Academic literature on the topic 'Endogenous Dimensionality'

The hypothesis that reactions associated with intracellular membranes enjoy a kinetic advantage from a reduced dimensionality for diffusion is inconsistent with available data on lateral diffusion rates, membrane-substrate affinities, and endogenous concentrations of enzymes and their aqueous substrates.

The pancreatic islets of Langerhans have low endogenous antioxidant levels and are thus especially sensitive to oxidative stress, which is known to influence cell survival and behaviour. As bioengineered islets are gaining interest for therapeutic purposes, it is important to understand how their composition can be optimized to diminish oxidative stress. We investigated how the ratio of the two main islet cell types (alpha and beta cells) and their culture in three-dimensional aggregates could protect against oxidative stress. Monolayer and aggregate cultures were established by seeding the alphaTC1 (alpha) and INS1E (beta) cell lines in varying ratios, and hydrogen peroxide was applied to induce oxidative stress. Viability, oxidative stress, and the level of the antioxidant glutathione were measured. Both aggregation and an increasing prevalence of INS1E cells in the co-cultures conferred greater resistance to cell death induced by oxidative stress. Increasing the prevalence of INS1E cells also decreased the number of alphaTC1 cells experiencing oxidative stress in the monolayer culture. In 3D aggregates, culturing the alphaTC1 and INS1E cells in a ratio of 50:50 prevented oxidative stress in both cell types. Together, the results of this study lead to new insight into how modulating the composition and dimensionality of a co-culture can influence the oxidative stress levels experienced by the cells.

Early breast cancer detection is an emerging field of research as it can save many lives infected by malignant tumors. Microwave imaging is one of the main pillars in biomedical fields of comprehensive cancer care. The mathematical theory of microwave tomography involves solving an image reconstruction problem for Maxwell’s equations. In this research contribution, we analyze the potential of an image reconstruction model for the early detection of breast tumors from microwave tomography method. The detection of early-stage tumors within the breast by microwave tomography imaging is challenged by both the moderate endogenous dielectric contrast between healthy and malignant glandular tissues and the spatial resolution available from illumination at microwave frequencies. The formulation as a shape-reconstruction problem offers several advantages compared to more traditional pixel-based schemes, to mention, in particular, well defined boundaries and the incorporation of an intrinsic regularization that reduces the dimensionality of the inverse problem whereby at the same time stabilizing the reconstruction. We present in this paper a novel strategy that can detect very small tumors compared to the wavelength used for illuminating the breast. In addition, our algorithm can determine the sizes and the dielectric properties of the tumors with good accuracy.

Triangular systems with nonadditively separable unobserved heterogeneity provide a theoretically appealing framework for the modeling of complex structural relationships. However, they are not commonly used in practice due to the need for exogenous variables with large support for identification, the curse of dimensionality in estimation, and the lack of inferential tools. This paper introduces two classes of semiparametric nonseparable triangular models that address these limitations. They are based on distribution and quantile regression modeling of the reduced form conditional distributions of the endogenous variables. We show that average, distribution, and quantile structural functions are identified in these systems through a control function approach that does not require a large support condition. We propose a computationally attractive three‐stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. These results establish the validity of the bootstrap for three‐stage estimators of structural functions, and lead to simple inference algorithms. We illustrate the implementation and applicability of all our methods with numerical simulations and an empirical application to demand analysis.

