Many roads to success: alternative routes to building an economic shell in land snails.

Land snails exhibit an extraordinary variety of shell shapes. The way shells are constructed underlies biological and mechanical constraints that vary across gastropod clades. Here, we quantify shell geometry of the two largest groups, Stylommatophora and Cyclophoroidea, to assess the potential causes for variation in shell shape and its relative frequency. Based on micro-computed tomography scans, we estimate material efficiency through 2D and 3D generalizations of the isoperimetric ratio, quantifying the ratios between area and perimeter of whorl cross-sections (2D) and shell volume and surface (3D), respectively. We find that stylommatophorans optimize material usage through whorl overlap, which may have promoted the diversification of flat-shelled species. Cyclophoroids are bound to a circular cross-section because of their operculum; flat shells are comparatively rare. Both groups show similar solutions for tall shells, where local geometry has a smaller effect because of the double overlap between previous and current whorls. Our results suggest that material efficiency is a driving factor in the selection of shell geometry. Essentially, the evolutionary success of Stylommatophora likely roots in their higher flexibility to produce an economic shell.

The fossil record of freshwater Gastropoda - a global review.

Gastropoda are an exceptionally successful group with a rich and diverse fossil record. They have conquered land and freshwater habitats multiple times independently and have dispersed across the entire globe. Since they are important constituents of fossil assemblages, they are often used for palaeoecological reconstruction, biostratigraphic correlations, and as model groups to study morphological and taxonomic evolution. While marine faunas and their evolution have been a common subject of study, the freshwater component of the fossil record has attracted much less attention, and a global overview is lacking. Here, I review the fossil record of freshwater gastropods on a global scale, ranging from their origins in the late Palaeozoic to the Pleistocene. As compiled here, the global fossil record of freshwater Gastropoda includes 5182 species in 490 genera, 44 families, and 12 superfamilies over a total of ~340 million years. Following a slow and poorly known start in the late Palaeozoic, diversity slowly increased during the Mesozoic. Diversity culminated in an all-time high in the Neogene, relating to diversification in numerous long-lived (ancient) lakes in Europe. I summarise well-documented and hypothesised freshwater colonisation events and compare the patterns found in freshwater gastropods to those in land snails. Furthermore, I discuss potential preservation and sampling biases, as well as the main drivers underlying species diversification in fresh water on a larger scale. In that context, I particularly highlight the importance of long-lived lakes as islands and archives of evolution and expand a well-known concept in ecology and evolution to a broader spectrum: scale-independent ecological opportunity.

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors.

More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development.

Short-term paleogeographic reorganizations and climate events shaped diversification of North American freshwater gastropods over deep time.

What controls species diversity and diversification is one of the major questions in evolutionary biology and paleontology. Previous studies have addressed this issue based on various plant and animal groups, geographic regions, and time intervals. However, as most previous research focused on terrestrial or marine ecosystems, our understanding of the controls on diversification of biota (and particularly invertebrates) in freshwater environments in deep time is still limited. Here, we infer diversification rates of North American freshwater gastropods from the Late Triassic to the Pleistocene and explore potential links between shifts in speciation and extinction and major changes in paleogeography, climate, and biotic interactions. We found that variation in the speciation rate is best explained by changes in continental fragmentation, with rate shifts coinciding with major paleogeographic reorganizations in the Mesozoic, in particular the retreat of the Sundance Sea and subsequent development of the Bighorn wetland and the advance of the Western Interior Seaway. Climatic events in the Cenozoic (Middle Eocene Climate Optimum, Miocene Climate Optimum) variably coincide with shifts in speciation and extinction as well, but no significant long-term association could be detected. Similarly, no influence of diversity dependence was found across the entire time frame of ~ 214 Myr. Our results indicate that short-term climatic events and paleogeographic changes are relevant to the diversification of continental freshwater biota, while long-term trends have limited effect.

A new freshwater snail genus and species (Gastropoda: Caenogastropoda, Cochliopidae) with extremely spinous shells from sub-recent spring deposits in northeastern Mexico.

