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Abstract Heterogeneity in the size distribution of cancer cell populations is linked to drug resistance and invasiveness. However, understanding how such heterogeneity arises is still damped by the difficulties of monitoring the proliferation at the typical timescales of mammalian cells. Here, we show how to infer the growth regime and division strategy of leukemia cell populations using live cell fluorescence labeling and flow cytometry in combination with an analytical model where cell growth and division rates depend on powers of the size. We found that the dynamics of the size distribution of Jurkat T-cells is reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution and (iii) fluctuations up to ten percent of the inherited fraction of size at division. Overall, our apparatus can be extended to other cell types and environmental conditions allowing for a comprehensive characterization of the growth and division model different cells adopt.

Zinc (Zn) shortage is a common micronutrient deficiency affecting plants worldwide, while Zn toxicity may occur when this metal is in excess. Ectomycorrhizal (ECM) fungi are known to be able to modulate the transfer of macro- and microelements, among them Zn, to the plant. However, the underlying mechanisms are not well understood. We identified the HcZnT2 gene from the ECM fungus Hebeloma cylindrosporum, encoding a member of the Cation Diffusion Facilitator (CDF) family including Zn transporters, and analyzed its transcriptional regulation, the transport function by yeast complementation experiments, and its subcellular localization using a GFP fusion protein in yeast. HcZnT2 is highly induced during mycorrhization of Pinus pinaster, and upregulated in presence of the host plant root even without any direct contact. However, HcZnT2 is repressed by Zn excess conditions. By functional expression in yeast, our results strongly support the ability of HcZnT2 to transport Zn and, to a lesser extent, manganese. HcZnT2 localization was associated with the endoplasmic reticulum of yeast. Mycorrhizal gene activation at low external Zn suggests that the Zn transporter HcZnT2 might be important for the early establishment of the ECM symbiosis during Zn deficiency, rather than under Zn excess. HcZnT2 arises as an extremely remarkable candidate playing a key role in Zn homeostasis and regulation in ectomycorrhiza.

Abstract Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating patterns. In this study, we present a novel storage method that harnesses naturally-occurring random structures to store an arbitrary pattern in a memory device. This method, the Stochastic Emergent Storage (SES), builds upon the concept of emergent archetypes, where a training set of imperfect examples (prototypes) is employed to instantiate an archetype in a Hopfield-like network through emergent processes. We demonstrate this non-Hebbian paradigm in the photonic domain by utilizing random transmission matrices, which govern light scattering in a white-paint turbid medium, as prototypes. Through the implementation of programmable hardware, we successfully realize and experimentally validate the capability to store an arbitrary archetype and perform classification at the speed of light. Leveraging the vast number of modes excited by mesoscopic diffusion, our approach enables the simultaneous storage of thousands of memories without requiring any additional fabrication efforts. Similar to a content addressable memory, all stored memories can be collectively assessed against a given pattern to identify the matching element. Furthermore, by organizing memories spatially into distinct classes, they become features within a higher-level categorical (deeper) optical classification layer.

Abstract Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes’ stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of $$\sim$$ ∼ 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Recording large amounts of users’ sessions performed through 3D applications may provide crucial insights into interaction patterns. Such data can be captured from interactive experiences in public exhibits, remote motion tracking equipment, immersive XR devices, lab installations or online web applications. Immersive analytics (IA) deals with the benefits and challenges of using immersive environments for data analysis and related design solutions to improve the quality and efficiency of the analysis process. Today, web technologies allow us to craft complex applications accessible through common browsers, and APIs like WebXR allow us to interact with and explore virtual 3D environments using immersive devices. These technologies can be used to access rich, immersive spaces but present new challenges related to performance, network bottlenecks and interface design. WebXR IA tools are still quite new in the literature: they present several challenges and leave quite unexplored the possibility of synchronous collaborative inspection. The opportunity to share the virtual space with remote analysts in fact improves sense-making tasks and offers new ways to discuss interaction patterns together, while inspecting captured records or data aggregates. Furthermore, with proper collaborative approaches, analysts are able to share machine learning (ML) pipelines and constructively discuss the outcomes and insights through tailored data visualization, directly inside immersive 3D spaces, using a web browser. Under the H2IOSC project, we present the first results of an open-source pipeline involving tools and services aimed at capturing, processing and inspecting interactive sessions collaboratively in WebXR with other analysts. The modular pipeline can be easily deployed in research infrastructures (RIs), remote dedicated hubs or local scenarios. The developed WebXR immersive analytics tool specifically offers advanced features for volumetric data inspection, query, annotation and discovery, alongside spatial interfaces. We assess the pipeline through users’ sessions captured during two remote public exhibits, by a WebXR application presenting generative AI content to visitors. We deployed the pipeline to assess the different services and to better understand how people interact with generative AI environments. The obtained results can be easily adopted for a multitude of case studies, interactive applications, remote equipment or online applications, to support or accelerate the detection of interaction patterns among remote analysts collaborating in the same 3D space.

