This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Machine learning with Python

Droplet microfluidics generates and manipulates discrete droplets through immiscible multiphase flows inside microchannels Due to its remarkable advantages, droplet microfluidics bears significant value in an extremely wide range of area In this review, we provide a comprehensive and in-depth insight into droplet microfluidics, covering fundamental research from microfluidic chip fabrication and droplet generation to the applications of droplets in bio(chemical) analysis and materials generation The purpose of this review is to convey the fundamentals of droplet microfluidics, a critical analysis on its current status and challenges, and opinions on its future development We believe this review will promote communications among biology, chemistry, physics, and materials science

Emerging Droplet Microfluidics

Embryos allocate cells to the three germ layers in a spatially ordered sequence. Human embryonic stem cells (hESCs) can generate the three germ layers in culture; however, differentiation is typically heterogeneous and spatially disordered. We show that geometric confinement is sufficient to trigger self-organized patterning in hESCs. In response to BMP4, colonies reproducibly differentiated to an outer trophectoderm-like ring, an inner ectodermal circle and a ring of mesendoderm expressing primitive-streak markers in between. Fates were defined relative to the boundary with a fixed length scale: small colonies corresponded to the outer layers of larger ones. Inhibitory signals limited the range of BMP4 signaling to the colony edge and induced a gradient of Activin-Nodal signaling that patterned mesendodermal fates. These results demonstrate that the intrinsic tendency of stem cells to make patterns can be harnessed by controlling colony geometries and provide a quantitative assay for studying paracrine signaling in early development.

A method to recapitulate early embryonic spatial patterning in human embryonic stem cells

Follow up what we will offer in this article about philosophical transactions of the royal society of london series b biological sciences no 600 vol 233 studies of the post glacial history of british vegetation x correlation between climate forest composition prehistoric agriculture and peat st. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

Philosophical Transactions of the Royal Society of London. for the Year Mdcccxxvii. Part I. Volume Cxvii:355–388, 1827

This article presents a comprehensive review of numerical methods and models for interface resolving simulations of multiphase flows in microfluidics and micro process engineering. The focus of the paper is on continuum methods where it covers the three common approaches in the sharp interface limit, namely the volume-of-fluid method with interface reconstruction, the level set method and the front tracking method, as well as methods with finite interface thickness such as color-function based methods and the phase-field method. Variants of the mesoscopic lattice Boltzmann method for two-fluid flows are also discussed, as well as various hybrid approaches. The mathematical foundation of each method is given and its specific advantages and limitations are highlighted. For continuum methods, the coupling of the interface evolution equation with the single-field Navier–Stokes equations and related issues are discussed. Methods and models for surface tension forces, contact lines, heat and mass transfer and phase change are presented. In the second part of this article applications of the methods in microfluidics and micro process engineering are reviewed, including flow hydrodynamics (separated and segmented flow, bubble and drop formation, breakup and coalescence), heat and mass transfer (with and without chemical reactions), mixing and dispersion, Marangoni flows and surfactants, and boiling.

Numerical modeling of multiphase flows in microfluidics and micro process engineering: a review of methods and applications

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors, and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based encoder significantly outperforms a baseline model trained on Morgan fingerprints and a selection of encoders based on SMILES, as well as the previously reported state-of-the-art for multimodal drug sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify to its potential for in silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.

Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders.

Local (cell-level) signaling environments, regulated by autocrine and paracrine signaling, and modulated by cell organization, are hypothesized to be fundamental stem cell fate control mechanisms used during development. It has, however, been challenging to demonstrate the impact of cell-level organization on stem cell fate control and to relate stem cell fate outcomes to autocrine and paracrine signaling. We address this fundamental problem using a combined in silico and experimental approach in which we directly manipulate, using laminar fluid flow, the local impact of endogenously secreted gp130-activating ligands and their activation of signal transducer and activator of transcription3 (STAT3) signaling in mouse embryonic stem cells (mESC). Our model analysis predicted that flow-dependent changes in autocrine and paracrine ligand binding would impact heterogeneity in cell- and colony-level STAT3 signaling activation and cause a gradient of cell fate determination along the direction of flow. Interestingly, analysis also predicted that local cell density would be inversely proportional to the degree to which endogenous secretion contributed to cell fate determination. Experimental validation using functional activation of STAT3 by secreted factors under microfluidic perfusion culture demonstrated that STAT3 activation and consequently mESC fate were manipulable by flow rate, position in the flow field, and local cell organization. As a unique demonstration of how quantitative control of autocrine and paracrine signaling can be integrated with spatial organization to elicit higher order cell fate effects, this work provides a general template to investigate organizing principles due to secreted factors.

https://www.pnas.org/content/pnas/109/9/3264.full.pdf

Predictive microfluidic control of regulatory ligand trajectories in individual pluripotent cells

Hydrodynamic phenomena are ubiquitous in living organisms and can be used to manipulate cells or emulate physiological microenvironments experienced in vivo Hydrodynamic effects influence multiple cellular properties and processes, including cell morphology, intracellular processes, cell–cell signaling cascades and reaction kinetics, and play an important role at the single-cell, multicellular, and organ level Selected hydrodynamic effects can also be leveraged to control mechanical stresses, analyte transport, as well as local temperature within cellular microenvironments With a better understanding of fluid mechanics at the micrometer-length scale and the advent of microfluidic technologies, a new generation of experimental tools that provide control over cellular microenvironments and emulate physiological conditions with exquisite accuracy is now emerging Accordingly, we believe that it is timely to assess the concepts underlying hydrodynamic control of cellular microenvironments and their applica

Hydrodynamics in Cell Studies.

The controlled self-assembly of polymer-stabilized quantum dots (QDs) into mesoscale aqueous spherical assemblies termed quantum dot compound micelles (QDCMs) using a two-phase gas-segmented microfluidic reactor is described. Self-assembly is initiated by the fast mixing of water (∼1 s) with a blend solution of polystyrene-coated QDs and amphiphilic polystyrene-block-poly(acrylic acid) stabilizing chains via chaotic advection within liquid plugs moving through a sinusoidal channel. Subsequent recirculating flow within a post-formation channel subjects the dynamic QDCMs to shear-induced processing, controlled via the flow rate and channel length, before a final quench into pure water. During processing, larger QDCMs within the initial population undergo breakup into smaller particles, resulting in smaller mean particle sizes, smaller relative standard deviations, and more skewed distribution shapes, as the overall shear exposure is increased. For these cases, shear-induced size reduction is sufficient to d...

Formation and Shear-Induced Processing of Quantum Dot Colloidal Assemblies in a Multiphase Microfluidic Chip

The controlled self-assembly of large compound quantum dot micelles (QDCMs), consisting of constituents of polymer-stabilized quantum dots (QDs) and amphiphilic polystyrene-b-poly(acrylic acid) sta...

Ali Oskooei

Papers

Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders.

Predictive microfluidic control of regulatory ligand trajectories in individual pluripotent cells

Hydrodynamics in Cell Studies.

Formation and Shear-Induced Processing of Quantum Dot Colloidal Assemblies in a Multiphase Microfluidic Chip

Controlled self-assembly of quantum dot-block copolymer colloids in multiphase microfluidic reactors.