Diastereoselective Indole-Dearomative Cope Rearrangements by Compounding Minor Driving Forces.

Reported herein is the discovery of a diastereoselective indole-dearomative Cope rearrangement. A suite of minor driving forces (substrate destabilizing effects; product stabilizing effects) are what promote this otherwise unfavorable dearomatization reaction. These include the following that work in concert to overcome the penalty for dearomatization: (i.) steric congestion in the starting material, (ii.) alkylidene malononitrile and stilbene conjugation events in the product, and (iii.) an unexpected intramolecular p–p* stack on the product side of the equilibrium. The key substrates are rapidly assembled from alkylidenemalononitriles and indole-phenylmethanol derivatives resulting in many successful examples (high yields and diastereoselectivity). The products are structurally complex bearing vicinal stereocenters generated by the dearomative Cope rearrangement. They also contain a variety of functional groups for interconversion to complex architectures. On this line, also described herein are proof-of-concept strategies for achieving enantioselectivity and conversion of the dearomative products to valuable and functionalized small drug-like molecules.

Methodology for the Assessment of Imprecise Multi-State System Availability

Most existing studies of a system’s availability in the presence of epistemic uncertainties assume that the system is binary. In this paper, a new methodology for the estimation of the availability of multi-state systems is developed, taking into consideration epistemic uncertainties. This paper formulates a combined approach, based on continuous Markov chains and interval contraction methods, to address the problem of computing the availability of multi-state systems with imprecise failure and repair rates. The interval constraint propagation method, which we refer to as the forward–backward propagation (FBP) contraction method, allows us to contract the probability intervals, keeping all the values that may be consistent with the set of constraints. This methodology is guaranteed, and several numerical examples of systems with complex architectures are studied.

Size-Dependent Properties of Solution-Processable Conductive MOF Nanocrystals

The diverse optical, magnetic, and electronic behaviors of most colloidal semiconductor nanocrystals emerge from materials with limited structural and elemental compositions. Conductive metal-organic frameworks (MOFs) possess rich compositions with complex architectures, but remain unexplored as nanocrystals, hindering their incorporation into scalable devices. Here, we report the controllable synthesis of conductive MOF nanoparticles based on Fe(1,2,3-trizolate)2. Sizes can be tuned as small as 5.5 nm, ensuring indefinite colloidal stability. These solution-processable MOFs can be analyzed by solution-state spectroscopy and electrochemistry and cast into conductive thin films with excellent uniformity. This unprecedented analysis of MOF materials reveals a strong size-dependence in optical and electronic behavior sensitive to the intrinsic porosity and guest-host interactions of MOFs. These results provide a radical departure from typical MOF characterization, enabling insight into physical properties otherwise impossible with bulk analogs, while offering a roadmap for the future of MOF nanoparticle synthesis and device fabrication.

Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation

Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve’s structure is needed to avoid adverse effects; thereby, ultrasound images are used as an aid approach. In addition, image-based automatic nerve segmentation from deep learning methods has been proposed to mitigate attenuation and speckle noise ultrasonography issues. Notwithstanding, complex architectures highlight the region of interest lacking suitable data interpretability concerning the learned features from raw instances. Here, a kernel-based deep learning enhancement is introduced for nerve structure segmentation. In a nutshell, a random Fourier features-based approach was utilized to complement three well-known semantic segmentation architectures, e.g., fully convolutional network, U-net, and ResUnet. Moreover, two ultrasound image datasets for PNB were tested. Obtained results show that our kernel-based approach provides a better generalization capability from image segmentation-based assessments on different nerve structures. Further, for data interpretability, a semantic segmentation extension of the GradCam++ for class-activation mapping was used to reveal relevant learned features separating between nerve and background. Thus, our proposal favors both straightforward (shallow) and complex architectures (deeper neural networks).

DNA self-organization controls valence in programmable colloid design

Just like atoms combine into molecules, colloids can self-organize into predetermined structures according to a set of design principles. Controlling valence—the number of interparticle bonds—is a prerequisite for the assembly of complex architectures. The assembly can be directed via solid “patchy” particles with prescribed geometries to make, for example, a colloidal diamond. We demonstrate here that the nanoscale ordering of individual molecular linkers can combine to program the structure of microscale assemblies. Specifically, we experimentally show that covering initially isotropic microdroplets with N mobile DNA linkers results in spontaneous and reversible self-organization of the DNA into Z(N) binding patches, selecting a predictable valence. We understand this valence thermodynamically, deriving a free energy functional for droplet–droplet adhesion that accurately predicts the equilibrium size of and molecular organization within patches, as well as the observed valence transitions with N. Thus, microscopic self-organization can be programmed by choosing the molecular properties and concentration of binders. These results are widely applicable to the assembly of any particle with mobile linkers, such as functionalized liposomes or protein interactions in cell–cell adhesion.

Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization.