Abstract Colorectal cancer (CRC) is the 2nd cause of cancer-related death. Despite standard therapies, more than 50% of patients experience relapse, eventually with metastatic disease. The CRC microenvironment is densely infiltrated by T-cells, which presence correlates with improved overall survival, thus sustaining the rational for immunotherapy. Here, we paired high-dimensional flow cytometry, bulk RNA sequencing and immunohistochemistry to describe the phenotype and the exhaustion status of T-cells infiltrating primary and metastatic CRC. Analysis of the healthy, peritumoral and neoplastic tissues of treatment-naïve primary CRCs and of the peritumoral and tumoral tissues of CRC patients undergoing surgery for liver metastasis, revealed extensive transcriptional and spatial remodeling across tumors. Unsupervised analysis of flow cytometry data performed by an advanced pipeline of data handling by dimensionality reduction and clustering algorithms allowed the definition of a peculiar inhibitory receptors signature on TILs enriched both in primary CRCs and liver metastases. Of note, CD39 was upregulated in both the signatures retrieved from primary and metastatic CRC, thus suggesting its relevance as molecular target for T-cells engineering. By CRISPR/Cas9 we disrupted the CD39 gene in T cells with >80% efficiency. We combined CD39 knock-out with the genetic disruption of alpha and beta chains of the endogenous TCR, observing >90% efficiency for both genes, thus generating triple-knockout T-cells. By repetitively stimulating healthy donors’ peripheral blood mononuclear cells with autologous antigen-presenting cells loaded with a pool of peptides selected to be immunogenic and expressed by CRC, we obtained a library of anti-tumor TCRs to redirected the specificity of triple knock-out lymphocytes. Our preliminary experiments showed a functional advantage for TCR-redirected, CD39 disrupted T-cells in recognizing and killing CRC target cells. Citation Format: Alessia Potenza, Chiara Balestrieri, Luca Albarello, Federica Pedica, Lorena Stasi, Francesco Manfredi, Martina Spiga, Elena Tassi, Beatrice Claudia Cianciotti, Danilo Abbati, Ugo Elmore, Andrea Biondi, Luca Aldrighetti, Claudia De Lalla, Giulia Di Lullo, Paolo Dellabona, Eliana Ruggiero, Riccardo Rosati, Chiara Bonini. CRISPR/Cas9-mediated CD39 disruption can be combined with TCR editing in T cells to improve the adoptive T cell therapy of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 567.

3122 Background: Tumors are complex ecosystems where exogenous and endogenous cues are integrated to either stimulate or inhibit cancer cell proliferation. However, the nature of these complex cell cycle states, their spatial organization, response to perturbation, and implications for clinical outcomes, are poorly characterized in tumor tissues. Methods: We used multiplexed tissue imaging to develop a robust classifier of proliferation, the multivariate proliferation index (MPI), using 513 unique tumors across five cancer types. Next, we used dimensionality reduction analysis to assess how the patterns of cell cycle protein expression in tumors were altered in response to perturbation. Results: The MPI outperforms single markers, like Ki67, when classifying proliferative index across diverse tumor types and reveals the proliferative architecture of tumors in situ. We find that proliferative and non-proliferative cancer cells are organized across microscopic (cell-to-cell) and macroscopic (tissue-level) scales. Both domains are reshaped by therapy, and local clusters of proliferative and non-proliferative tumor cells preferentially neighbor distinct tumor-infiltrating immune cells. We further phenotyped non-proliferating cancer cells using markers of quiescent cancer cells, cancer stem cells, and dormant cancer cells. We found that these types of non-proliferating cancer cells can occupy distinct regions within the same primary tumor. In high-dimensional marker space, populations of proliferative cancer cells express canonical patterns of cell cycle protein markers, a property we refer to as “cell cycle coherence”. Untreated tumors exist in a continuum of coherence states, ranging from optimal coherence, akin to freely cycling cells in culture, to reduced coherence characterized by either cell cycle polarization or non-canonical marker expression. Coherence can be stereotypically altered by induction and abrogation of mitogen signaling in a HER2-driven model of breast cancer. Cell cycle coherence is modulated by neoadjuvant therapy in patients with localized breast cancer, and coherence is associated with disease-free survival after adjuvant therapy in patients with colorectal cancer, mesothelioma and glioblastoma. Conclusions: The MPI robustly defines proliferating and non-proliferating cells in tissues, with immediate implications for clinical practice and research. The coherence metrics capture the diversity of post-treatment cell cycle states directly in clinical samples, a fundamental step in advancing precision medicine. More broadly, replacing binary metrics with multivariate traits provides a quantitative framework to study temporal processes from fixed static images and to investigate the rich spatial biology of human cancers.

