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Phase transitions in cells provide a wonderful arena for investigation by students of physics, chemistry, and biology to come together to apply their foundational knowledge. In this article, we provide an intuitive approach to the basic physical principles of phase separation. We then highlight several examples that illustrate how the cell, the fundamental unit of life, makes use of phase separation to organise its contents. [ABSTRACT FROM AUTHOR]

With the advent of continuous direct compression (CDC) process, it becomes increasingly desirable to characterize inherent powder blend heterogeneity at a small batch scale for a robust and CDC-amenable formulation. To accomplish this goal, a near infrared spectroscopy (NIRS)-based characterization approach was developed and implemented on multiple direct compression (DC) blends in this study, with the intended purpose of complementing existing formulation development tools and enabling to build an early CMC data package for late-phased process analytical technology (PAT) method development. Three fumaric acid DC blends, designed to harbor varied degrees of inherent blend heterogeneity, were employed. Near infrared spectral data were collected on a kg-scale batch blender via both time- and angle-based triggering modes. The time-triggered data were used to investigate the blending heterogeneity with respect to rotation angles, while the angle-triggered data were used to provide blending variability characterization and compare against off-line HPLC-based results. The time-triggered data revealed that the greatest blend variability was observed between revolutions, while the blending variability within a single revolution stayed relatively low with respect to rotation angles. This confirmed earlier literature findings that the bottom layer of powder blends tends to move with the blender within each revolution, and the most intense powder mixing takes place across revolutions. This also indicates the use of blending speed and the number of co-adds are not able to increase sampling volume to improve signal-to-noise ratio under a tumble-bin blender as what were typically done in a feedframe application. The angle-triggered data showed that there is a consistent trend between NIRS and HPLC-based methods on characterizing blend heterogeneity across the blends at a given sample size. This study contributes to establishing NIRS as a potential characterization approach for inherent powder blend heterogeneity for early R&D. It also highlights the promise of continuous characterization of inherent powder blend heterogeneity from gram scale to mini-batch CDC scale. [ABSTRACT FROM AUTHOR]

Shear banding in entangled polymer solutions is an elusive phenomenon in polymer rheology. One recently proposed mechanism for the existence of banded velocity profiles in entangled polymer solutions stems from a coupling of the flow to banded concentration profiles. Recent work [Burroughs et al., Phys. Rev. Lett.126, 207801 (2021)] provided experimental evidence for the development of large gradients in concentration across the fluid. Here, a more systematic investigation is reported of the transient and steady-state banded velocity and concentration profiles of entangled polybutadiene in dioctyl phthalate solutions as a function of temperature (T) , number of entanglements (Z), and applied shear rate (W i a p p ), which control the susceptibility of the fluid to unstable flow-concentration coupling. The results are compared to a two-fluid model that accounts for coupling between elastic and osmotic polymer stresses, and a strong agreement is found between model predictions and measured concentration profiles. The interface locations and widths of the time-averaged, steady-state velocity profiles are quantified from high-order numerical derivatives of the data. At high levels of entanglement and large W i a p p , a significant wall slip is observed at both inner and outer surfaces of the flow geometry but is not a necessary criterion for a nonhomogeneous flow. Furthermore, the transient evolution of flow profiles for large Z indicate transitions from curved to "stair-stepped" and, ultimately, a banded steady state. These observed transitions provide detailed evidence for shear-induced demixing as a mechanism of shear banding in polymer solutions. [ABSTRACT FROM AUTHOR]

In this letter, we consider a novel problem of blind source separation from observed magnitude-only measurements of their convolutive mixture in different communication systems. The problem setups correspond to a blind receiver architecture that either does not have phase information in the measurements or has excessive phase noise that cannot be easily recovered. We have formulated the problem as a matrix recovery problem by using the lifting technique and proposed a convex programming-based solution for joint recovery of the unknown channel and source signals. We have implemented the proposed solution using the alternating direction method of multipliers (ADMM). We have plotted a phase transition diagram for random Gaussian subspaces that shows, for s source signals each of length n and channel of length k, the minimum measurements required for exact recovery are m ≥ 1.19 (s n + k) log 2 m that is in accord with our theoretical result. We have also plotted a phase transition diagram for the case where the channel delays matrix is deterministic (consisting of the first k columns of the identity matrix) that shows the minimum measurements required for exact recovery are m ≥ 2.86 (s n + k) log 2 m which are higher than random subspaces. [ABSTRACT FROM AUTHOR]

[Display omitted] • New methodology allows exploration of full formulation or phase diagram. • Mixing rules predict properties of binary and ternary mixtures. • Demixing rules predict properties of individual pure component powders. • Can be used to obtain parameters of materials that show poor tabletting. • Quantitative and qualitative agreement of compaction pressures and shear stresses. A set of numerical methods is described that allows predictive finite element method (FEM) simulations of the compaction of multi-component pharmaceutical powder formulations across the entire range of compositions. An automated parametrisation procedure was used to extract density-dependent Drucker-Prager Cap (dDPC) model parameters from experimental data. Subsequently, these parameters were interpolated (mixed) or extrapolated (demixed) to predict dDPC model parameters of unseen powder formulations. Pure, binary, and ternary formulations of micro-crystalline cellulose (MCC, plastic), dibasic calcium phosphate dihydrate (DCPD, brittle), and pre-gelatinised starch (STA, elastic) powders were used to validate the parametrisation and mixing/demixing methodologies. FEM simulations were capable of reproducing compaction curves with errors only marginally greater than the experimental variability. Using only pure component data, FEM simulations with mixing rules were capable of predicting the compaction curves of mixtures as well as their shear stress distributions. Moreover, with data of only two or three powder formulations, a new demixing methodology was able to predict the behaviour of the constituent powders. The combination of these methodologies provides a powerful tool to rapidly explore powder formulations anywhere within the composition phase diagram, providing compaction curves but also stress profiles that are essential to early-stage formulation process development and tooling design. [ABSTRACT FROM AUTHOR]

A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells' shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.

Neutron diffraction in combination with isotopic substitution on the zero-scatterer 62 Ni 43 63 Cu 57 shows indications for chemical short-range order in the stable liquid as evidenced by oscillations in the concentration-concentration structure factor S CC ( q ). This points towards a non-ideal solution behavior of Ni-Cu contrary to common believe but in agreement with measurements of free enthalpy of mixing. The temperature dependence of S CC at small momentum transfer provides evidence of critical compositional fluctuations in Ni 43 Cu 57 melts., (Creative Commons Attribution license.)