Finding navigation cues near fishways.

Many fish species depend on migration for various parts of their life cycle. Well-known examples include diadromous fish such as salmon and eels that need both fresh water and salt water to complete their life cycle. Migration also occurs within species that depend only on fresh water. In recent decades, anthropogenic pressures on freshwater systems have increased greatly, and have resulted, among other effects, in drastic habitat fragmentation. Fishways have been developed to mitigate the resulting habitat fragmentation, but these are not always effective. To improve fishway efficiency, the variety of navigation cues used by fish must be better understood: fish use a multitude of sensory inputs ranging from flow variables to olfactory cues. The reaction of a fish is highly dependent on the intensity of the cue, the fish species involved, and individual traits. Recently developed monitoring technologies allow us to gain insights into different combinations of environmental and physiological conditions. By combining fish behavioural models with environmental models, interactions among these components can be investigated. Several methods can be used to analyse fish migration, with state-space models, hidden Markov models, and individual-based models potentially being the most relevant since they can use individual data and can tie them to explicit spatial locations within the considered system. The aim of this review is to analyse the navigational cues used by fish and the models that can be applied to gather knowledge on these processes. Such knowledge could greatly improve the design and operation of fishways for a wider range of fish species and conditions.

Individual variation in the habitat selection of upstream migrating fish near a barrier.

BACKGROUND: Migration is a vital element of the life cycle of many freshwater fish species but is increasingly hampered globally by riverine barriers. Fish passes are a common approach to enable migration past barriers but are often ineffective. More knowledge is required on fish behaviour as they approach barriers such as habitat preferences. METHODS: We evaluate the habitat selection of two upstream migrating fish species, barbel Barbus barbus and grayling Thymallus thymallus, at a hydropower plant in southern Germany, considering individual variation and population trends. Fish were tracked via fine-scale 2D acoustic telemetry in 2018 during their spawning migration. Step selection functions were used to evaluate selection of hydraulic parameters by the fish for a time step of 20 s. Exploratory models were built via model selection for each individual fish, to evaluate the extent of individual variation in model structure. A population model was developed for each species by averaging coefficients from individual models to describe general trends. The extent of individual variation was determined and confidence intervals for the population model coefficients were calculated. RESULTS: Fish varied greatly in individual model structure though common terms were apparent in both species, such as depth, flow velocity, the angular difference between fish and velocity, and the logarithm of the step length. Final population models for barbel included several parameters describing habitat selection and displacement. Barbel selected for faster flows, deeper water, and higher spatial velocity gradients. In addition, they selected to move more with the flow than against. Interactions were also present between habitat parameters, suggesting selection is context dependent. Barbel movement speed also changed with depth, flow velocity and spatial velocity gradient. With grayling, terms often had contrasting effects among individuals and thus general trends could not be distinguished for most terms. CONCLUSION: Our findings demonstrate habitat selection by upstream migrating fish approaching a fish pass and differences in individual selection which may have an impact on barrier management. Step selection functions are a promising approach and can provide useful insight into habitat selection and movement by migrating freshwater fish in an altered river system.

A comprehensive assessment of plastic remediation technologies.

The global presence of plastic litter and its accumulation in the environment has become an issue of concern to the public and policymakers. This concern has triggered innovators in past decades to design and develop a multitude of remediation technologies to prevent plastic from entering the environment, or to clean up legacy litter. This study aims to (i) systematically review the current scientific literature on plastic remediation technologies, (ii) create a 'plastic clean-up and prevention overview' illustrating 124 remediation technologies and 29 characteristics, (iii) qualitatively analyse their key characteristics (e.g., fields of application, targeted plastic), and (iv) investigate challenges and opportunities of clean-up technologies for inland waterways (e.g., canals, rivers) and ports. We identified 61 scientific publications on plastic remediation technologies, until June 2022. Thirty-four of these studies were published within the last three years, demonstrating a growing interest. The presented overview indicates that inland waterways are, so far, the preferred field of application, with 22 technologies specifically designed for cleaning up plastics from inland waterways, and 52 additional ones with the potential to be installed in these locations. Given the importance of clean-up technologies in inland waterways, we highlighted their strengths, weaknesses, opportunities, and threats (SWOT). Our results indicate that, despite the challenges, these technologies provide essential prospects, from improving the environmental quality to raising awareness. Our study is instrumental as it illustrates an up-to-date overview and provides a comprehensive analysis of current in design phase, testing, and in use plastic remediation technologies.

Individual based models for the simulation of fish movement near barriers: Current work and future directions.