We previously introduced the brain erythropoietin (EPO) circle as a model to explain the adaptive ‘brain hardware upgrade’ and enhanced performance. In this fundamental circle, brain cells, challenged by motor-cognitive tasks, experience functional hypoxia, triggering the expression of EPO among other genes. We attested hypoxic cells by a transgenic reporter approach under the ubiquitous CAG promoter, with Hif-1α oxygen-dependent degradation-domain (ODD) fused to CreERT2-recombinase. To specifically focus on the functional hypoxia of excitatory pyramidal neurons, here, we generated CaMKIIα-CreERT2-ODD::R26R-tdTomato mice. Behavioral challenges, light-sheet microscopy, immunohistochemistry, single-cell mRNA-seq, and neuronal cultures under normoxia or hypoxia served to portray these mice. Upon complex running wheel performance as the motor-cognitive task, a distinct increase in functional hypoxic neurons was assessed immunohistochemically and confirmed three-dimensionally. In contrast, fear conditioning as hippocampal stimulus was likely too short-lived to provoke neuronal hypoxia. Transcriptome data of hippocampus under normoxia versus inspiratory hypoxia revealed increases in CA1 CaMKIIα-neurons with an immature signature, characterized by the expression of Dcx, Tbr1, CaMKIIα, Tle4, and Zbtb20, and consistent with accelerated differentiation. The hypoxia reporter response was reproduced in vitro upon neuronal maturation. To conclude, task-associated activity triggers neuronal functional hypoxia as a local and brain-wide reaction mediating adaptive neuroplasticity. Hypoxia-induced genes such as EPO drive neuronal differentiation, brain maturation, and improved performance.

Five established clearing protocols were compared with a modified and simplified method to determine an optimal clearing reagent for three-dimensionally visualizing fluorophores in the murine liver, a challenging organ to clear. We report successful clearing of whole liver lobes by modification of an established protocol (UbasM) using only Ub-1, a urea-based amino sugar reagent, in a simpler protocol that requires only a 24-h processing time. With Ub-1 alone, we observed sufficiently preserved liver tissue structure in three dimensions along with excellent preservation of fluorophore emissions from endogenous protein reporters and lipophilic tracer dyes. This streamlined technique can be used for 3D cell lineage tracing and fluoroprobe-based reporter gene expression to compare various experimental conditions.

Abstract Mechanical reductionism, which deals entirely with homogeneous variables, will constrain and enable the activities of richly heterogeneous living systems, but it cannot determine their outcomes. Such indeterminism owes to problems with dimensionality, dynamical logic, intractability, and insufficiency. The order in any living structure arises via an historical series of contingencies that were selected endogenously by stable autocatalytic processes in tandem with, and usually in opposition to, conventional external influences (natural selection). The development of living communities thereby resembles a Heraclitean dialectic between processes that build up and those that tear down. Investigating this unconventional dynamic requires metaphysical assumptions that are complementary to those that have guided science over the past three centuries. The new dynamics can be represented in terms of weighted networks of interacting processes, which facilitate the statement of testable hypotheses. Network analysis thereby implements and tests ideas that heretofore could only be addressed as verbal propositions.

SynopsisSir Aubrey Lewis studied 61 depressives in considerable detail, principally cross-sectionally but also by reviewing progress. He concluded that he could find no qualitative distinctions between the depressed patients and thus established himself as a strong and influential advocate of the unitary view of depression (i.e. that depression varies dimensionally, not categorically). Subsequently, Kiloh & Garside (proponents of the binary view of two depressive ‘types’) coded the Lewis data and undertook a principal components analysis. They claimed success in distinguishing ‘endogenous’ and ‘neurotic’ depressive types within Lewis' sample. In this paper we re-analyse the data set using both a latent class categorical approach and mixture analyses. We suggest that any demonstration of sub-types was limited by relative homogeneity of the sample (in that up to 80% had probable or possible psychotic conditions), and by Lewis rating a number of important features (e.g. delusions) dimensionally rather than categorically. Nevertheless, we identify one categorical class (essentially an agitated psychotic depressive condition) and a residual (presumably heterogeneous) class. The presence of those two classes was supported by demonstrating bimodality in composite scores derived from the fourteen differentiating clinical features (and not evident when all clinical features were considered), and formally confirmed by mixture analyses. Membership of the categorical class was determined principally by psychotic features (delusions and hallucinations) and by objectively-judged psychomotor disturbance, and we consider the nature of that ‘class’. Lewis' data set is unusual (in having self-report and observationally rated data), and historically important in demonstrating that conclusions may depend on the choice of variables examined and analytical approaches.