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SYNE2 mutations have been associated with skeletal and cardiac muscle diseases, including Emery-Dreifuss muscular dystrophy (EDMD). Here, we present a 70-year-old male patient with muscle pain and elevated serum creatine kinase levels in whom whole-exome sequencing revealed a novel heterozygous SYNE2 splice site mutation (NM_182914.3:c.15306+2T>G). This mutation is likely to result in the loss of the donor splice site in intron 82. While a diagnostic muscle biopsy showed unspecific myopathological findings, immunofluorescence analyses of skeletal muscle and dermal cells derived from the patient showed nuclear shape alterations when compared to control cells. In addition, a significantly reduced nesprin-2 giant protein localisation to the nuclear envelope was observed in patient-derived dermal fibroblasts. Our findings imply that the novel heterozygous SYNE2 mutation results in a monoallelic splicing defect of nesprin-2, thereby leading to a rare cause of myalgia and hyperCKemia.

We studied the regeneration of orthotopic and heterotopic tails in larval axolotls. First, we analyzed tail regeneration following reciprocal exchange of cuffs of tail integument between dark-colored (wild-type) and yellow-colored (hybrid) larval animals. Second, we studied tail regeneration in larval axolotls following transplantation of cuffs of tail integument from metamorphosed dark-colored conspecifics and from an adult fire salamander. In all cases, the amputation planes involved the transplanted integumental cuffs. In the first experiment, the regenerated tails showed the color of the host animals, not that of the transplanted cuffs. This suggests that the melanocytes of the regenerated tails were derived from the host hypodermis. Following transplantation of metamorphosed skin from axolotls and a fire salamander onto larval axolotls, the metamorphosed epidermis reverted to a larval condition. This indicates that the state of differentiation of the metamorphosed epidermis was not permanent. Rather, in order to maintain the metamorphosed epidermal structure, a continuous exposure of the animals to sufficient levels of thyroid hormones was required. Transplantation of tail buds from yellow-colored onto dark-colored axolotl embryos caused the formation of yellow-colored tails both in the head and the anterior limb region of the hosts. Incomplete resection of these heterotopic tails was followed by tail regeneration, while no tail regeneration occurred following complete resection of the heterotopic tails. Successful tail regeneration depended on the presence of neural tissue along the resection plane.

International policy and humanitarian guidance emphasize the need for precise, subnational malaria risk assessments with cross-regional comparability. Spatially explicit indicator-based assessments can support humanitarian aid organizations in identifying and localizing vulnerable populations for scaling resources and prioritizing aid delivery. However, the reliability of these assessments is often uncertain due to data quality issues. This article introduces a data evaluation framework to assist risk modelers in evaluating data adequacy. We operationalize the concept of “data adequacy” by considering “quality by design” (suitability) and “quality of conformance” (reliability). Based on a use case we developed in collaboration with Médecins Sans Frontières, we assessed data sources popular in spatial malaria risk assessments and related domains, including data from the Malaria Atlas Project, a healthcare facility database, WorldPop population counts, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation estimates, European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation forecast, and Armed Conflict Location and Event Data Project (ACLED) conflict events data. Our findings indicate that data availability is generally not a bottleneck, and data producers effectively communicate contextual information pertaining to sources, methodology, limitations and uncertainties. However, determining such data’s adequacy definitively for supporting humanitarian intervention planning remains challenging due to potential inaccuracies, incompleteness or outdatedness that are difficult to quantify. Nevertheless, the data hold value for awareness raising, advocacy and recognizing trends and patterns valuable for humanitarian contexts. We contribute a domain-agnostic, systematic approach to geodata adequacy evaluation, with the aim of enhancing geospatial risk assessments, facilitating evidence-based decisions.

Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.

