Distinct lineage-dependent structural and functional organization of the hippocampus.

The hippocampus, as part of the cerebral cortex, is essential for memory formation and spatial navigation. Although it has been extensively studied, especially as a model system for neurophysiology, the cellular processes involved in constructing and organizing the hippocampus remain largely unclear. Here, we show that clonally related excitatory neurons in the developing hippocampus are progressively organized into discrete horizontal, but not vertical, clusters in the stratum pyramidale, as revealed by both cell-type-specific retroviral labeling and mosaic analysis with double markers (MADM). Moreover, distinct from those in the neocortex, sister excitatory neurons in the cornu ammonis 1 region of the hippocampus rarely develop electrical or chemical synapses with each other. Instead, they preferentially receive common synaptic input from nearby fast-spiking (FS), but not non-FS, interneurons and exhibit synchronous synaptic activity. These results suggest that shared inhibitory input may specify horizontally clustered sister excitatory neurons as functional units in the hippocampus.

Direct pumping of polar fluids with traveling-wave dielectrophoresis.

Efficiently pumping fluids without moving parts in extremely miniaturized formats is challenging. Here, we propose and numerically explore a new type of fluid pump in which a series of electrodes driven at different phases produce a force directly on the molecules of the fluid. This effect is based on traveling-wave dielectrophoresis (twDEP), which has been observed to drive the motion of colloidal particles. Here, we leverage the time needed for fluid molecules with permanent dipoles to align with the applied field to maintain a phase lag between the applied field and the molecular polarization. While requiring operation in the GHz range, this effect is predicted to be efficient due to its ability to directly drive bulk fluid motion. We begin by establishing the foundational equations for this effect and performing finite element simulations to determine its magnitude in a model geometry. By combining theory and a systematic series of calculations, we validate that twDEP pumps should exhibit a fluid flow that scales as the voltage squared divided by the electrode period and that it should increase with the complex permittivity of the fluid and decrease with increasing viscosity. This results in a general equation that predicts the performance of twDEP pumps. Collectively, these computations provide a blueprint for producing twDEP pumps of polar fluids such as water and ethanol. We conclude by noting that the growing interest in high power microwave technology along with metasurfaces to locally tailor phase could provide a path to realizing twDEP pumps in practice.

A framework for materials informatics education through workshops.

Abstract: The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. In this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.

Quantitative nanopatterning of fg-scale liquids with dip-pen nanolithography.

The ability to precisely pattern nanoscale amounts of liquids is essential for biotechnology and high-throughput chemistry, but controlling fluid flow on these scales is very challenging. Scanning probe lithography methods such as dip-pen nanolithography (DPN) provide a mechanism to write fluids at the nanoscale, but this is an open loop process as methods to provide feedback while patterning sub-pg features have yet to be reported. Here, we demonstrate a novel method for programmably nanopatterning liquid features at the fg-scale through a combination of ultrafast atomic force microscopy probes, the use of spherical tips, and inertial mass sensing. We begin by investigating the required probe properties that would provide sufficient mass responsivity to detect fg-scale mass changes and find ultrafast probes to be capable of this resolution. Further, we attach a spherical bead to the tip of an ultrafast probe as we hypothesize that the spherical tip could hold a drop at its apex which both facilitates interpretation of inertial sensing and maintains a consistent fluid environment for reliable patterning. We experimentally find that sphere-tipped ultrafast probes are capable of reliably patterning hundreds of features in a single experiment. Analyzing the changes in the vibrational resonance frequency during the patterning process, we find that drift in the resonance frequency complicates analysis, but that it can be removed through a systematic correction. Subsequently, we quantitatively study patterning using sphere-tipped ultrafast probes as a function of retraction speed and dwell time to find that the mass of fluid transferred can be modulated by greater than an order of magnitude and that liquid features as small as 6 fg can be patterned and resolved. Taken together, this work addresses a persistent concern in DPN by enabling quantitative feedback for nanopatterning of aL-scale features and lays the foundation for programmably nanopatterning fluids.

Electric field induced macroscopic cellular phase of nanoparticles.

A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase that is composed of particle-rich walls and particle-free voids under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely (1) a system that is considered two dimensional and (2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

Catalyst discovery through megalibraries of nanomaterials.

The nanomaterial landscape is so vast that a high-throughput combinatorial approach is required to understand structure-function relationships. To address this challenge, an approach for the synthesis and screening of megalibraries of unique nanoscale features (>10,000,000) with tailorable location, size, and composition has been developed. Polymer pen lithography, a parallel lithographic technique, is combined with an ink spray-coating method to create pen arrays, where each pen has a different but deliberately chosen quantity and composition of ink. With this technique, gradients of Au-Cu bimetallic nanoparticles have been synthesized and then screened for activity by in situ Raman spectroscopy with respect to single-walled carbon nanotube (SWNT) growth. Au3Cu, a composition not previously known to catalyze SWNT growth, has been identified as the most active composition.