Neural rosettes develop from the self-organization of differentiating human pluripotent stem cells. This process mimics the emergence of the embryonic central nervous system primordium, i.e., the neural tube, whose formation is under close investigation as errors during such process result in severe diseases like spina bifida and anencephaly. While neural tube formation is recognized as an example of self-organization, we still do not understand the fundamental mechanisms guiding the process. Here, we discuss the different theoretical frameworks that have been proposed to explain self-organization in morphogenesis. We show that an explanation based exclusively on stem cell differentiation cannot describe the emergence of spatial organization, and an explanation based on patterning models cannot explain how different groups of cells can collectively migrate and produce the mechanical transformations required to generate the neural tube. We conclude that neural rosette development is a relevant experimental 2D in-vitro model of morphogenesis because it is a multi-scale self-organization process that involves both cell differentiation and tissue development. Ultimately, to understand rosette formation, we first need to fully understand the complex interplay between growth, migration, cytoarchitecture organization, and cell type evolution.

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems.

Disordered, self-assembled media, contain a large amount of information, which can be seen as a huge set of random and uncontrolled memory patterns. In the framework of optics, in which an opaque medium may modelled with the transmission matrix approach, each transmitted mode “contains” a memory element which is embodied by the correspondent transmission vector. Even if the stored amount of information in this system is huge, these random memories cannot be tailored easily. Here we present a new approach to write, read, and classify memory patterns: the photonic emergent learning. The writing paradigm is borrowed form a-physical- mathematical model for the biological memory, the emergent archetype, which we translated to photonics. In our approach the random patterns enclosed in the transmitted electromagnetic modes, are used as prototypes which are summed in constructive fashion in order write our target archetype-memory into our disordered optical memory (DOM). The DOM can work as a content addressable memory, retrieving at the lightning speed which memory in the library is the closest to an optically proposed query pattern. Moreover, the optical memories can be organized into super structures containing memories of the same thus efficiently delivering a classification task.

Abstract Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illumination patterns. It is thus advantageous in experimental conditions where toxicity or biological fluctuations are an issue. In this work, we introduce a custom convolutional neural network architecture for blind-SIM: BS-CNN. We show that BS-CNN outperforms other blind-SIM deconvolution algorithms providing a resolution improvement of 2.17 together with a very high Fidelity (artifacts reduction). Furthermore, BS-CNN proves to be robust in cross-database variability: it is trained on synthetically augmented open-source data and evaluated on experiments. This approach paves the way to the employment of CNN-based deconvolution in all scenarios in which a statistical model for the illumination is available while the specific realizations are unknown or noisy.

Zinc (Zn) shortage is a common micronutrient deficiency affecting plants worldwide, while Zn toxicity may occur when this metal is in excess. Ectomycorrhizal (ECM) fungi are known to be able to modulate the transfer of macro- and microelements, among them Zn, to the plant. However, the underlying mechanisms are not well understood. We identified the HcZnT2 gene from the ECM fungus Hebeloma cylindrosporum, encoding a member of the Cation Diffusion Facilitator (CDF) family including Zn transporters, and analyzed its transcriptional regulation, the transport function by yeast complementation experiments, and its subcellular localization using a GFP fusion protein in yeast. HcZnT2 is highly induced during mycorrhization of Pinus pinaster, and upregulated in presence of the host plant root even without any direct contact. However, HcZnT2 is repressed by Zn excess conditions. By functional expression in yeast, our results strongly support the ability of HcZnT2 to transport Zn and, to a lesser extent, manganese. HcZnT2 localization was associated with the endoplasmic reticulum of yeast. Mycorrhizal gene activation at low external Zn suggests that the Zn transporter HcZnT2 might be important for the early establishment of the ECM symbiosis during Zn deficiency, rather than under Zn excess. HcZnT2 arises as an extremely remarkable candidate playing a key role in Zn homeostasis and regulation in ectomycorrhiza. [ABSTRACT FROM AUTHOR]

Miotto et al. perform molecular dynamics simulations on a selected set of possible SARS-CoV-2 spike variants in order to assess the stabilizing effect of particular amino acid substitutions on the molecular complex. Their analysis can help to monitor further possible spike variants.