Tissue inflammation drives the infiltration of innate immune cells that generate reactive species to kill bacteria and recruit adaptive immune cells. Neutrophil activation fosters the release of myeloperoxidase (MPO) enzyme, a heme-containing protein generating hypochlorous acid (HOCl) from hydrogen peroxide (H2O2) and chloride ions. MPO-dependent oxidant formation initiates bioactive oxidation and chlorination products and induces oxidative post-translational modifications (oxPTMs) on proteins and lipid oxidation. Besides HOCl and H2O2, further reactive species such as singlet oxygen and nitric oxide are generated in inflammation, leading to modified proteins, potentially resulting in their altered bioactivity. So far, knowledge about multiple free radical-induced modifications of MPO and its effects on HOCl generation is lacking. To mimic this multi-oxidant microenvironment, human MPO was exposed to several reactive species produced simultaneously via argon plasma operated at body temperature. Several molecular gas admixes were used to modify the reactive species type profiles generated. MPO was investigated by studying its oxPTMs, changes in protein structure, and enzymatic activity. MPO activity was significantly reduced after treatment with all five tested plasma gas conditions. Dynamic light scattering and CD-spectroscopy revealed altered MPO protein morphology indicative of oligomerization. Using mass spectrometry, various oxPTMs, such as +1O, +2O, and +3O, were determined on methionine and cysteine (Cys), and -1H-1N+1O was detected in asparagine (Asp). The modification types identified differed between argon-oxygen and argon-nitrogen plasmas. However, all plasma gas conditions led to the deamidation of Asp and oxidation of Cys residues, suggesting an inactivation of MPO due to oxPTM-mediated conformational changes.

Autophagy and the ubiquitin proteasome system are the two major processes for the clearance and recycling of proteins and organelles in eukaryotic cells. Evidence is accumulating that there is extensive crosstalk between the two pathways, but the underlying mechanisms are still unclear. We previously found that autophagy 9 (ATG9) and 16 (ATG16) proteins are crucial for full proteasomal activity in the unicellular amoeba Dictyostelium discoideum. In comparison to AX2 wild-type cells, ATG9−and ATG16− cells displayed a 60%, and ATG9−/16− cells a 90%, decrease in proteasomal activity. Mutant cells also showed a significant increase in poly-ubiquitinated proteins and contained large ubiquitin-positive protein aggregates. Here, we focus on possible reasons for these results. Reanalysis of published tandem mass tag-based quantitative proteomic results of AX2, ATG9−, ATG16−, and ATG9−/16− cells revealed no change in the abundance of proteasomal subunits. To identify possible differences in proteasome-associated proteins, we generated AX2 wild-type and ATG16− cells expressing the 20S proteasomal subunit PSMA4 as GFP-tagged fusion protein, and performed co-immunoprecipitation experiments followed by mass spectrometric analysis. The results revealed no significant differences in the abundance of proteasomes between the two strains. However, we found enrichment as well as depletion of proteasomal regulators and differences in the ubiquitination of associated proteins for ATG16−, as compared to AX2 cells. Recently, proteaphagy has been described as a means to replace non-functional proteasomes. We propose that autophagy-deficient D. discoideum mutants suffer from inefficient proteaphagy, which results in the accumulation of modified, less-active, and also of inactive, proteasomes. As a consequence, these cells exhibit a dramatic decrease in proteasomal activity and deranged protein homeostasis.

Multi-agent-based modelling and simulation provides an adequate environment to study the real world. This paper presents the use of a multi-agent research and simulation (MARS) framework and model design based on the overview, design concepts, design (ODD) protocol to model and simulate small-scale management strategies that are important for increased milk yield per cow. In reality, strategies for farm management at a small-scale level are purely based on heuristics that cost farmers and lead to inadequate milk yields. A differential assessment of the farming strategies was conducted to yield a data-driven approach for selection of the best strategies, which in turn will optimize investments and increase milk yield. The agent-based modelling and simulation revealed that, the studied strategies based on income, farm, and farmer-based characteristics influenced an increase of up to 7.72 L of milk above the average (12.7 ± 4.89). Generally, there was an increase in milk yield based on the identified evolvement strategies; from a baseline data average milk yield of 12.7 ± 4.89 to simulated milk yield average of 17.57 ± 0.72. Evaluating the agent-based models in real-world scenarios will strengthen the assurance that the identified strategies can move small-scale dairy farmers from low to higher milk producers.