Patient perspectives and involvement in precision medicine research.

The Kidney Precision Medicine Project will advance understanding of chronic kidney disease attributed to diabetes or hypertension and acute kidney injury through a protocol kidney biopsy used for deep phenotyping with state-of-the-art methodology. To guide scientific inquiry toward clinically meaningful benefit, patients are equal partners for priority setting, study design and conduct, and dissemination of findings. Patients from stakeholder organizations, recruitment sites, tissue interrogation sites, and the Central Hub are represented on the Community Engagement Committee. This unique collaboration between patients and scientists has set a new standard for inclusion in precision medicine research.

Rationale and design of the Kidney Precision Medicine Project.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

Theory for hierarchical assembly with dielectrophoresis and the role of particle anisotropy.

Nonuniform electric fields cause polarizable particles to move through an effect known as dielectrophoresis (DEP). Additionally, the particles themselves create nonuniform fields due to their induced dipoles. When the nonuniform field of one particle causes another to move, it represents a path to hierarchical assembly termed mutual DEP (mDEP). Anisotropic particles potentially provide further opportunities for assembly through intense and intricate local field profiles. Here, we construct a theoretical framework for describing anisotropic particles as templates for assembly through mDEP by considering the motion of small nanoparticles near larger anisotropic nanoparticles. Using finite element analysis, we study eight particle shapes and compute their field enhancement and polarizability in an orientation-specific manner. Strikingly, we find a more than tenfold enhancement in the field near certain particle shapes, potentially promoting mDEP. To more directly relate the field intensity to the anticipated assembly outcome, we compute the volume experiencing each field enhancement versus particle shape and orientation. Finally, we provide a framework for predicting how mixtures of two distinct particle species will begin to assemble in a manner that allows for the identification of conditions that kinetically bias assembly toward specific hierarchical outcomes.

Reinforcing Magnetorheological Fluids with Highly Anisotropic 2D Materials.

Magnetorheological fluids (MRF) are suspensions of magnetic particles that solidify in the presence of a magnetic field. While non-magnetic additives could improve MRF performance, explorations into such additives have not coalesced into an understanding of their influence, and particularly the role of additive morphology. Here, we explore α-Ni(OH)2 2D sheets, with aspect ratios of ∼25,000, as highly anisotropic MRF additives. Experiments studying pressure-driven flow of an MRF with and without these sheets show that their addition can increase the saturation pressure by as much as 46 %. However, shear-mode rheology reveals that they can also weaken the MRF by inhibiting the chaining of the iron particles at low field strengths and have no effect at higher field strengths. In order to reconcile the strikingly different results, we propose that 2D materials introduce a non-Newtonian handle to modify smart fluids in a manner that depends on the curvature of the shearing strain rate profile. Specifically, we identify a modification to the Buckingham-Reiner model of pressure-driven flow for a Bingham plastic in which the sheets widen the solidified plug. This work highlights the subtle interaction between particles in smart fluids and flows while emphasizing the opportunity for using anisotropy to tune this interaction.

Reinforcing Magnetorheological Fluids with Highly Anisotropic 2D Materials.

The front cover artwork is provided by the group of Professor Keith Brown at Boston University. The image shows the magnetorheological fluid in a pressure-driven flow and highlights the length scales of the magnetic particles and highly anisotropic 2D sheets. Read the full text of the Article at 10.1002/cphc.202000948.

Physiologically Relevant Mechanics of Biodegradable Polyester Nanoparticles.

Despite the extensive use of biodegradable polyester nanoparticles for drug delivery, and reports of the strong influence of nanoparticle mechanics on nano-bio interactions, there is a lack of systematic studies on the mechanics of these nanoparticles under physiologically relevant conditions. Here, we report indentation experiments on poly(lactic acid) and poly(lactide-co-glycolide) nanoparticles using atomic force microscopy. While dried nanoparticles were found to be rigid at room temperature, their elastic modulus was found to decrease by as much as 30 fold under simulated physiological conditions (i.e., in water at 37 °C). Differential scanning calorimetry confirms that this softening can be attributed to the glass transition of the nanoparticles. Using a combination of mechanical and thermoanalytical characterization, the plasticizing effects of miniaturization, molecular weight, and immersion in water were investigated. Collectively, these experiments provide insight for experimentalists exploring the relationship between polymer nanoparticle mechanics and in vivo behavior.

Machine Learning in Nanoscience: Big Data at Small Scales.

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML's advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this Mini Review, we highlight some recent efforts to connect the ML and nanoscience communities by focusing on three types of interaction: (1) using ML to analyze and extract new insights from large nanoscience data sets, (2) applying ML to accelerate material discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers.