A new monotypic genus of freshwater snail from late Holocene spring deposits in Viesca, Coahuila (Mexico), is described based on shell morphology. Spinopyrgus luismaedai n. gen. et n. sp. has two to three carinate shells with long and wide shovel-shaped spines, strong axial ridges and a pointed protoconch. All sculptural ornamentations on the teleoconch are part of the calcareous shell material and not projections of the periostracum. This combination of shell features and their almost marine-like appearance is unknown among North American recent and fossil freshwater snails. Because of its shell characteristics, we placed the new genus tentatively in the Cochliopidae. The springs of Viesca dried up in the second half of the 20th century so that any living occurrence of this species in neighboring areas is unlikely, rendering the new genus and species possibly extinct.

Adapting mark-recapture methods to estimating accepted species-level diversity: a case study with terrestrial Gastropoda.

We introduce a new method of estimating accepted species diversity by adapting mark-recapture methods to comparisons of taxonomic databases. A taxonomic database should become more complete over time, so the error bar on an estimate of its completeness and the known diversity of the taxon it treats will decrease. Independent databases can be correlated, so we use the time course of estimates comparing them to understand the effect of correlation. If a later estimate is significantly larger than an earlier one, the databases are positively correlated, if it is significantly smaller, they are negatively correlated, and if the estimate remains roughly constant, then the correlations have averaged out. We tested this method by estimating how complete MolluscaBase is for accepted names of terrestrial gastropods. Using random samples of names from an independent database, we determined whether each name led to a name accepted in MolluscaBase. A sample tested in August 2020 found that 16.7% of tested names were missing; one in July 2021 found 5.3% missing. MolluscaBase grew by almost 3,000 accepted species during this period, reaching 27,050 species. The estimates ranged from 28,409 ± 365 in 2021 to 29,063 ± 771 in 2020. All estimates had overlapping 95% confidence intervals, indicating that correlations between the databases did not cause significant problems. Uncertainty beyond sampling error added 475 ± 430 species, so our estimate for accepted terrestrial gastropods species at the end of 2021 is 28,895 ± 630 species. This estimate is more than 4,000 species higher than previous ones. The estimate does not account for ongoing flux of species into and out of synonymy, new discoveries, or changing taxonomic methods and concepts. The species naming curve for terrestrial gastropods is still far from reaching an asymptote, and combined with the additional uncertainties, this means that predicting how many more species might ultimately be recognized is presently not feasible. Our methods can be applied to estimate the total number of names of Recent mollusks (as opposed to names currently accepted), the known diversity of fossil mollusks, and known diversity in other phyla.

Interpolating Strange Attractors via Fractional Brownian Bridges.

We present a novel method for interpolating univariate time series data. The proposed method combines multi-point fractional Brownian bridges, a genetic algorithm, and Takens' theorem for reconstructing a phase space from univariate time series data. The basic idea is to first generate a population of different stochastically-interpolated time series data, and secondly, to use a genetic algorithm to find the pieces in the population which generate the smoothest reconstructed phase space trajectory. A smooth trajectory curve is hereby found to have a low variance of second derivatives along the curve. For simplicity, we refer to the developed method as PhaSpaSto-interpolation, which is an abbreviation for phase-space-trajectory-smoothing stochastic interpolation. The proposed approach is tested and validated with a univariate time series of the Lorenz system, five non-model data sets and compared to a cubic spline interpolation and a linear interpolation. We find that the criterion for smoothness guarantees low errors on known model and non-model data. Finally, we interpolate the discussed non-model data sets, and show the corresponding improved phase space portraits. The proposed method is useful for interpolating low-sampled time series data sets for, e.g., machine learning, regression analysis, or time series prediction approaches. Further, the results suggest that the variance of second derivatives along a given phase space trajectory is a valuable tool for phase space analysis of non-model time series data, and we expect it to be useful for future research.

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data.

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.

Onset of Late Cretaceous diversification in Europe's freshwater gastropod fauna links to global climatic and biotic events.

The Mesozoic rise of the European freshwater gastropod fauna is still poorly understood. Compared to the well documented Cenozoic history, little is known about the patterns and processes underlying the early diversification preceding their extinction crisis at the K-Pg boundary. We assess what is probably a first pulse of diversification of the Cenozoic-type fauna in the Late Cretaceous along with the potential abiotic and biotic controls for shifts in species diversification. We find strong support that the increase in the speciation rate in the Santonian (~ 85 Myr ago) is linked to a global sea level rise, which caused extensive flooding of continental areas and the formation of vast brackish-water ecosystems. The following decline of the speciation rate coincides with a rise in diversity and reflects increasing interspecific competition. The peak in the speciation rate postdates the Cenomanian-Turonian Thermal Maximum, which probably limited the potential for diversification among freshwater gastropods due to ecological constraints. The peak coincides moreover with the end phase of the Cretaceous Terrestrial Revolution, which sparked the radiation of angiosperms. The expansion and diversification of flowering plants, being an important food source for freshwater gastropods today, could have formed a necessary basis for gastropod diversification.