Gas plasma is a medical technology with antimicrobial properties. Its main mode of action is oxidative damage via reactive species production. The clinical efficacy of gas plasma-reduced bacterial burden has been shown to be hampered in some cases. Since the reactive species profile produced by gas plasma jets, such as the kINPen used in this study, are thought to determine antimicrobial efficacy, we screened an array of feed gas settings in different types of bacteria. Antimicrobial analysis was performed by single-cell analysis using flow cytometry. We identified humidified feed gas to mediate significantly greater toxicity compared to dry argon and many other gas plasma conditions. The results were confirmed by inhibition zone analysis on gas-plasma-treated microbial lawns grown on agar plates. Our results may have vital implications for clinical wound management and potentially enhance antimicrobial efficacy of medical gas plasma therapy in patient treatment.

In cancer, antigen-presenting cells (APC), including dendritic cells (DCs), take up and process proteins to mount adaptive antitumor immune responses. This often happens in the context of inflamed cancer, where reactive oxygen species (ROS) are ubiquitous to modify proteins. However, the inflammatory consequences of oxidized protein uptake in DCs are understudied. To this end, we investigated human monocyte-derived cell surface marker expression and cytokine release profiles when exposed to oxidized and native proteins. Seventeen proteins were analyzed, including viral proteins (e.g., CMV and HBV), inflammation-related proteins (e.g., HO1 and HMGB1), matrix proteins (e.g., Vim and Coll), and vastly in the laboratory used proteins (e.g., BSA and Ova). The multifaceted nature of inflammation-associated ROS was mimicked using gas plasma technology, generating reactive species cocktails for protein oxidation. Fourteen oxidized proteins led to elevated surface marker expression levels of CD25, CD40, CD80, CD86, and MHC-II as well as strongly modified release of IL6, IL8, IL10, IL12, IL23, MCP-1, and TNFα compared to their native counterparts. Especially IL8, heme oxygenase 2, and vimentin oxidation gave pronounced effects. Furthermore, protein kinase phospho-array studies in monocyte-derived cells pulsed with native vs. oxidized IL8 and insulin showed enhanced AKT and RSK2 phosphorylation. In summary, our data provide for the first time an overview of the functional consequences of oxidized protein uptake by human monocyte-derived cells and could therefore be a starting point for exploiting such principle in anticancer therapy in the future.

Protein vaccines rely on eliciting immune responses. Inflammation is a prerequisite for immune responses to control infection and cancer but is also associated with disease onset. Reactive oxygen species (ROSs) are central during inflammation and are capable of inducing non-enzymatic oxidative protein modifications (oxMods) associated with chronic disease, which alter the functionality or immunogenicity of proteins that are relevant in cancer immunotherapy. Specifically, antigen-presenting cells (APCs) take up and degrade extracellular native and oxidized proteins to induce adaptive immune responses. However, it is less clear how oxMods alter the protein’s immunogenicity, especially in inflammation-related short-lived reactive species. Gas plasma technology simultaneously generates a multitude of ROSs to modify protein antigens in a targeted and controlled manner to study the immunogenicity of oxMods. As model proteins relevant to chronic inflammation and cancer, we used gas plasma-treated insulin and CXCL8. We added those native or oxidized proteins to human THP-1 monocytes or primary monocyte-derived cells (moDCs). Both oxidized proteins caused concentration-independent maturation phenotype alterations in moDCs and THP-1 cells concerning surface marker expression and chemokine and cytokine secretion profiles. Interestingly, concentration-matched H2O2-treated proteins did not recapitulate the effects of gas plasma, suggesting sufficiently short diffusion distances for the short-lived reactive species to modify proteins. Our data provide evidence of dendric cell maturation and activation upon exposure to gas plasma- but not H2O2-modified model proteins. The biological consequences of these findings need to be elucidated in future inflammation and cancer disease models.

