Miracle-dz/UniFAR-FTUs huggingface dataset:
The ins and outs of membrane bending by intrinsically disordered proteins

Membrane curvature is essential to diverse cellular functions. While classically attributed to structured domains, recent work illustrates that intrinsically disordered proteins are also potent drivers of membrane bending. Specifically, repulsive interactions among disordered domains drive convex bending, while attractive interactions, which lead to liquid-like condensates, drive concave bending. How might disordered domains that contain both repulsive and attractive domains impact curvature? Here we examine chimeras that combine attractive and repulsive interactions. When the attractive domain was closer to the membrane, its condensation amplified steric pressure among repulsive domains, leading to convex curvature. In contrast, when the repulsive domain was closer to the membrane, attractive interactions dominated, resulting in concave curvature. Further, a transition from convex to concave curvature occurred with increasing ionic strength, which reduced repulsion while enhancing condensation. In agreement with a simple mechanical model, these results illustrate a set of design rules for membrane bending by disordered proteins.
Development of a 3D Bioprinted Airway Smooth Muscle Model for Manipulating Structure and Measuring Contraction

The contractile function of airway smooth muscle (ASM) is inextricably linked to its mechanical properties and interaction with the surrounding mechanical environment. As tissue engineering approaches become more commonplace for studying lung biology, the inability to replicate realistic mechanical contexts for ASM will increasingly become a barrier to a fulsome understanding of lung health and disease. To address this knowledge gap, we describe the use of 3D bioprinting technology to generate a novel experimental model of ASM with a wide scope for modulating tissue mechanics. Using a stiffness modifiable alginate-collagen-fibrinogen bioink, we demonstrate that modulating the stiffness of free-floating ASM ‘bare rings’ is unfeasible; bioink conditions favorable for muscle formation produce structures that rapidly collapse. However, the creation of novel ‘sandwich’ and ‘spiderweb’ designs that encapsulate the ASM bundle within stiff acellular load bearing frames successfully created variable elastic loads opposing tissue collapse and contraction. Sandwich and spiderweb constructs demonstrated realistic actin filament organisation, generated significant baseline tone, and responded appropriately to acetylcholine, potassium chloride and cytochalasin D. Importantly, the two designs feasibly simulate different mechanical contexts within the lung. Specifically, the sandwich was relatively compliant and subject to plastic deformation under high contractile loads, whereas the stiffer spiderweb was more robust and only deformed minimally after repeated maximal contractions. Thus, our model represents a new paradigm for studying ASM contractile function in a realistic mechanical context. Moreover, it holds significant capacity to study the effects of ECM composition, multiple cell types and fibrosis on lung health and disease. GRANTS Natural Sciences and Engineering Research Council, Discovery Grant (Adrian West) Research Manitoba, New Investigator Operating Grant (Adrian West) Children’s Hospital Research Institute of Manitoba, Operating Grant (Adrian West) Canadian Foundation for Innovation, John R. Evans Leaders Fund (Adrian West) University of Manitoba, Manitoba Graduate Scholarship (Jeffery Osagie) Research Manitoba, Master’s Studentship Award (Jeffery Osagie) Research Manitoba, Master’s Studentship Award (Sanjana Syeda) Children’s Hospital Research Institute of Manitoba, Summer Studentship (Michelle Guimond) University of Manitoba, Jack Prior Memorial Undergraduate Student Research Award (Lumiere Parrenas) University of Manitoba, Undergraduate Research Award (Ahsen Haroon) University of Manitoba, UMSU Undergraduate Research Award (Philip Imasuen) The grant bodies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Combination drug screen targeting glioblastoma core vulnerabilities reveals pharmacological synergisms

Synergistic drug combinations are an attractive anticancer strategy but prove challenging to identify. Here we present a stepwise approach consisting in revealing core cancer vulnerabilities and exploiting them through drug combination screen to uncover synergistic treatments for glioblastoma patients. Methods We established an innovative method, based on high-throughput screening, target deconvolution and functional genomics, to reveal core vulnerabilities in glioblastoma. Combination drug screen targeting these vulnerabilities was then designed to unveil synergistic associations. The therapeutic potential of the top drug combination was validated in two different clinically-relevant models: an organotypic ex vivo model and a syngeneic orthotopic mouse model of glioblastoma. Results Large-scale monotherapy drug screening identified 83 potent anti-glioblastoma compounds. Target deconvolution using public chemoinformatic databases uncovered 1,100 targets and interactors of the hit compounds. Screening of a focused siRNA library targeting the top 292 drug interactors revealed 22 targetable vulnerabilities, 9 of which were confirmed as core glioblastoma vulnerabilities by mining the CRISPR screen cohort data from the online Cancer Dependency Map portal. Six selective inhibitors of the core vulnerabilities were then screened in combination with a custom-made library of 88 compounds and synergies amongst the 528 tested pairwise combinations were predicted. The combinations of CHK1 / MEK and AURKA / BET inhibitors were highlighted and validated in 3D tumor spheroids. Using an organotypic ex vivo model and a syngeneic orthotopic mouse model, we definitively ascertained the efficacy of dual AURKA / BET inhibition in glioblastoma. Conclusions Collectively, we uncovered that dual inhibition of BET proteins and aurora kinase A is highly synergistic against GBM. Moreover, our study indicates that our approach to exploit drug poly-pharmacology for the rational design of drug combination screens represent a valuable strategy to discover synergistic treatments against refractory cancers.
Startle: a star homoplasy approach for CRISPR-Cas9 lineage tracing