Drivers of diversification in freshwater gastropods vary over deep time.

Unravelling the drivers of species diversification through geological time is of crucial importance for our understanding of long-term evolutionary processes. Numerous studies have proposed different sets of biotic and abiotic controls of speciation and extinction rates, but typically they were inferred for a single, long geological time frame. However, whether the impact of biotic and abiotic controls on diversification changes over time is poorly understood. Here, we use a large fossil dataset, a multivariate birth-death model and a comprehensive set of biotic and abiotic predictors, including a new index to quantify tectonic complexity, to estimate the drivers of diversification for European freshwater gastropods over the past 100 Myr. The effects of these factors on origination and extinction are estimated across the entire time frame as well as within sequential time windows of 20 Myr each. Our results find support for temporal heterogeneity in the factors associated with changes in diversification rates. While the factors impacting speciation and extinction rates vary considerably over time, diversity-dependence and topography are consistently important. Our study highlights that a high level of heterogeneity in diversification rates is best captured by incorporating time-varying effects of biotic and abiotic factors.

[Management of joint empyema]. / Management von Gelenkempyemen.

Joint empyema, also known as septic arthritis, is a severe disease associated with considerable morbidity and mortality. Failing to initiate immediate treatment can result in irreversible joint destruction within a short time. The knee joint is most frequently involved, followed by the shoulder and hip joints. Small joints are rarely affected. Typical risk factors include immunosuppression, renal insufficiency, diabetes mellitus and previous joint interventions. An early targeted diagnostic work-up and initiation of therapeutic steps is crucial to avoid irreversible joint destruction. Joint aspiration for diagnostic purposes is essential and should be performed immediately when a septic arthritis is suspected. An important differential diagnosis is metabolic arthritis (gout and chondrocalcinosis), which typically presents in a similar way. A differentiation from septic arthritis is important as metabolic arthritis requires a completely different treatment. After confirming the diagnosis, treatment consisting of a surgical procedure combined with antibiotics is initiated. In most cases an arthroscopic intervention is sufficient. In severe cases the arthroscopy needs to be repeated. An arthrotomy must be carried out only rarely. A pre-emptive antibiotic treatment is initially administered and is later adjusted according to the resistogram. This article gives an overview on the pathophysiology, diagnostics and general management of joint empyema.

Combining Fractional Derivatives and Machine Learning: A Review.

Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is that fractional derivatives inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches.

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review.

Measures of signal complexity, such as the Hurst exponent, the fractal dimension, and the Spectrum of Lyapunov exponents, are used in time series analysis to give estimates on persistency, anti-persistency, fluctuations and predictability of the data under study. They have proven beneficial when doing time series prediction using machine and deep learning and tell what features may be relevant for predicting time-series and establishing complexity features. Further, the performance of machine learning approaches can be improved, taking into account the complexity of the data under study, e.g., adapting the employed algorithm to the inherent long-term memory of the data. In this article, we provide a review of complexity and entropy measures in combination with machine learning approaches. We give a comprehensive review of relevant publications, suggesting the use of fractal or complexity-measure concepts to improve existing machine or deep learning approaches. Additionally, we evaluate applications of these concepts and examine if they can be helpful in predicting and analyzing time series using machine and deep learning. Finally, we give a list of a total of six ways to combine machine learning and measures of signal complexity as found in the literature.

Taming the Chaos in Neural Network Time Series Predictions.