Summary: Microglia reactivity entails a large-scale remodeling of cellular geometry, but the behavior of the microtubule cytoskeleton during these changes remains unexplored. Here we show that activated microglia provide an example of microtubule reorganization from a non-centrosomal array of parallel and stable microtubules to a radial array of more dynamic microtubules. While in the homeostatic state, microglia nucleate microtubules at Golgi outposts, and activating signaling induces recruitment of nucleating material nearby the centrosome, a process inhibited by microtubule stabilization. Our results demonstrate that a hallmark of microglia reactivity is a striking remodeling of the microtubule cytoskeleton and suggest that while pericentrosomal microtubule nucleation may serve as a distinct marker of microglia activation, inhibition of microtubule dynamics may provide a different strategy to reduce microglia reactivity in inflammatory disease.

Emerging experimental observation suggests that asymmetrical partitioning in cell division plays an important role in cell-to-cell variability, cell fate determination, cellular aging, and rejuvenation. Here, the authors propose a method based on multicolor flow cytometry to measure asymmetric division of cellular organelles, finding that cell cytoplasm is divided symmetrically but mitochondria and membrane lipids are asymmetrically distributed, and explain these observations through a minimal model of asymmetric partitioning based on biased binomial statistics.

Heterogeneity in the size distribution of cancer cell populations is linked to drug resistance and invasiveness. However, understanding how such heterogeneity arises is still damped by the difficulties of monitoring the proliferation at the typical timescales of mammalian cells. Here, we show how to infer the growth regime and division strategy of leukemia cell populations using live cell fluorescence labeling and flow cytometry in combination with an analytical model where cell growth and division rates depend on powers of the size. We found that the dynamics of the size distribution of Jurkat T-cells is reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution and (iii) fluctuations up to ten percent of the inherited fraction of size at division. Overall, our apparatus can be extended to other cell types and environmental conditions allowing for a comprehensive characterization of the growth and division model different cells adopt.Understanding how cancer cells regulate their size is still largely an open question. The authors propose a method to infer the division strategy of leukemia cells via live cell fluorescence labeling and flow cytometry measurements combined with a mathematical model based on size-dependent growth and division rates. [ABSTRACT FROM AUTHOR]

In the orthopaedic surgery field, the use of medical implants to treat a patient's bone fracture is nowadays a common practice, nevertheless, it is associated with possible cases of infection. The consequent hardware infection can lead to implant failure and systemic infections, with prolonged hospitalization, time-consuming rehabilitation treatments, and extended antibiotic therapy. Hardware infections are strictly related to bacterial adhesion to the implant, leading to infection occurrence and consequent pH decreasing from physiological level to acid pH. Here, we demonstrate the new strategy to use an orthopaedic implant functionalized with iridium oxide film as the working electrode for the potentiometric monitoring of pH in hardware infection diagnosis. A functional investigation was focused on selecting the implant material, namely titanium, titanium alloy, and stainless steel, and the component, namely screws and implants. After selecting the titanium-based implant as the working electrode and a silver wire as the reference electrode in the final configuration of the smart sensing orthopaedic implant, a calibration curve was performed in standard solutions. An equation equal to y = (0.76 ± 0.02) - (0.068 ± 0.002) x, R2 = 0.996, was obtained in the pH range of 4-8. Subsequently, hysteresis, interference, matrix effect, recovery study, and storage stability were investigated to test the overall performance of the sensing device, demonstrating the tremendous potential of electrochemical sensors to deliver the next generation of smart orthopaedic implants. [ABSTRACT FROM AUTHOR]

We present a method for efficiently and effectively assessing whether and where two proteins can interact with each other to form a complex. This is still largely an open problem, even for those relatively few cases where the 3D structure of both proteins is known. In fact, even if much of the information about the interaction is encoded in the chemical and geometric features of the structures, the set of possible contact patches and of their relative orientations are too large to be computationally affordable in a reasonable time, thus preventing the compilation of reliable interactome. Our method is able to rapidly and quantitatively measure the geometrical shape complementarity between interacting proteins, comparing their molecular iso-electron density surfaces expanding the surface patches in term of 2D Zernike polynomials. We first test the method against the real binding region of a large dataset of known protein complexes, reaching a success rate of 0.72. We then apply the method for the blind recognition of binding sites, identifying the real region of interaction in about 60% of the analyzed cases. Finally, we investigate how the efficiency in finding the right binding region depends on the surface roughness as a function of the expansion order.

