Example Plots Examples of ArviZ's plotting functionality are shown in Figure 2 through Figure 5. Additional examples can be found in the ArviZ documentation.

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC’s versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming.

A better understanding of protein aggregation is bound to translate into critical advances in several areas, including the treatment of misfolded protein disorders and the development of self-assembling biomaterials for novel commercial applications. Because of its ubiquity and clinical potential, albumin is one of the best-characterized models in protein aggregation research; but its properties in different conditions are not completely understood. Here, we carried out all-atom molecular dynamics simulations of albumin to understand how electrostatics can affect the conformation of a single albumin molecule just prior to self-assembly. We then analyzed the tertiary structure and solvent accessible surface area of albumin after electrostatically triggered partial denaturation. The data obtained from these single protein simulations allowed us to investigate the effect of electrostatic interactions between two proteins. The results of these simulations suggested that hydrophobic attractions and counterion binding may be strong enough to effectively overcome the electrostatic repulsions between the highly charged monomers. This work contributes to our general understanding of protein aggregation mechanisms, the importance of explicit consideration of free ions in protein solutions, provides critical new insights about the equilibrium conformation of albumin in its partially denatured state at low pH, and may spur significant progress in our efforts to develop biocompatible protein hydrogels driven by electrostatic partial denaturation.

Background: Protected areas, regarded today as a cornerstone of nature conservation, result from an array of multiple motivations and opportunities. We explored at global and regional levels the current distribution of protected areas along biophysical, human, and biological gradients, and assessed to what extent protection has pursued (i) a balanced representation of biophysical environments, (ii) a set of preferred conditions (biological, spiritual, economic, or geopolitical), or (iii) existing opportunities for conservation regardless of any representation or preference criteria.Methods: We used histograms to describe the distribution of terrestrial protected areas along biophysical, human, and biological independent gradients and linear and non-linear regression and correlation analyses to describe the sign, shape, and strength of the relationships. We used a random forest analysis to rank the importance of different variables related to conservation preferences and opportunity drivers, and an evenness metric to quantify representativeness.Results: We find that protection at a global level is primarily driven by the opportunities provided by isolation and a low population density (variable importance = 34.6 and 19.9, respectively). Preferences play a secondary role, with a bias towards tourism attractiveness and proximity to international borders (variable importance = 12.7 and 3.4, respectively). Opportunities shape protection strongly in “North America & Australia–NZ” and “Latin America & Caribbean,” while the importance of the representativeness of biophysical environments is higher in “Sub-Saharan Africa” (1.3 times the average of other regions).Discussion: Environmental representativeness and biodiversity protection are top priorities in land conservation agendas. However, our results suggest that they have been minor players driving current protection at both global and regional levels. Attempts to increase their relevance will necessarily have to recognize the predominant opportunistic nature that the establishment of protected areas has had until present times.

A server (CheShift) has been developed to predict C-13(alpha) chemical shifts of protein structures. It is based on the generation of 696,916 conformations as a function of the phi, psi, omega, chi 1 and chi 2 torsional angles for all 20 naturally occurring amino acids. Their C-13(alpha) chemical shifts were computed at the DFT level of theory with a small basis set and extrapolated, with an empirically-determined linear regression formula, to reproduce the values obtained with a larger basis set. Analysis of the accuracy and sensitivity of the CheShift predictions, in terms of both the correlation coefficient R and the conformational-averaged rmsd between the observed and predicted C-13(alpha) chemical shifts, was carried out for 3 sets of conformations: (i) 36 x-ray-derived protein structures solved at 2.3 angstrom or better resolution, for which sets of C-13(alpha) chemical shifts were available; (ii) 15 pairs of x-ray and NMR-derived sets of protein conformations; and (iii) a set of decoys for 3 proteins showing an rmsd with respect to the x-ray structure from which they were derived of up to 3 angstrom. Comparative analysis carried out with 4 popular servers, namely SHIFTS, SHIFTX, SPARTA, and PROSHIFT, for these 3 sets of conformations demonstrated that CheShift is the most sensitive server with which to detect subtle differences between protein models and, hence, to validate protein structures determined by either x-ray or NMR methods, if the observed C-13(alpha) chemical shifts are available. CheShift is available as a web server.

Background: South America faces strong environmental pressures as a result of agriculture and infrastructure expansion and also of demographic growth, demanding immediate action to preserve natural assets by establishing protected areas. Currently, 7.1% of the (sub)continent is under strict conservation categories (I to IV, IUCN), but the spatial distribution of these 1.3 × 10⁶ km² is poorly understood. We evaluated the representation of nature within the networks of protected areas, map conservation priorities and assess demographic, economic or geopolitical causes of existing protection patterns. Methods: We characterized nature representation by looking at two components: the extent and the equality of protection. The first refers to the fraction of territory under protection, while the second refers to the homogeneity in the distribution along natural conditions of this protected fraction. We characterized natural conditions by either 113 biogeographical units (specifically, ecoregions) or a series of limited and significant climatic, topographic and edaphic traits. We analyzed representation every ten years since 1960 at national and continental levels. In the physical approach, histograms allowed us to map the degree of conservation priorities. Finally, we ranked the importance of different economic or geopolitical variables driving the observed distributions with a random forest technique. Results: Nature representation varied across countries in spite of its priority in conservation agendas. In Brazil, Peru and Argentina there are still natural conditions with no formal protection, while in Bolivia and Venezuela, protected areas incorporate the natural diversity in a more balanced manner. As protected networks have increased their extent, so did their equality across and within countries over time. Our maps revealed as top continental priorities the southern temperate, subhumid and fertile lowland environments, and other country-specific areas. Protection extent was generally driven by a low population density and isolation, while other variables like distance to frontiers, were relevant only locally (e.g., in Argentina). Discussion: Our description of the spatial distribution of protected areas can help societies and governments to improve the allocation of conservation efforts. We identified the main limitations that future conservation efforts will face, as protection was generally driven by the opportunities provided by low population density and isolation. From a methodological perspective, the physical approach reveals new properties of protection and provides tools to explore nature representation at different spatial, temporal and conceptual levels, complementing the traditional ones based on biodiversity or biogeographical attributes.