Measuring Nanoparticle Polarizability Using Fluorescence Microscopy.

Using a novel method developed to quantify the polarizability of photoluminescent nanoparticles in water, we present experimental observations of the extraordinary polarizability exhibited by nanoparticles of commensurate size with the Debye screening length, confirming previously reported theory. Semiconductor quantum dots (QDs) are ideal model nanoparticles to demonstrate this assay, due to their tunable size and bright photoluminescence. This assay is based upon microfluidic chambers with microelectrodes that generate trapping potentials that are weaker than thermal energy. By comparing the local electric field strength and variations in QD concentration, their polarizability was computed and found to agree with estimates based upon the hydrodynamic diameter found using light scattering. Strikingly, the polarizability of the nanoparticles increased 30-fold in low salt conditions compared to high salt conditions due to the increased thickness of the Debye layer relative to the particle radius. In addition to providing evidence that corroborates theoretical work studying direct solutions to the Poisson-Nernst-Planck equations, these observations provide an explanation for the previously observed conductivity dependence of biomolecule polarizability. As the polarizability of nanoparticles is of high importance to the electrically directed assembly of particles, as well as their interactions with other materials in complex environments, we anticipate that these results will be highly relevant to ongoing efforts in materials by design and nanomedicine.

Design and Realization of 3D Printed AFM Probes.

Atomic force microscope (AFM) probes and AFM imaging by extension are the product of exceptionally refined silicon micromachining, but are also restricted by the limitations of these fabrication techniques. Here, the nanoscale additive manufacturing technique direct laser writing is explored as a method to print monolithic cantilevered probes for AFM. Not only are 3D printed probes found to function effectively for AFM, but they also confer several advantages, most notably the ability to image in intermittent contact mode with a bandwidth approximately ten times larger than analogous silicon probes. In addition, the arbitrary structural control afforded by 3D printing is found to enable programming the modal structure of the probe, a capability that can be useful in the context of resonantly amplifying nonlinear tip-sample interactions. Collectively, these results show that 3D printed probes complement those produced using conventional silicon micromachining and open the door to new imaging techniques.

Atlantic butterflies: a data set of fruit-feeding butterfly communities from the Atlantic forests.

Butterflies are one of the best-known insect groups, and they have been the subject of numerous studies in ecology and evolution, especially in the tropics. Much attention has been given to the fruit-feeding butterfly guild in biodiversity conservation studies, due to the relative ease with which taxa may be identified and specimens sampled using bait traps. However, there remain many uncertainties about the macroecological and biogeographical patterns of butterflies in tropical ecosystems. In the present study, we gathered information about fruit-feeding butterfly species in local communities from the Atlantic Forests of South America. The ATLANTIC BUTTERFLIES data set, which is part of ATLANTIC SERIES data papers, results from a compilation of 145 unpublished inventories and 64 other references, including articles, theses, and book chapters published from 1949 to 2018. In total, the data set contains 7,062 records (presence) of 279 species of fruit-feeding butterflies identified with taxonomic certainty, from 122 study locations. The Satyrini is the tribe with highest number of species (45%) and records (30%), followed by Brassolini, with 13% of species and 12.5% of records. The 10 most common species correspond to 14.2% of all records. This data set represents a major effort to compile inventories of fruit-feeding butterfly communities, filling a knowledge gap about the diversity and distribution of these butterflies in the Atlantic Forest. We hope that the present data set can provide guidelines for future studies and planning of new inventories of fruit-feeding butterflies in this biome. The information presented here also has potential use in studies across a great variety of spatial scales, from local and landscape levels to macroecological research and biogeographical research. We expect that such studies be very important for the better implementation of conservation initiatives, and for understanding the multiple ecological processes that involve fruit-feeding butterflies as biological indicators. No copyright restrictions apply to the use of this data set. Please cite this Data paper when using the current data in publications or teaching events.

Preferential electrical coupling regulates neocortical lineage-dependent microcircuit assembly.

Radial glial cells are the primary neural progenitor cells in the developing neocortex. Consecutive asymmetric divisions of individual radial glial progenitor cells produce a number of sister excitatory neurons that migrate along the elongated radial glial fibre, resulting in the formation of ontogenetic columns. Moreover, sister excitatory neurons in ontogenetic columns preferentially develop specific chemical synapses with each other rather than with nearby non-siblings. Although these findings provide crucial insight into the emergence of functional columns in the neocortex, little is known about the basis of this lineage-dependent assembly of excitatory neuron microcircuits at single-cell resolution. Here we show that transient electrical coupling between radially aligned sister excitatory neurons regulates the subsequent formation of specific chemical synapses in the neocortex. Multiple-electrode whole-cell recordings showed that sister excitatory neurons preferentially form strong electrical coupling with each other rather than with adjacent non-sister excitatory neurons during early postnatal stages. This preferential coupling allows selective electrical communication between sister excitatory neurons, promoting their action potential generation and synchronous firing. Interestingly, although this electrical communication largely disappears before the appearance of chemical synapses, blockade of the electrical communication impairs the subsequent formation of specific chemical synapses between sister excitatory neurons in ontogenetic columns. These results suggest a strong link between lineage-dependent transient electrical coupling and the assembly of precise excitatory neuron microcircuits in the neocortex.