Gas plasma is an approved technology that generates a plethora of reactive oxygen species, which are actively applied for chronic wound healing. Its particular antimicrobial action has spurred interest in other medical fields, such as periodontitis in dentistry. Recent work has indicated the possibility of performing gas plasma-mediated biofilm removal on teeth. Teeth frequently contain restoration materials for filling cavities, e.g., resin-based composites. However, it is unknown if such materials are altered upon gas plasma exposure. To this end, we generated a new in-house workflow for three commonly used resin-based composites following gas plasma treatment and incubated the material with human HaCaT keratinocytes in vitro. Cytotoxicity was investigated by metabolic activity analysis, flow cytometry, and quantitative high-content fluorescence imaging. The inflammatory consequences were assessed using quantitative analysis of 13 different chemokines and cytokines in the culture supernatants. Hydrogen peroxide served as the control condition. A modest but significant cytotoxic effect was observed in the metabolic activity and viability after plasma treatment for all three composites. This was only partially treatment time-dependent and the composites alone affected the cells to some extent, as evident by differential secretion profiles of VEGF, for example. Gas plasma composite modification markedly elevated the secretion of IL6, IL8, IL18, and CCL2, with the latter showing the highest correlation with treatment time (Pearson’s r > 0.95). Cell culture media incubated with gas plasma-treated composite chips and added to cells thereafter could not replicate the effects, pointing to the potential that surface modifications elicited the findings. In conclusion, our data suggest that gas plasma treatment modifies composite material surfaces to a certain extent, leading to measurable but overall modest biological effects.

Understanding the dynamics of tree species and their demography is necessary for predicting future developments in savanna ecosystems. In this contribution, elephant-tree and firewood collector-tree interactions are compared using a multiagent model. To investigate these dynamics, we compared three different tree species in two plots. The first plot is located in the protected space of Kruger National Park (KNP), South Africa, and the second plot in the rural areas of the Bushbuckridge Municipality, South Africa. The agent-based modeling approach enabled the modeling of individual trees with characteristics such as species, age class, size, damage class, and life history. A similar level of detail was applied to agents that represent elephants and firewood collectors. Particular attention was paid to modeling purposeful behavior of humans in contrast to more instinct-driven actions of elephants. The authors were able to predict future developments by simulating the time period between 2010 and 2050 with more than 500,000 individual trees. Modeling individual trees for a time span of 40 years might yield more detailed information than a simple woody mass aggregation. The results indicate a significant trend toward more and thinner trees together with a notable reduction in mature trees, while the total aboveground biomass appears to stay more or less constant. Furthermore, the KNP scenarios show an increase in young Combretum apiculatum, which may correspond to bush encroachment.

Agent-based models provide detailed, bottom-up approaches to investigate complex socio-ecological systems. This study presents a first step towards a modular agent-based simulation that is based upon empirical data, as well as environmental suitability maps and an assessment of livestock units. To illustrate the capabilities of our simulation, we use a geographically explicit approach to simulate a component of the production of animal products of a rural settlement in the lower Bakırçay catchment, western Turkey. The model structurally couples various agent types representing several elements and processes of the animal husbandry and food production value chain, such as sedentary herders—practising daily, short-distance pastoralism—and their flocks of goats and sheep, as well as milking and slaughtering. The modelling tool captures the fundamental socio-ecological dynamics of animal husbandry and food production in rural settlements. Therefore, the tool is valuable as a basis to discuss hypotheses regarding the number of animals that are needed to cover the requirements of different growing populations.

Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

A novel approach for a robust computation of positional relations of two-dimensional geometric features is presented which guarantees reliable results, provided that the initial data is valid. The method is based on the use of integer coordinates and a method to generate a complete, gap-less and non-overlapping spatial decomposition. The spatial relationships of two geometric features are then represented using DE-9IM matrices. These allow the spatial relationships to be represented compactly. The DE-9IM matrices are based on the spatial decomposition using explicit neighborhood relations. No further geometric calculations are required for their computation. Based on comparative tests, it could be proven that this approach, up to a predictable limit, provides correct results and thus offers advantages over classical methods for the calculation of spatial relationships. This novel method can be used in all fields, especially where guaranteed reliable results are required.

Skeletal muscle is a heterogeneous tissue consisting of blood vessels, connective tissue, and muscle fibers. The last are highly adaptive and can change their molecular composition depending on external and internal factors, such as exercise, age, and disease. Thus, examination of the skeletal muscles at the fiber type level is essential to detect potential alterations. Therefore, we established a protocol in which myosin heavy chain isoform immunolabeled muscle fibers were laser microdissected and separately investigated by mass spectrometry to develop advanced proteomic profiles of all murine skeletal muscle fiber types. All data are available via ProteomeXchange with the identifier PXD025359. Our in-depth mass spectrometric analysis revealed unique fiber type protein profiles, confirming fiber type-specific metabolic properties and revealing a more versatile function of type IIx fibers. Furthermore, we found that multiple myopathy-associated proteins were enriched in type I and IIa fibers. To further optimize the assignment of fiber types based on the protein profile, we developed a hypothesis-free machine-learning approach, identified a discriminative peptide panel, and confirmed our panel using a public data set.

Cancer is the second leading cause of death worldwide. Today, the critical role of the immune system in tumor control is undisputed. Checkpoint antibody immunotherapy augments existing antitumor T cell activity with durable clinical responses in many tumor entities. Despite the presence of tumor-associated antigens and neoantigens, many patients have an insufficient repertoires of antitumor T cells. Autologous tumor vaccinations aim at alleviating this defect, but clinical success is modest. Loading tumor material into autologous dendritic cells followed by their laboratory expansion and therapeutic vaccination is promising, both conceptually and clinically. However, this process is laborious, time-consuming, costly, and hence less likely to solve the global cancer crisis. Therefore, it is proposed to re-focus on personalized anticancer vaccinations to enhance the immunogenicity of autologous therapeutic tumor vaccines. Recent work re-established the idea of using the alarming agents of the immune system, oxidative modifications, as an intrinsic adjuvant to broaden the antitumor T cell receptor repertoire in cancer patients. The key novelty is the use of gas plasma, a multi-reactive oxygen and nitrogen species-generating technology, for diversifying oxidative protein modifications in a, so far, unparalleled manner. This significant innovation has been successfully used in proof-of-concept studies and awaits broader recognition and implementation to explore its chances and limitations of providing affordable personalized anticancer vaccines in the future. Such multidisciplinary advance is timely, as the current COVID-19 crisis is inexorably reflecting the utmost importance of innovative and effective vaccinations in modern times.

Proteins succumb to numerous post-translational modifications (PTMs). These relate to enzymatic or non-enzymatic reactions taking place in either the intracellular or extracellular compartment. While intracellular oxidative changes are mainly due to redox stress, extracellular PTMs may be induced in an inflammatory micro milieu that is rich in reactive species. The increasing recognition of oxidative modifications as a causing agent or side-effect of pathophysiological states and diseases puts oxidative PTMS (oxPTMs) into the spotlight of inflammation research. Pathological hyper-modification of proteins can lead to accumulation, aggregation, cell stress, altered antigenic peptides, and damage-associated molecular pattern (DAMP)-like recognition by host immunity. Such processes are linked to cardiovascular disease and autoinflammation. At the same time, a detailed understanding of the mechanisms governing inflammatory responses to oxPTMs may capitalize on new therapeutic routes for enhancing adaptive immune responses as needed, for instance, in oncology. We here summarize some of the latest developments of oxPTMs in disease diagnosis and therapy. Potential target proteins and upcoming technologies, such as gas plasmas, are outlined for future research that may aid in identifying the molecular basis of immunogenic vs. tolerogenic oxPTMs.