CRISPR-Cas9 based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. While standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9 induced mutations. Here, we introduce the star homoplasy model, a novel evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the non-modifiability property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies by identifying a relationship between the star homoplasy model and the binary perfect phylogeny model. We use this characterization to develop an algorithm, Startle (Star tree lineage estimator), that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated CRISPR-based lineage tracing data compared to existing methods; particularly on data with high amounts of dropout and homoplasy. Startle also infers more parsimonious phylogenies with fewer metastatic migrations on a lineage tracing dataset from mouse metastatic lung adenocarcinoma. Code availability Software is available at https://github.com/raphael-group/startle
WKB solution of the Stark effect in hydrogen

We present an analytical solution for the nonrelativistic Stark effect in hydrogen separated in parabolic coordinates. The solution is given in terms of elliptic integrals and is obtained in a first-order WKB approximation. In addition, by a suitable approximation, we extend the WKB results to autoionizing states, above the threshold barrier. A short comparison of the present results with exact numerical calculations is also presented.
Use of carbon micro-particles for improved infrared temperature measurement of CMOS MEMS devices

We report a technique which can be used to improve the accuracy of infrared (IR) surface temperature measurements made on micro-electro-mechanical systems (MEMS). In this work, a silicon-on-insulator (SOI) complementary metal oxide semiconductor (CMOS) MEMS thermal flow sensor was studied. The device consists of a meander-shaped resistive heater element that was fabricated using CMOS aluminum metallization and embedded in an SOI silicon oxide (SiO2) membrane. Conventional IR temperature measurements, made on the active device, were shown to give significant surface temperature errors—a consequence of the optical transparency of the SiO2 membrane and low emissivity of the metalized areas. Radiative carbon micro-particles were used to improve the measurement accuracy. By making IR measurements on carbon micro-particles placed in isothermal contact with parts of the MEMS sensor's structure, the accuracy of the surface temperature determination was significantly improved. The peak device operating temperatures measured using the ‘IR micro-particle’ technique were found to be in good agreement with those obtained from thermoelectric characterization. The work shows how wide area surface temperature profiles can be quickly built up by imaging multiple micro-particles using an IR charge-coupled device (CCD) detector array. The IR micro-particle technique removes the problems associated with coating MEMS sensors with a uniform high emissivity layer, which may cause damage to such devices and/or affect their thermal performance due to additional heat spreading.
Machine Learning-Based Approach for Fake News Detection

In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset.
Investigation of Ant Colony Optimization Implementation Strategies For Low-Memory Operating Environments

In search guided by meta-heuristics , a fundamental tradeoff exists between exploitation of good solutions ( intensification ) and exploration of the solution space for better solutions ( diversification ). Over-exploitation can limit the search to suboptimal solutions while over-exploration can reduce the efficiency of the overall search. Ant Colony Optimization (ACO) is a well-known meta-heuristic inspired by biological ants for solving NP-hard combinatorial search problems like the Traveling Salesman Problem (TSP). In nature, biological ant colonies navigate to find efficient paths around complex obstacles by depositing and following simple chemicals called pheromones. ACO algorithms model this behavior by implementing sets of artificial ants which iteratively explore a solution space, marking high quality solutions with “pheromone” values and biasing subsequent searches toward those values. TSP serves as a benchmark for evaluating ACO algorithms. Current implementations of ACO algorithms require the storage of distances and pheromone levels between all pairs of vertices. This dissertation presents a variant that has significantly lower storage requirements, thus rendering it practical for use on larger problem instances. The tradeoff between memory use and solution quality is empirically investigated using benchmark TSP problem instances. MAX-MIN Ant System, one of the best available ACO variants, serves as a baseline method for comparison. The variant of ACO algorithm proposed in this dissertation provides memory savings in terms of both distances stored and pheromone levels stored. Instead of maintaining distances between all pairs of vertices, distances between each vertex its k nearest neighbors are maintained; k is chosen to be much smaller than the number of vertices in the graph. An efficient approximate algorithm is used to identify nearest neighbors. Observing that values most within a narrow range in an iteration, the ACO in this study a default pheromone value and uses a sparse matrix that only maintains values between a pair of if it differs from the default value by more than a tolerance limit; the default pheromone level is updated in each iteration. The memory requirement for pheromone storage thus decreases with increasing tolerance limit, the cost of
A nonempirical model of the gas‐cell atomic frequency standard