Genome-wide DNA methylation analysis identifies MEGF10 as a novel epigenetically repressed candidate tumor suppressor gene in neuroblastoma.

Neuroblastoma is a childhood cancer in which many children still have poor outcomes, emphasising the need to better understand its pathogenesis. Despite recent genome-wide mutation analyses, many primary neuroblastomas do not contain recognizable driver mutations, implicating alternate molecular pathologies such as epigenetic alterations. To discover genes that become epigenetically deregulated during neuroblastoma tumorigenesis, we took the novel approach of comparing neuroblastomas to neural crest precursor cells, using genome-wide DNA methylation analysis. We identified 93 genes that were significantly differentially methylated of which 26 (28%) were hypermethylated and 67 (72%) were hypomethylated. Concentrating on hypermethylated genes to identify candidate tumor suppressor loci, we found the cell engulfment and adhesion factor gene MEGF10 to be epigenetically repressed by DNA hypermethylation or by H3K27/K9 methylation in neuroblastoma cell lines. MEGF10 showed significantly down-regulated expression in neuroblastoma tumor samples; furthermore patients with the lowest-expressing tumors had reduced relapse-free survival. Our functional studies showed that knock-down of MEGF10 expression in neuroblastoma cell lines promoted cell growth, consistent with MEGF10 acting as a clinically relevant, epigenetically deregulated neuroblastoma tumor suppressor gene. © 2016 The Authors. Molecular Carcinogenesis Published by Wiley Periodicals, Inc.

p53 protein accumulation predicts malignant progression in Barrett's metaplasia: a prospective study of 275 patients.

AIMS: The purpose of this study was to determine prospectively whether p53 protein accumulation in biopsies of Barrett's metaplasia (BM) is a predictor of malignant progression, without relying on dysplasia grading. METHODS AND RESULTS: Sections of formalin-fixed paraffin-embedded tissue from the initial biopsies of 275 patients with BM, who had no high-grade dysplasia (HGD) or oesophageal adenocarcinoma (EAC), were stained for p53 by immunohistochemistry. The mean follow-up was 41 months. p53-positive biopsies were divided into four groups: scattered positive cells, multifocal scattered positive cells, aggregates of positive cells, and multifocal aggregates of positive cells. Kaplan-Meier analysis with the log-rank test was used to determine the rate of progression to HGD/EAC. Of the 275 patients, 227 had initial biopsies that were completely negative for p53, and, of these, one (0.4%) progressed to HGD/EAC; none of 24 (0%) patients with scattered positive cells and none of four (0%) of patients with multifocal scattered positive cells progressed. In contrast, five of 16 (31.25%) patients with aggregates of positive cells and three of four (75%) of those with multifocal aggregates of positive cells progressed to HGD/EAC. Kaplan-Meier analysis with log-rank statistics showed the difference in progression rate between the five groups to be highly significant (P < 0.0001). CONCLUSIONS: We conclude that p53 protein accumulation, detected by immunohistochemistry in aggregates of cells, is a significant predictor of malignant progression in patients with BM.

Quantifying Liquid Transport and Patterning Using Atomic Force Microscopy.

Atomic force microscopy (AFM) provides unique insight into the nanoscale properties of materials. It has been challenging, however, to use AFM to study soft materials such as liquids or gels because of their tendency to flow in response to stress. We propose an AFM-based technique for quantitatively analyzing the transport of soft materials from an AFM probe to a surface. Specifically, we present a method for loading an AFM probe with a single 0.3 to 30 pL droplet of liquid and subsequently measuring the mass of this liquid by observing the change in the vibrational resonance frequency of the cantilever. Using this approach, the mass of this liquid was detected with picogram-scale precision by a commercial AFM system. Additionally, sub-femtoliter droplets of liquid were transferred from the probe to a surface with agreement found between the real-time change in mass of the liquid-loaded probe and the volume of the feature written on the surface. To demonstrate the utility of this approach in studying nanoscale capillary and transport phenomena, we experimentally determine that the quantity of liquid transported from the tip to a surface in a given patterning operation scales as the mass of liquid on the probe to the 1.35 power. In addition to providing new avenues for studying the dynamics of soft materials on the nanoscale, this method can improve nanopatterning of soft materials by providing in situ feedback.