SPITFIR(e): A supermaneuverable algorithm for restoring 2D-3D fluorescence images and videos, and background subtraction

While fluorescent microscopy imaging has become the spearhead of modern biology as it is able to generate long-term videos depicting 4D nanoscale cell behaviors, it is still limited by the optical aberrations and the photon budget available in the specimen and to some extend to photo-toxicity. A direct consequence is the necessity to develop flexible and "off-road" algorithms in order to recover structural details and improve spatial resolution, which is critical when pushing the illumination to the low levels in order to limit photo-damages. Moreover, as the processing of very large temporal series of images considerably slows down the analysis, special attention must be paid to the feasibility and scalability of the developed restoration algorithms. To address these specifications, we present a very flexible method designed to restore 2D-3D+Time fluorescent images and subtract undesirable out-of-focus background. We assume that the images are sparse and piece-wise smooth, and are corrupted by mixed Poisson-Gaussian noise. To recover the unknown image, we consider a novel convex and non-quadratic regularizer Sparse Hessian Variation) defined as the mixed norms which gathers image intensity and spatial second-order derivatives. This resulting restoration algorithm named SPITFIR(e) (SParse fIT for Fluorescence Image Restoration) utilizes the primal-dual optimization principle for energy minimization and can be used to process large images acquired with varied fluorescence microscopy modalities. It is nearly parameter-free as the practitioner needs only to specify the amount of desired sparsity (weak, moderate, high). Experimental results in lattice light sheet, stimulated emission depletion, multifocus microscopy, spinning disk confocal, and wide-field microscopy demonstrate the generic ability of the SPITFIR(e) algorithm to efficiently reduce noise and blur, and to subtract undesirable fluorescent background, while avoiding the emergence of deconvolution artifacts.

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Enhancing Quantitative and Data Science Education for Graduate Students in Biomedical Science

Substantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science, but far less attention has been paid to the graduate curriculum. In this setting, we propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities. Due to the diversity of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We developed a novel prioritization approach in which we mined and analyzed quantitative concepts and skills from publications that faculty in relevant units deemed central to the scientific comprehension of their field. The analysis provides a prioritization of quantitative skills and concepts and could represent an effective method to drive curricular focus based upon program-specific faculty input for biological science programs of all types. Our results highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty.One Sentence SummaryWe developed a novel approach to prioritize quantitative concepts and methods for inclusion in a graduate biomedical science curriculum based upon approaches included in faculty-identified key publications.

Features of the microscopic structure of the adrenal gland of geese

The study of the morphology of the adrenal gland of birds is one of the most critical issues of modern biology and veterinary medicine cause its solution contributes to the scientific justification of technologies for rearing, using, and treating birds. The work aims to clarify the features of the microscopic structure of the adrenal gland of geese. As a peripheral organ of the endocrine system, the adrenal gland affects the growth and differentiation of tissues, regulates water, protein, carbohydrate, fat, and mineral metabolism, the body's resistance to infections, intoxication, stress, and other factors. During research were used histological indagation methods. It was found that blood vessels, clusters of nerve cells are registered in the capsule of the adrenal gland of geese. Nodes of the sympathetic nervous system are registered outside it. Connective tissue septa with hemocapillaries extend from the capsule to the adrenal parenchyma, which is represented by intertwined cell strands of interrenal and suprarenal tissues. The narrow spaces between these cell strands are filled with layers of loose fibrous connective tissue with sinusoidal hemocapillaries and venous sinuses. The subcapsular layer, peripheral and central zones are recorded on the incision of the adrenal gland. The adrenal vein is localized in the central zone. The subcapsular zone is mainly represented by cells of suprarenal tissue, the peripheral and central zone – by cells of interrenal tissue. Suprarenal tissue cells have a polygonal shape, basophilic cytoplasm, and a rounded, centrally located nucleus. Cells of interrenal tissue are columnar or cubic, have eosinophilic colored cytoplasm, a round or oval nucleus placed eccentrically. Venous sinuses are localized in the central and peripheral zones of the adrenal gland. Their wall is thin, formed by flat endotheliocytes, bounded by groups of cells of interrenal and suprarenal tissues. As a result, established microscopic structural features of the adrenal gland of geese can be used to formulate the base of its typical morphological characteristics, which give a possibility to assess the Morpho-functional essential state of the bird adrenal gland of this species by the influence of various factors and pathologies, in perspective of following researches – the exploration of morphometric parameters of the structural components of the adrenal gland of geese.

Psychoanalysing Technoscience

While the previous chapter discussed the shift from Hegelian dialectics to dialectical materialism, this chapter addresses the shift from dialectics to psychoanalysis, notably in France, paying due attention to the productive tensions between both approaches. After a concise exposition of Freudian psychoanalysis, focussing on Beyond the Pleasure Principle, the text in which Freud explicitly “plunged into the thickets” of modern biology (Gay, 1988, p. 401), I will extensively discuss the views of Gaston Bachelard and Jacques Lacan on technoscience. Building on a previous publication (Zwart, 2019a), where I already presented a psychoanalytic understanding of technoscience, which I don’t want to duplicate here (focussing on the oeuvres of Sigmund Freud, Carl Gustav Jung, Gaston Bachelard and Jacques Lacan), I will now emphasise the continuity between dialectic and psychoanalysis, indicating how dialectics remains an important moment in Bachelard’s and Lacan’s efforts to develop a psychoanalysis of technoscience, both as a discourse and as a practice. In addition, I will elucidate the added value of this convergence by extrapolating it to three concrete case studies, one borrowed from particle physics and two from life sciences research: the Majorana particle, the malaria mosquito and the nude mouse.