Deep learning algorithms for radiation therapy treatment planning automation require large patient datasets and complex architectures that often take hundreds of hours to train. Some of these algorithms require constant dose updating (such as with reinforcement learning) and may take days. When these algorithms rely on commerical treatment planning systems to perform dose calculations, the data pipeline becomes the bottleneck of the entire algorithm’s efficiency. Further, uniformly accurate distributions are not always needed for the training and approximations can be introduced to speed up the process without affecting the outcome. These approximations not only speed up the calculation process, but allow for custom algorithms to be written specifically for the purposes of use in AI/ML applications where the dose and fluence must be calculated a multitude of times for a multitude of different situations. Here we present and investigate the effect of introducing matrix sparsity through kernel truncation on the dose calculation for the purposes of fluence optimzation within these AI/ML algorithms. The basis for this algorithm relies on voxel discrimination in which numerous voxels are pruned from the computationally expensive part of the calculation. This results in a significant reduction in computation time and storage. Comparing our dose calculation against calculations in both a water phantom and patient anatomy in Eclipse without heterogenity corrections produced gamma index passing rates around 99% for individual and composite beams with uniform fluence and around 98% for beams with a modulated fluence. The resulting sparsity introduces a reduction in computational time and space proportional to the square of the sparsity tolerance with a potential decrease in cost greater than 10 times that of a dense calculation allowing not only for faster caluclations but for calculations that a dense algorithm could not perform on the same system.

How cells tell up from down and stick together to construct multicellular tissues – interplay between apicobasal polarity and cell–cell adhesion

ABSTRACT Polarized epithelia define a topological inside and outside, and hence constitute a key evolutionary innovation that enabled the construction of complex multicellular animal life. Over time, this basic function has been elaborated upon to yield the complex architectures of many of the organs that make up the human body. The two processes necessary to yield a polarized epithelium, namely regulated adhesion between cells and the definition of the apicobasal (top–bottom) axis, have likewise undergone extensive evolutionary elaboration, resulting in multiple sophisticated protein complexes that contribute to both functions. Understanding how these components function in combination to yield the basic architecture of a polarized cell–cell junction remains a major challenge. In this Review, we introduce the main components of apicobasal polarity and cell–cell adhesion complexes, and outline what is known about their regulation and assembly in epithelia. In addition, we highlight studies that investigate the interdependence between these two networks. We conclude with an overview of strategies to address the largest and arguably most fundamental unresolved question in the field, namely how a polarized junction arises as the sum of its molecular parts.

Intramolecular, Visible Light-Mediated Aza Paternò-Büchi Reactions of Unactivated Alkenes

Azetidines are of particular interest in medicinal chemistry for their favorable properties, including increased resistance to oxidative metabolism and lower lipophilicity. The recent development of [2+2] reactions has significantly benefitted the previously limited methods for azetidine synthesis, but access to more complex architectures still requires further development. Herein we report a visible-light enabled intramolecular [2+2] cycloaddition to access tricyclic azetidines with 3D complex structures and high levels of saturation.

Biomimetic Vasculatures by 3D-Printed Porous Molds

Anatomically and biologically relevant vascular models are critical to progress our understanding of cardiovascular diseases (CVDs) that can lead to effective therapies. Despite advances in 3D bioprinting, recapitulating complex architectures (i.e., freestanding, branching, multilayered, perfusable) of a cell-laden vascular construct remains technically challenging, and the development of new techniques that can recapitulate both anatomical and biological features of blood vessels is of paramount importance. In this work, we introduce a unique, microfluidics-enabled molding technique that allows us to fabricate anatomically-relevant, cell-laden hydrogel vascular models. Our approach employed 3D-printed porous molds of poly(ethylene glycol) diacrylate (PEGDA) as templates to cast alginate-containing bioinks. Due to the porous and aqueous nature of the PEGDA mold, the calcium ion (Ca2+) was diffusively released to crosslink the bioinks to create hollow structures. Applying this technique, multiscale, multilayered vascular constructs that were freestanding and perfusable were readily fabricated using cell-compatible bioinks (i.e., alginate and gelatin methacryloyl (GelMA)). The bioinks were also readily customizable to either improve the compatibility with specific vascular cells or tune the mechanical modulus to mimic native blood vessels. Importantly, we successfully integrated smooth muscle cells and endothelial cells in a biomimetic organization within our vessel constructs and demonstrated a significant increase in monocyte adhesion upon stimulation with an inflammatory cytokine, tumor necrosis factor-alpha (TNF-α). We also demonstrated that the fabricated vessels were amenable for testing percutaneous coronary interventions (i.e., drug-eluting balloons and stents) under physiologically-relevant mechanical states, such as vessel stretching and bending. Overall, we introduce a versatile fabrication technique with multi-faceted possibilities of generating biomimetic vascular models that can benefit future research in mechanistic understanding of CVD progression and the development of therapeutic interventions.