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

We propose a computational investigation on the interaction mechanisms between SARS-CoV-2 spike protein and possible human cell receptors. In particular, we make use of our newly developed numerical method able to determine efficiently and effectively the relationship of complementarity between portions of protein surfaces. This innovative and general procedure, based on the representation of the molecular isoelectronic density surface in terms of 2D Zernike polynomials, allows the rapid and quantitative assessment of the geometrical shape complementarity between interacting proteins, which was unfeasible with previous methods. Our results indicate that SARS-CoV-2 uses a dual strategy: in addition to the known interaction with angiotensin-converting enzyme 2, the viral spike protein can also interact with sialic-acid receptors of the cells in the upper airways.

Recording large amounts of users' sessions performed through 3D applications may provide crucial insights into interaction patterns. Such data can be captured from interactive experiences in public exhibits, remote motion tracking equipment, immersive XR devices, lab installations or online web applications. Immersive analytics (IA) deals with the benefits and challenges of using immersive environments for data analysis and related design solutions to improve the quality and efficiency of the analysis process. Today, web technologies allow us to craft complex applications accessible through common browsers, and APIs like WebXR allow us to interact with and explore virtual 3D environments using immersive devices. These technologies can be used to access rich, immersive spaces but present new challenges related to performance, network bottlenecks and interface design. WebXR IA tools are still quite new in the literature: they present several challenges and leave quite unexplored the possibility of synchronous collaborative inspection. The opportunity to share the virtual space with remote analysts in fact improves sense-making tasks and offers new ways to discuss interaction patterns together, while inspecting captured records or data aggregates. Furthermore, with proper collaborative approaches, analysts are able to share machine learning (ML) pipelines and constructively discuss the outcomes and insights through tailored data visualization, directly inside immersive 3D spaces, using a web browser. Under the H2IOSC project, we present the first results of an open-source pipeline involving tools and services aimed at capturing, processing and inspecting interactive sessions collaboratively in WebXR with other analysts. The modular pipeline can be easily deployed in research infrastructures (RIs), remote dedicated hubs or local scenarios. The developed WebXR immersive analytics tool specifically offers advanced features for volumetric data inspection, query, annotation and discovery, alongside spatial interfaces. We assess the pipeline through users' sessions captured during two remote public exhibits, by a WebXR application presenting generative AI content to visitors. We deployed the pipeline to assess the different services and to better understand how people interact with generative AI environments. The obtained results can be easily adopted for a multitude of case studies, interactive applications, remote equipment or online applications, to support or accelerate the detection of interaction patterns among remote analysts collaborating in the same 3D space. [ABSTRACT FROM AUTHOR]

Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: i) the great geographical differences in the degree of virus contagiousness and lethality that were found in the different phases of the epidemic progression, and, ii) the potential role of the infected people's blood type in both the virus infectivity and the progression of the disease. A recent hypothesis could shed some light on both aspects. Specifically, it has been proposed that, in the subject-to-subject transfer, SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject. Thus these conserved antigens can potentially cause an immune reaction in a receiving subject that has previously acquired specific antibodies for the source subject antigens. This hypothesis implies a blood type-dependent infection rate. The strong geographical dependence of the blood type distribution could be, therefore, one of the factors at the origin of the observed heterogeneity in the epidemics spread. Here, we present an epidemiological deterministic model where the infection rules based on blood types are taken into account, and we compare our model outcomes with the exiting worldwide infection progression data. We found an overall good agreement, which strengthens the hypothesis that blood types do play a role in the COVID-19 infection.

Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.

We address the problem of encoding the state variables of a finite state machine such that the BDD representing the next state function and the output function has the minimum number of nodes. We present an exact algorithm to solve this problem when only the present state variables are encoded. We provide results on MCNC benchmark circuits.