population.

Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting.

Genomic rearrangements are a common phenomenon in rapidly growing cell lines such as Chinese hamster ovary (CHO) cells, a feature that in the context of production of biologics may lead to cell line and product instability. Few methods exist to assess such genome wide instability. Here, we use the population distribution of chromosome numbers per cell as well as chromosome painting to quantify the karyotypic variation in several CHO host cell lines. CHO-S, CHO-K1 8 mM glutamine, and CHO-K1 cells adapted to grow in media containing no glutamine were analyzed over up to 6 months in culture. All three cell lines were clearly distinguishable by their chromosome number distribution and by the specific chromosome rearrangements that were present in each population. Chromosome Painting revealed a predominant karyotype for each cell line at the start of the experiment, completed by a large number of variants present in each population. Over time in culture, the predominant karyotype changed for CHO-S and CHO-K1, with the diversity increasing and new variants appearing, while CHO-K1 0 mM Gln preferred chromosome pattern increased in percent of the population over time. As control, Chinese hamster lung fibroblasts were shown to also contain an increasing number of variants over time in culture.

Associations of growth patterns and islet autoimmunity in children with increased risk for type 1 diabetes: a functional analysis approach.

BACKGROUND: Several studies indicate associations between early growth and type 1 diabetes (T1D). However, it remains an open question whether these findings can be translated to typical growth patterns associated with increased risk for T1D-associated islet autoimmunity. METHODS: We analyzed pooled data from 2236 children followed up in two large prospective German birth cohorts with a genetically increased risk for T1D including 18 564 measurements of height and weight, which were transformed to sex- and age-specific standard deviation scores (SDS). A total of 191 children developed any islet autoantibodies, 101 multiple islet autoantibodies. We applied a model-based clustering technique to derive typical height and body mass index (BMI) growth patterns, stratified for maternal T1D status. These patterns were used to predict islet autoimmunity in logistic regression models, adjusted for potential confounders. RESULTS: Growth patterns were not associated with islet autoimmunity in the whole dataset and in children of diabetic mothers, respectively. In children of non-diabetic mothers ,however, islet autoimmunity was associated with rapidly increasing BMI SDS values until the age of 3 yr [adjusted odds ratio (95% confidence interval): 2.02 (1.03, 3.73) for development of any islet autoantibodies) and with consistently above average height SDS values [odds ratio: 2.21 (1.15, 4.17)]. In contrast, a pattern of high height SDS values at birth followed by a decrease to average values after 3 yr was associated with a reduced rate of islet autoimmunity [odds ratio: 0.16 (0.01, 0.62)]. CONCLUSION: Early growth patterns may be associated with T1D-related islet autoimmunity risk in children of non-diabetic mothers.

Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc.

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations.

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical "quality by testing" to rational, knowledge-based "quality by design" approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real-time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed-batch production processes with varying cultivation conditions employing a comprehensive on- and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two-dimensional multi-wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.

A review on the temporal pattern of deer-vehicle accidents: impact of seasonal, diurnal and lunar effects in cervids.

The increasing number of deer-vehicle-accidents (DVAs) and the resulting economic costs have promoted numerous studies on behavioural and environmental factors which may contribute to the quantity, spatiotemporal distribution and characteristics of DVAs. Contrary to the spatial pattern of DVAs, data of their temporal pattern is scarce and difficult to obtain because of insufficient accuracy in available datasets, missing standardization in data aquisition, legal terms and low reporting rates to authorities. Literature of deer-traffic collisions on roads and railways is reviewed to examine current understanding of DVA temporal trends. Seasonal, diurnal and lunar peak accident periods are identified for deer, although seasonal pattern are not consistent among and within species or regions and data on effects of lunar cycles on DVAs is almost non-existent. Cluster analysis of seasonal DVA data shows nine distinct clusters of different seasonal DVA pattern for cervid species within the reviewed literature. Studies analyzing the relationship between time-related traffic predictors and DVAs yield mixed results. Despite the seasonal dissimilarity, diurnal DVA pattern are comparatively constant in deer, resulting in pronounced DVA peaks during the hours of dusk and dawn frequently described as bimodal crepuscular pattern. Behavioural aspects in activity seem to have the highest impact in DVAs temporal trends. Differences and variations are related to habitat-, climatic- and traffic characteristics as well as effects of predation, hunting and disturbance. Knowledge of detailed temporal DVA pattern is essential for prevention management as well as for the application and evaluation of mitigation measures.