Macroautophagy, a highly conserved and complex intracellular degradative pathway, involves more than 20 core autophagy (ATG) proteins, among them the hexameric ATG12~5/16 complex, which is part of the essential ubiquitin-like conjugation systems in autophagy. Dictyostelium discoideum atg5 single, atg5/12 double, and atg5/12/16 triple gene knock-out mutant strains displayed similar defects in the conjugation of ATG8 to phosphatidylethanolamine, development, and cell viability upon nitrogen starvation. This implies that ATG5, 12 and 16 act as a functional unit in canonical autophagy. Macropinocytosis of TRITC dextran and phagocytosis of yeast were significantly decreased in ATG5¯ and ATG5¯/12¯ and even further in ATG5¯/12¯/16¯ cells. In contrast, plaque growth on Klebsiella aerogenes was about twice as fast for ATG5¯ and ATG5¯/12¯/16¯ cells in comparison to AX2, but strongly decreased for ATG5¯/12¯ cells. Along this line, phagocytic uptake of Escherichia coli was significantly reduced in ATG5¯/12¯ cells, while no difference in uptake, but a strong increase in membrane association of E. coli, was seen for ATG5¯ and ATG5¯/12¯/16¯ cells. Proteasomal activity was also disturbed in a complex fashion, consistent with an inhibitory activity of ATG16 in the absence of ATG5 and/or ATG12. Our results confirm the essential function of the ATG12~5/16 complex in canonical autophagy, and furthermore are consistent with autophagy-independent functions of the complex and its individual components. They also strongly support the placement of autophagy upstream of the ubiquitin-proteasome system (UPS), as a fully functional UPS depends on autophagy.

Due to the fact that the South Africa’s savanna landscapes are under changing conditions, the previously sustainable firewood collection system in rural areas has become a social-ecological factor in questions about landscape management. While the resilience of savannas in national parks such as Kruger National Park (KNP) in South Africa has been widely acknowledged in ecosystem management, the resilience of woody vegetation outside protected areas has been underappreciated. Collecting wood is the dominant source of energy for rural households, and there is an urgent need for land management to find sustainable solutions for this complex social-ecological system. However, the firewood collection scenario is only one example, and stands for all “human-ecosystem service” interactions under the topic of over-utilization, e.g., fishery, grazing, harvesting. Agent-based modeling combined with goal-oriented action planning (GOAP) can provide fresh insights into the relationship between individual needs of humans and changes in land use. At the same time, this modeling approach includes adaptive behavior under changing conditions. A firewood collection scenario was selected for a proof-of-concept comprising households, collectors, ecosystem services and firewood sites. Our results have shown that, even when it is predictable what a single human agent will do, massive up-scaling is needed in order to understand the whole complexity of social-ecological systems. Under changing conditions, such as climate and an increasing population, fair distribution of natural goods become an important issue.

The introduction of livestock species in Europe has been followed by various genetic events, which created a complex spatial pattern of genetic differentiation. Spatial principal component (sPCA) analysis and spatial metric multidimensional scaling (sMDS) incorporate geography in multivariate analysis. This method was applied to three microsatellite data sets for 45 goat breeds, 46 sheep breeds, and 101 cattle breeds from Europe, Southwest Asia, and India. The first two sPCA coordinates for goat and cattle, and the first sPCA coordinate of sheep, correspond to the coordinates of ordinary PCA analysis. However, higher sPCA coordinates suggest, for all three species, additional spatial structuring. The goat is the most geographically structured species, followed by cattle. For all three species, the main genetic cline is from southeast to northwest, but other geographic patterns depend on the species. We propose sPCA and sMDS to be useful tools for describing the correlation of genetic variation with geography.