Machine learning methods, such as Long Short-Term Memory (LSTM) neural networks can predict real-life time series data. Here, we present a new approach to predict time series data combining interpolation techniques, randomly parameterized LSTM neural networks and measures of signal complexity, which we will refer to as complexity measures throughout this research. First, we interpolate the time series data under study. Next, we predict the time series data using an ensemble of randomly parameterized LSTM neural networks. Finally, we filter the ensemble prediction based on the original data complexity to improve the predictability, i.e., we keep only predictions with a complexity close to that of the training data. We test the proposed approach on five different univariate time series data. We use linear and fractal interpolation to increase the amount of data. We tested five different complexity measures for the ensemble filters for time series data, i.e., the Hurst exponent, Shannon's entropy, Fisher's information, SVD entropy, and the spectrum of Lyapunov exponents. Our results show that the interpolated predictions consistently outperformed the non-interpolated ones. The best ensemble predictions always beat a baseline prediction based on a neural network with only a single hidden LSTM, gated recurrent unit (GRU) or simple recurrent neural network (RNN) layer. The complexity filters can reduce the error of a random ensemble prediction by a factor of 10. Further, because we use randomly parameterized neural networks, no hyperparameter tuning is required. We prove this method useful for real-time time series prediction because the optimization of hyperparameters, which is usually very costly and time-intensive, can be circumvented with the presented approach.

A revision of the Pontocaspian gastropods of the subfamily Caspiinae (Caenogastropoda: Hydrobiidae).

The Caspiinae comprise small gastropod species inhabiting low-salinity environments of the Pontocaspian region, specifically the Caspian Sea and the limans, estuaries and lower river reaches of the northern Black Sea Basin. There is considerable discrepancy in the literature as to which taxa are attributed to this hydrobiid subfamily, which of them are valid and what rank they have. Moreover, the generic classification is not agreed upon. Here, we aim at elucidating systematic relationships and species identities by a thorough taxonomic and systematic revision of the Caspiinae, including a detailed morphological study along with information on biogeography, ecology, conservation and fossil record. We use historical and recent collections from all around the Pontocaspian region acquired over the past 170 years, including several type series that were only recently discovered as well as selected Pleistocene and Holocene materials. Several of the species are illustrated properly here for the first time, using both macrophotographs and scanning electron microscopy images to facilitate evaluation of morphological details. The genus Andrusovia, which has been classified in a number of different families and even gastropod subclasses before, is here attributed to the Caspiinae. By designating a lectotype for the type species, A. dybowskii, we finally fix the identity of this previously ambiguous species and genus. Our revision yields four genera and eleven accepted species of Caspiinae for the modern Pontocaspian fauna: Andrusovia (1), Caspia (1), Clathrocaspia (7) and Ulskia (2). In addition, two species of uncertain status (1 nomen dubium, 1 taxon inquirendum) are discussed, and two species introduced as Caspiinae are here found not to belong in that group. Andrusovia antecessor sp. nov. and Andrusovia cyrensis sp. nov. are introduced as new (sub)fossil species from the Caspian Sea.

Convergent evolution of specialized generalists: Implications for phylogenetic and functional diversity of carabid feeding groups.

Closely related species are often assumed to be functionally similar. Phylogenetic information is thus widely used to infer functional diversity and assembly of communities. In contrast, evolutionary processes generating functional similarity of phylogenetically distinct taxa are rarely addressed in this context.To investigate the impact of convergent evolution on functional diversity (FD) and phylogenetic diversity (PD), we reconstructed the phylogenetic structure of carabid trophic groups. We then analyzed the mandible shapes using geometric morphometrics to link specialization in functional morphology with feeding specialization among herbivores, generalist carnivores, and specialized consumers of Collembola.Our results show that carabid feeding groups are paraphyletic. Herbivory evolved at least twice and specialization to Collembola predation at least three times. Species within feeding groups share a remarkably similar mandible morphology, which evolved convergently. While specialized mandibles of herbivores and collembolan specialists represent an adaptation to their main food source, the particular mandible morphologies do not necessarily reflect the degree of food specialization within feeding groups. Only a few species with a specialized herbivorous mandible may occasionally feed on animals, but the range of specific food resources in generalist carnivore species is large, despite an almost identical mandible shape.Thus, convergent evolution in specialized feeding groups reverses the relationship between PD and functional similarity compared with generalist carnivores. We conclude that phylogenetic relationship is a poor proxy of FD in carabids. Moreover, the inconsistencies between relatedness, morphological adaptation, and ecological function require caution in the characterization of functional groups. Rather than assuming general relationships between PD and FD, we suggest integrating the analysis of evolutionary processes into functional community analyses.

Deep drilling reveals massive shifts in evolutionary dynamics after formation of ancient ecosystem.