In this paper a signal model of the rubidium (Rb) atomic frequency standard is developed. The model combines an analysis of the atomic physics required to describe processes occurring within the Rb absorption cell with a feedback analysis of the clock’s servocontrol circuitry, and thus allows clock performance in terms of Allan variance to be predicted from a number of electronic and physical parameters. All previous models of the Rb clock have been limited to an analysis of the clock’s short‐term performance, Allan variance averaging times less than 10 000 s. However, by explicitly including the effects of discharge lamp intensity fluctuations, which are transformed into output frequency variations via the light shift effect, clock performance can be predicted for averaging times greater than 10 000 s. Furthermore, the present model is the first which incorporates the influence of an optically thick Rb vapor, along with the diffusion of optically pumped atoms to the walls of the absorption cell, into the.
Topological Initialization of Injective Integer Grid Maps

Integer Grid Maps (IGM) are a class of mappings characterized by integer isolines that align up to unit translations and rotations of multiples of 90 degrees. They are widely used in the context of remeshing, to lay a quadrilateral grid onto the mapped surface. Computing an IGM is notoriously a challenging task, because it requires to solve a numerical problem with mixed discrete and continuous variables which is known to be NP-Hard. As a result, state of the art methods rely on heuristics that may occasionally fail to produce a valid quadrilateral mesh. Existing pipelines incorporate a final sanitization step which attempts to fix such defects, but no guaranteees can be given in this regard. In this paper we propose a simple topological construction that allows to reduce the problem of computing an IGM to the one of mapping a topological disk to a convex domain. This is a much easier problem to deal with, because it does not endow integer translational and rotational constraints, permitting to obtain a parameterization that is guaranteed to incorporate all the correct integer transitions and to not contain degenerate or inverted elements. Despite provably correct, the so generated maps contain a considerable amount of geometric distortion and a poor quad connectivity, making this technique more suitable for a robust initialization rather than for the computation of an application-ready IGM. In the article we present the details of our construction, also analyzing its geometric and topological properties.
THE FLUORESCENCE EFFICIENCIES OF THE CN VIOLET BANDS IN COMETS

We have carried out calculations of the fluorescent equilibrium of the CN molecule in the solar radiation field as a function of heliocentric radial velocity and distance. The detailed rotational line intensities of the CN violet 0–0 band have been recalculated for radial velocities between −60 and +60 km s−1 and at 10 distances in steps of from 0.25 to 5.7 AU. Comparisons with observed CN violet 0–0 band profiles yield reasonable agreement and remaining discrepancies are well understood. Based on the derived equilibrium rotational level populations of the ground state, 1–1 band rotational line intensities were also calculated. Total band fluorescence efficiencies for the violet Δν = 0 sequence are tabulated for the above range of distances and velocities, permitting the interpolation to any given comet's observational circumstances. These fluorescence efficiencies, L/N, or “g-factors,” needed to reduce CN emission band fluxes to CN abundances, are also accessible via a Web-based service. The values at any heliocentric distance vary by about a factor of two with varying velocities, but the specific structure of this variation with velocity changes dramatically with distance.
Coupling of defect pairs and generation of dynamical band gaps in the impurity bands of nonlinear photonic crystals for all-optical switching

We investigate the coupling of two single defects in two-dimensional photonic crystals (PCs) with the same frequency but different field distributions. The defect pair like this is generally present in PCs as a combination of a reduced-size defect and an increased-size defect. In spite of the significant difference in field distribution, quasiflat impurity bands suitable for the transmission of ultrashort pulses can be achieved by properly choosing defect pairs. More importantly, the coupled cavity waveguide constructed with defect pairs offers an opportunity to establish a periodic modulation of defect modes with a control light. The dynamical band gap generated by the periodic modulation of defect modes suggests a high-efficiency all-optical switching operation in nonlinear PCs.
A new LC‐MS/MS assay for the quantification of Aβ40 and Aβ42 in plasma: validation and clinical performance