River fragmentation is an increasing issue for water managers and conservationists. Barriers such as dams interfere with freshwater fish migration, leading to drastic population declines. While there are a range of widely implemented mitigation approaches, e.g. fish passes, such measures are often inefficient due to suboptimal operation and design. There is increasing need to be able to assess mitigation options prior to implementation. Individual based models (IBMs) are a promising option. IBMs can simulate the fine-scale movement of individual fish within a population as they attempt to find a fish pass, incorporating movement processes themselves. Moreover, IBMs have high transferability to other sites or conditions (e.g. changing mitigation, change in flow conditions), making them potentially valuable for freshwater fish conservation yet their application to the fine-scale movement of fish past barriers is still novel. Here, we present an overview of existing IBMs for fine-scale freshwater fish movement, with emphasis on study species and the parameters driving movement in the models. In this review, we focus on IBMs suitable for the simulation of fish tracks as they approach or pass a single barrier. The selected IBMs for modelling fine-scale freshwater fish movement largely focus on salmonids and cyprinid species. IBMs have many applications in the context of fish passage, such as testing different mitigation options or understanding processes behind movement. Existing IBMs include movement processes such as attraction and rejection behaviours, as reported in literature. Yet some factors affecting fish movement e.g. biotic interactions are not covered by existing IBMs. As the technology available for fine scale data collection continues to advance, such as increasing data linking fish behaviour to hydraulics, IBMs could become a more common tool in the design and implementation of fish bypass structures.

Big-data approaches lead to an increased understanding of the ecology of animal movement.

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries.

Current mitigation strategies to offset marine plastic pollution, a global concern, typically rely on preventing floating debris from reaching coastal ecosystems. Specifically, clean-up technologies are designed to collect plastics by removing debris from the aquatic environment such as rivers and estuaries. However, to date, there is little published data on their potential impact on riverine and estuarine organisms and ecosystems. Multiple parameters might play a role in the chances of biota and organic debris being unintentionally caught within a mechanical clean-up system, but their exact contribution to a potential impact is unknown. Here, we identified four clusters of parameters that can potentially determine the bycatch: (i) the environmental conditions in which the clean-up system is deployed, (ii) the traits of the biota the system interacts with, (iii) the traits of plastic items present in the system, and, (iv) the design and operation of the clean-up mechanism itself. To efficiently quantify and assess the influence of each of the clusters on bycatch, we suggest the use of transparent and objective tools. In particular, we discuss the use of Bayesian Belief Networks (BBNs) as a promising probabilistic modelling method for an evidence-based trade-off between removal efficiency and bycatch. We argue that BBN probabilistic models are a valuable tool to assist stakeholders, prior to the deployment of any clean-up technology, in selecting the best-suited mechanism to collect floating plastic debris while managing potential adverse effects on the ecosystem.

Immunologic profiles of multiple sclerosis treatments reveal shared early B cell alterations.

OBJECTIVE: We undertook a systems immunology approach of the adaptive immune system in multiple sclerosis (MS), overcoming tradeoffs between scale and level of detail, in order to identify the immunologic signature of MS and the changes wrought by current immunomodulatory treatments. METHODS: We developed a comprehensive flow cytometry platform measuring 38 immunologic cell types in the peripheral blood of 245 individuals in a routine clinical setting. These include patients with MS, untreated or receiving any of 4 current immunomodulatory treatments (interferon-ß, glatiramer acetate, natalizumab, or fingolimod), patients with autoimmune thyroid disease, and healthy controls. RESULTS: An increase in memory CD8(+) T cells and B cells was observed in untreated patients with MS. Interferon-ß and fingolimod induce significant changes upon multiple aspects of the peripheral immune system, with an unexpectedly prominent alteration of B cells. Overall, both treatments push the immune system in different directions, with only 2 significant effects shared across these treatments-an increase in transitional B cells and a decrease in class-switched B cells. We further identified heightened B cell-activating factor (BAFF) levels as regulating this shared B cell pathway. CONCLUSIONS: A systems immunology approach established different immunologic profiles induced by current immunomodulatory MS treatments, offering perspectives for personalized medicine. Pathways shared between the immunologic architecture of existing efficacious treatments identify targets for future treatment design.

Genetic variants are major determinants of CSF antibody levels in multiple sclerosis.