The scarcity of high-resolution empirical data directly tracking diversity over time limits our understanding of speciation and extinction dynamics and the drivers of rate changes. Here, we analyze a continuous species-level fossil record of endemic diatoms from ancient Lake Ohrid, along with environmental and climate indicator time series since lake formation 1.36 million years (Ma) ago. We show that speciation and extinction rates nearly simultaneously decreased in the environmentally dynamic phase after ecosystem formation and stabilized after deep-water conditions established in Lake Ohrid. As the lake deepens, we also see a switch in the macroevolutionary trade-off, resulting in a transition from a volatile assemblage of short-lived endemic species to a stable community of long-lived species. Our results emphasize the importance of the interplay between environmental/climate change, ecosystem stability, and environmental limits to diversity for diversification processes. The study also provides a new understanding of evolutionary dynamics in long-lived ecosystems.

Structure-Permeability Relationship of Semipeptidic Macrocycles-Understanding and Optimizing Passive Permeability and Efflux Ratio.

We herein report the first thorough analysis of the structure-permeability relationship of semipeptidic macrocycles. In total, 47 macrocycles were synthesized using a hybrid solid-phase/solution strategy, and then their passive and cellular permeability was assessed using the parallel artificial membrane permeability assay (PAMPA) and Caco-2 assay, respectively. The results indicate that semipeptidic macrocycles generally possess high passive permeability based on the PAMPA, yet their cellular permeability is governed by efflux, as reported in the Caco-2 assay. Structural variations led to tractable structure-permeability and structure-efflux relationships, wherein the linker length, stereoinversion, N-methylation, and peptoids site-specifically impact the permeability and efflux. Extensive nuclear magnetic resonance, molecular dynamics, and ensemble-based three-dimensional polar surface area (3D-PSA) studies showed that ensemble-based 3D-PSA is a good predictor of passive permeability.

The value of a single character: the Paleogene European land snail Ferussina Grateloup, 1827 is likely a cyclophorid (Gastropoda, Caenogastropoda).

Ferussina Grateloup, 1827 is a European Paleogene land snail genus, which is currently classified in its own family, the Ferussinidae Wenz, 1923 (1915), in the superfamily Cyclophoroidea. The shell of this genus is remarkable by its last quarter whorl turning towards the apex instead of away from it, which is an unusual trait in terrestrial snails. We show, however, that this trait has evolved at least nine times in terrestrial Eupulmonata and Caenogastropoda, and it does not justify distinction at the family level in any of the reported cases. This observation suggests the systematic position of Ferussina should not be based on the apexward-turning last quarter whorl alone but instead on the general morphology of the shell. As a result, we re-evaluate the systematic position of the Ferussinidae and treat it as a subfamily of the Cyclophoridae.

Old lake versus young taxa: a comparative phylogeographic perspective on the evolution of Caspian Sea gastropods (Neritidae: Theodoxus).

The Caspian Sea has been a highly dynamic environment throughout the Quaternary and witnessed major oscillations in lake level, which were associated with changes in salinity and habitat availability. Such environmental pressures are considered to drive strong phylogeographic structures in species by forcing populations into suitable refugia. However, little is actually known on the effect of lake-level fluctuations in the Caspian Sea on its aquatic biota. We compared the phylogeographic patterns of the aquatic Neritidae snail genus Theodoxus across the Pontocaspian region with refugial populations in southern Iran. Three gene fragments were used to determine relationships and divergence times between the sampled populations in both groups. A dated phylogeny and statistical haplotype networks were generated in conjunction with the analyses of molecular variance and calculations of isolation by distance using distance-based redundancy analyses. Extended Bayesian skyline plots were constructed to assess demographic history. Compared with the southern Iranian populations, we found little phylogeographic structure for the Pontocaspian Theodoxus group, with more recent diversification, homogeneity of haplotypes across the Pontocaspian region and a relatively stable demographic history since the Middle Pleistocene. Our results argue against a strong influence of Caspian Sea low stands on the population structure post the early Pleistocene, whereas high stands may have increased the dispersal possibilities and homogenization of haplotypes across the Pontocaspian region during this time. However, during the early Pleistocene, a more dramatic low stand in the Caspian Sea, around a million years ago, may have caused the reduction in Theodoxus diversity to a single lineage in the region. In addition, our results provide new insights into Theodoxus taxonomy and outlooks for regional conservation.