Early detection of Alzheimer’s disease (AD) represents an unmet clinical need. Cerebrospinal fluid (CSF) biomarkers can potentially fulfil this need, but specimen collection is invasive. Blood‐derived biomarkers are preferable, and several studies have shown an association between the Aβ42/40 ratio and amyloid PET status. However, blood diagnostic tests for AD are challenging due to interlaboratory differences in analytical techniques and preanalytical handling. Here we describe the development and validation of a LC‐MS/MS assay to quantify Aβ40 and Aβ42 from plasma and assess clinical performance.
CONSTANT EFFORT AND CONSTANT QUOTAFISHING POLICIES WITH CUT‐OFFS IN A RANDOM ENVIRONMENT

ABSTRACT. Consider a population subjected to constant effort or constant quota fishing with a generaldensity‐dependence population growth function (because that function is poorly known). Consider environmental random fluctuations that either affect an intrinsic growth parameter or birth/death rates, thus resulting in two stochastic differential equations models. From previous results of ours, we obtain conditions for non‐extinction and for existence of a population size stationary density. Constant quota (which always leads to extinction in random environments) and constant effort policies are studied; they are hard to implement for extreme population sizes. Introducing cut‐offs circumvents these drawbacks. In a deterministic environment, for a wide range of values, cutting‐off does not affect the steady‐state yield. This is not so in a random environment and we will give expressions showing how steady‐state average yield and population size distribution vary as functions of cut‐off choices. We illustrate these general results with function plots for the particular case of logistic growth.
Mingle with an Amoeba

Amoebas are microorganisms ranging in length from 25 to 400µm and are not large enough for humans to touch. Also, since they are not visible in everyday life, it isn’t easy to get involved with amoebas. In this project, we create an opportunity to have simulated contact with amoebas in a virtual environment. By touching the transparent slime as if it were an amoeba, you can experience transforming the amoeba with your own hands, seeing the amoeba’s reaction to being touched, and perceiving Pseudo Haptics information via visual vibrations from the amoeba
An algorithm for total variation regularization in high-dimensional linear problems

This paper describes an iterative algorithm for high-dimensional linear inverse problems, which is regularized by a differentiable discrete approximation of the total variation (TV) penalty. The algorithm is an interlaced iterative method based on optimization transfer with a separable quadratic surrogate for the TV penalty. The surrogate cost function is optimized using the block iterative regularized algebraic reconstruction technique (RSART). A proof of convergence is given and convergence is illustrated by numerical experiments with simulated parallel-beam computerized tomography (CT) data. The proposed method provides a block-iterative and convergent, hence efficient and reliable, algorithm to investigate the effects of TV regularization in applications such as CT.
High Resolution Lifetime Studies of the d 3Πg, C 1Πg and D 1Σu+ States in C2 with Applications to Estimates of the Solar Carbon Abundance

Lifetimes have been measured for a number of vibrational levels of the d3Πg, C1Πg and D1Σu+ states in the C2 molecule using the High Frequency Deflection technique. Particular attention was paid to various rotational levels of the v' = 6 level of the d3Πg state but no decrease in lifetime was found indicating the absence of earlier suggested predissociation effects, which would be an explanation of the occurrence of the high-pressure carbon bands. For the d-a (Swan) bands oscillator strengths are found which are considerably higher than earlier estimates demanding a 40% decrease in the estimate of the solar carbon abundance.
Exact nonparametric inference for component lifetime distribution based on lifetime data from systems with known signatures

In this paper, we develop exact nonparametric statistical inference for some characteristics of the component lifetime distribution based on the lifetimes of coherent systems with known signatures. Distribution-free confidence limits for population quantiles of component lifetime distributions are derived. Computational formulas as well as a procedure for choosing suitable confidence limits are presented. Construction of tolerance limits for the component lifetime distribution is also described. Finally, some examples are presented to illustrate the methods of inference developed here.
Simple Techniques for a Novel Human Body Pose Optimisation Using Differentiable Inverse Rendering

Human body 3D reconstruction has a wide range of applications including 3D-printing, art, games, and even technical sport analysis. This is a challenging problem due to 2D ambiguity, diversity of human poses, and costs in obtaining multiple views. We propose a novel optimisation scheme that bypasses the prior bias bottleneck and the 2D-pose annotation bottleneck that we identify in single-view reconstruction, and move towards low-resource photo-realistic 3D reconstruction that directly and fully utilises the target image. Our scheme combines domain-specific method SMPLify-X and domain-agnostic inverse rendering method redner, with two simple yet powerful techniques. We demonstrate that our techniques can 1) improve the accuracy of the reconstructed body both qualitatively and quantitatively for challenging inputs, and 2) control optimisation to a selected part only. Our ideas promise extension to more difficult problems and domains even beyond human body reconstruction.