Immunological hallmarks of multiple sclerosis include the production of antibodies in the central nervous system, expressed as presence of oligoclonal bands and/or an increased immunoglobulin G index-the level of immunoglobulin G in the cerebrospinal fluid compared to serum. However, the underlying differences between oligoclonal band-positive and -negative patients with multiple sclerosis and reasons for variability in immunoglobulin G index are not known. To identify genetic factors influencing the variation in the antibody levels in the cerebrospinal fluid in multiple sclerosis, we have performed a genome-wide association screen in patients collected from nine countries for two traits, presence or absence of oligoclonal bands (n = 3026) and immunoglobulin G index levels (n = 938), followed by a replication in 3891 additional patients. We replicate previously suggested association signals for oligoclonal band status in the major histocompatibility complex region for the rs9271640*A-rs6457617*G haplotype, correlated with HLA-DRB1*1501, and rs34083746*G, correlated with HLA-DQA1*0301 (P comparing two haplotypes = 8.88 × 10(-16)). Furthermore, we identify a novel association signal of rs9807334, near the ELAC1/SMAD4 genes, for oligoclonal band status (P = 8.45 × 10(-7)). The previously reported association of the immunoglobulin heavy chain locus with immunoglobulin G index reaches strong evidence for association in this data set (P = 3.79 × 10(-37)). We identify two novel associations in the major histocompatibility complex region with immunoglobulin G index: the rs9271640*A-rs6457617*G haplotype (P = 1.59 × 10(-22)), shared with oligoclonal band status, and an additional independent effect of rs6457617*G (P = 3.68 × 10(-6)). Variants identified in this study account for up to 2-fold differences in the odds of being oligoclonal band positive and 7.75% of the variation in immunoglobulin G index. Both traits are associated with clinical features of disease such as female gender, age at onset and severity. This is the largest study population so far investigated for the genetic influence on antibody levels in the cerebrospinal fluid in multiple sclerosis, including 6950 patients. We confirm that genetic factors underlie these antibody levels and identify both the major histocompatibility complex and immunoglobulin heavy chain region as major determinants.

Development and assessment of an integrated ecological modelling framework to assess the effect of investments in wastewater treatment on water quality.

Worldwide, large investments in wastewater treatment are made to improve water quality. However, the impacts of these investments on river water quality are often not quantified. To assess water quality, the European Water Framework Directive (WFD) requires an integrated approach. The aim of this study was to develop an integrated ecological modelling framework for the River Drava (Croatia) that includes physical-chemical and hydromorphological characteristics as well as the ecological river water quality status. The developed submodels and the integrated model showed accurate predictions when comparing the modelled results to the observations. Dissolved oxygen and nitrogen concentrations (ammonium and organic nitrogen) were the most important variables in determining the ecological water quality (EWQ). The result of three potential investment scenarios of the wastewater treatment infrastructure in the city of Varazdin on the EWQ of the River Drava was assessed. From this scenario-based analysis, it was concluded that upgrading the existing wastewater treatment plant with nitrogen and phosphorus removal will be insufficient to reach a good EWQ. Therefore, other point and diffuse pollution sources in the area should also be monitored and remediated to meet the European WFD standards.

No evidence for a role of rare CYP27B1 functional variations in multiple sclerosis.

Association studies have implicated common variants in the 12q14.1 region containing CYP27B1 in multiple sclerosis (MS). Rare CYP27B1 mutations cause autosomal recessive vitamin D-dependent rickets type 1, and it has recently been reported that heterozygous CYP27B1 mutations are associated with increased MS susceptibility and lower active vitamin D levels. By sequencing CYP27B1 in 134 multiplex families and genotyping the most common variant R389H in 2,608 MS patients and 1,987 controls from Italy and Belgium (a total of 4,729 individuals), we were unable to replicate these observations. These results provide evidence against a major role for CYP27B1 mutations in MS.

Rare MEFV variants are not associated with risk to develop multiple sclerosis and severity of disease.

BACKGROUND: Recent studies suggest an association between rare variants in Mediterranean fever (MEFV), the gene underlying the auto-inflammatory disorder Familial Mediterranean Fever (FMF), the risk to develop multiple sclerosis (MS) and severity of MS. OBJECTIVE: The objective of this study is to investigate these findings in a Belgian MS population and to test for association with additional clinical parameters such as treatment response. METHODS: MEFV was sequenced in a cohort of MS patients (N=94) suffering from auto-inflammatory symptoms, systemic side-effects upon interferon-beta (IFN-ß) treatment, or patients in whom glatiramer acetate was started as first choice due to severe fatigue. Five rare non-synonymous variants were detected in this cohort and subsequently genotyped in 915 MS patients and 763 healthy controls. RESULTS: We observed no association between these alleles and susceptibility to MS (p-value=0.99) or disease severity (p-value=0.78). However, we did observe a correlation between carrying an MEFV variant and the development of systemic side-effects upon IFN-ß treatment (p-value=0.022). CONCLUSION: In contrast to recent smaller studies, we did not find an association between carrying a rare variant in the MEFV gene and the risk to develop MS or disease severity. However, carrying rare variants in MEFV was associated with the development of severe systemic side-effects upon IFN-ß treatment.

Progress in multiple sclerosis genetics.

A genetic component in the susceptibility to multiple sclerosis (MS) has long been known, and the first and major genetic risk factor, the HLA region, was identified in the 1970's. However, only with the advent of genome-wide association studies in the past five years did the list of risk factors for MS grow from 1 to over 50. In this review, we summarize the search for MS risk genes and the latest results. Comparison with data from other autoimmune and neurological diseases and from animal models indicates parallels and differences between diseases. We discuss how these translate into an improved understanding of disease mechanisms, and address current challenges such as genotype-phenotype correlations, functional mechanisms of risk variants and the missing heritability.