The microbiome impacts host hybridization and speciation

Microbial symbiosis and speciation profoundly shape the composition of life’s biodiversity. Despite the enormous contributions of these two fields to the foundations of modern biology, there is a vast and exciting frontier ahead for research, literature, and conferences to address the neglected prospects of merging their study. Here, we survey and synthesize exemplar cases of how endosymbionts and microbial communities affect animal hybridization and vice versa. We conclude that though the number of case studies remain nascent, the wide-ranging types of animals, microbes, and isolation barriers impacted by hybridization will likely prove general and a major new phase of study that includes the microbiome as part of the functional whole contributing to reproductive isolation. Though microorganisms were proposed to impact animal speciation a century ago, the weight of the evidence supporting this view has now reached a tipping point.

Genetically Encoded Fluorescent Biosensors for Biomedical Applications

One of the challenges of modern biology and medicine is to visualize biomolecules in their natural environment, in real-time and in a non-invasive fashion, so as to gain insight into their physiological behavior and highlight alterations in pathological settings, which will enable to devise appropriate therapeutic strategies. Genetically encoded fluorescent biosensors constitute a class of imaging agents that enable visualization of biological processes and events directly in situ, preserving the native biological context and providing detailed insight into their localization and dynamics in cells. Real-time monitoring of drug action in a specific cellular compartment, organ, or tissue type; the ability to screen at the single-cell resolution; and the elimination of false-positive results caused by low drug bioavailability that is not detected by in vitro testing methods are a few of the obvious benefits of using genetically encoded fluorescent biosensors in drug screening. This review summarizes results of the studies that have been conducted in the last years toward the fabrication of genetically encoded fluorescent biosensors for biomedical applications with a comprehensive discussion on the challenges, future trends, and potential inputs needed for improving them.

3GOLD: Optimized Levenshtein distance for clustering third-generation sequencing data

Background:Third-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence have rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data.Results: We successfully introduced a bidirectional frameshift allowance with end-user determined accommodation caps combined with weighted error discrimination. Furthermore, our modifications dramatically improved the computational speed of Levenstein distance. For simulated ONT MinION and PacBio Sequel datasets, the average clustering sensitivity for 3GOLD was 41.45% (S.D. 10.39) higher than Sequence-Levenstein distance, 52.14% (S.D. 9.43) higher than Levenshtein distance, 55.93% (S.D. 8.67) higher than Starcode, 42.68% (S.D. 8.09) higher than CD-HIT-EST and 61.49% (S.D. 7.81) higher than DNACLUST. For biological ONT MinION data, 3GOLD clustering sensitivity was 27.99% higher than Sequence-Levenstein distance, 52.76% higher than Levenshtein distance, 56.39% higher than Starcode, 48% higher than CD-HIT-EST and 70.4% higher than DNACLUST.Conclusion:Our modifications to Levenshtein distance have improved its speed and accuracy compared to the classic Levenshtein distance, Sequence-Levenshtein distance and other commonly used clustering approaches on simulated and biological third-generation sequenced datasets. Our clustering approach is appropriate for datasets of unknown cluster centroids, such as those generated with unique molecular identifiers as well as known centroids such as barcoded datasets. A strength of our approach is high accuracy in resolving small clusters and mitigating the number of singletons.

Very Stable Two Mega Dalton High-Molecular-Mass Multiprotein Complex from Sea Cucumber Eupentacta fraudatrix

In contrast to many human organs, only the human liver can self-regenerate, to some degree. Some marine echinoderms are convenient objects for studying the processes of regenerations of organs and tissues. For example, sea cucumbers Eupentacta fraudatrix can completely restore within several weeks, the internal organs and the whole body after their division into two or three parts. Therefore, these cucumbers are a very convenient model for studying the general mechanisms of regeneration. However, there is no literature data yet on which biomolecules of these cucumbers can stimulate the regeneration of organs and the whole-body processes. Studying the mechanisms of restoration is very important for modern biology and medicine, since it can help researchers to understand which proteins, enzymes, hormones, or possible complexes can play an essential role in regeneration. This work is the first to analyze the possible content of very stable protein complexes in sea cucumbers Eupentacta fraudatrix. It has been shown that their organisms contain a very stable multiprotein complex of about 2000 kDa. This complex contains 15 proteins with molecular masses (MMs) >10 kDa and 21 small proteins and peptides with MMs 2.0–8.6 kDa. It is effectively destroyed only in the presence of 3.0 M MgCl2 and, to a lesser extent, 3.0 M NaCl, while the best dissociation occurs in the presence of 8.0 M urea + 0.1 M EDTA. Our data indicate that forming a very stable proteins complex occurs due to the combination of bridges formed by metal ions, electrostatic contacts, and hydrogen bonds.