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Abstract The artificial intelligence (AI) paradigm shifts from software to implementing general‐purpose or application‐specific hardware systems with lower power requirements. This study explored a material physical reservoir consisting of a material random network, called in‐materio physical reservoir computing (RC) to achieve efficient hardware systems. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions as reservoir nodes, demonstrated the requisite characteristics of an in‐materio physical reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. The power consumption of the in‐materio physical reservoir is 0.07 nW per nanojunctions, confirming its highly efficient information processing system. As a hardware reservoir, the devices successfully performed waveform generation tasks. Finally, a voice classification by an in‐materio physical reservoir is achieved over 80%, comparable to an RC software simulation. In‐materio physical RC with rich nonlinear dynamics has huge potential for next‐generation hardware‐based AI.

To explore a proof-of-concept for atomically precise manufacturing (APM) using scanning probe microscopy (SPM), first principle theoretical calculations of atom-by-atom transfer from the apex of an SPM tip to an individual radical on a surface-bound organic molecule have been performed. Atom transfer is achieved by spatially controlled motion of a gold terminated tip to the radical. Two molecular tools for SPM-based APM have been designed and investigated, each comprising an adamantane core, a radical end group, and trithiol linkers to enable strong chemisorption on the Au(111) surface: ethynyl-adamantane-trithiol and adamantyl-trithiol. We demonstrate the details of controlled Au atom abstraction during tip approach toward and retraction from the radical species. Upon approach of the tip, the apical Au atom undergoes a transfer toward the carbon radical at a clearly defined threshold separation. This atomic displacement is accompanied by a net energy gain of the system in the range −0.5 to −1.5 eV, depending on the radical structure. In the case of a triangular pyramidal apex model, two tip configurations are possible after the tip atom displacement: (1) an Au atom is abstracted from the tip and bound to the C radical, not bound to the tip base anymore, and (2) apical tip atoms rearrange to form a continuous neck between the tip and radical. In the second case, subsequent tip retraction leads to the same final configuration as the first, with the abstracted Au atom bound to radical carbon atom of the molecular tool. For the less reactive adamantyl-trithiol radical molecular tool, Au atom transfer is less energetically favored, but this has the advantage of avoiding other apex gold atoms from rearrangement.

The study of physical and mechanical features of cancer cells, or cancer cell mechanobiology, is a new frontier in cancer research. Such studies may enhance our understanding of the disease process, especially mechanisms associated with cancer cell invasion and metastasis, and may help the effort of developing diagnostic biomarkers and therapeutic drug targets. Cancer cell mechanobiological changes are associated with the complex interplay of activation/inactivation of multiple signaling pathways, which can occur at both the genetic and epigenetic levels, and the interactions with the cancer microenvironment. It has been shown that metastatic tumor cells are more compliant than morphologically similar benign cells in actual human samples. Subsequent studies from us and others further demonstrated that cell mechanical properties are strongly associated with cancer cell invasive and metastatic potential, and thus may serve as a diagnostic marker of detecting cancer cells in human body fluid samples. In this review, we provide a brief narrative of the molecular mechanisms underlying cancer cell mechanobiology, the technological platforms utilized to study cancer cell mechanobiology, the status of cancer cell mechanobiological studies in various cancer types, and the potential clinical applications of cancer cell mechanobiological study in cancer early detection, diagnosis, and treatment.

Hardware‐based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in‐materio RC) platform consisting of a recurrent network formed by the single‐walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/fγ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one‐hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/fγ power law of maximized information processing.

Power laws are of interest to several scientific disciplines because they can provide important information about the underlying dynamics (e.g. scale invariance and self-similarity) of a given system. Because power laws are of increasing interest to the cardiac sciences as potential indicators of cardiac dysfunction, it is essential that rigorous, standardized analytical methods are employed in the evaluation of power laws. This study compares the methods currently used in the fields of condensed matter physics, geoscience, neuroscience, and cardiology in order to provide a robust analytical framework for evaluating power laws in stem cell-derived cardiomyocyte cultures. One potential power law-obeying phenomenon observed in these cultures is pacemaker translocations, or the spatial and temporal instability of the pacemaker region, in a 2D cell culture. Power law analysis of translocation data was performed using increasingly rigorous methods in order to illustrate how differences in analytical robustness can result in misleading power law interpretations. Non-robust methods concluded that pacemaker translocations adhere to a power law while robust methods convincingly demonstrated that they obey a doubly truncated power law. The results of this study highlight the importance of employing comprehensive methods during power law analysis of cardiomyocyte cultures.

Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.

Cancer cell mechanotype changes are newly recognized cancer phenotypic events, whereas metastatic cancer cells show decreased cell stiffness and increased deformability relative to normal cells. To further examine how cell mechanotype changes in early stages of cancer transformation and progression, an in vitro multi-step human urothelial cell carcinogenic model was used to measure cellular Young’s modulus, deformability, and transit time using single-cell atomic force microscopy, microfluidic-based deformability cytometry, and quantitative deformability cytometry, respectively. Measurable cell mechanotype changes of stiffness, deformability, and cell transit time occur early in the transformation process. As cells progress from normal, to preinvasive, to invasive cells, Young’s modulus of stiffness decreases and deformability increases gradually. These changes were confirmed in three-dimensional cultured microtumor masses and urine exfoliated cells directly from patients. Using gene screening and proteomics approaches, we found that the main molecular pathway implicated in cell mechanotype changes appears to be epithelial to mesenchymal transition.

Physical mapping of DNA can be used to detect structural variants and for whole-genome haplotype assembly. Here, the authors use CRISPR-Cas9 and high-speed atomic force microscopy to ‘nanomap’ single molecules of DNA.

Background: Transient receptor potential vanilloid (TRPV) channels act as sensors of pain, temperature, and other external stimuli. We have recently shown that DPV576, an aqueous mixture of nanodiamond (ND) and nanoplatinum (NP), can modulate the activity of TRPV on human primary keratinocytes, suggesting their potential as a possible pain modulator. Here, we sought to examine the effect of DPV576 in modulating the functions of human CD4+ T lymphocytes and whether the modulation is mediated via TRPV channels. Materials and methods: Human primary CD4+ T cells were activated with anti CD3/CD28 with and without DPV576 at 1:10 and 1:100 dilutions for 24 h in vitro. TRPV receptor expression (TRPV1 and TRPV4) on CD4+ T cells were examined by flow cytometry. The capacity of DPV576 to modulate the activity of TRPV1 agonist capsaicin in CD4+ T cells was also determined. Activation of CD4+ T cells was determined by production of cytokines TNF-α, IFN-γ, and IL-10 using specific ELISA kits. Results: DPV576 treatment of CD4+ T cells that were activated with anti CD3/CD28 resulted in decreased expression of the TRPV1 channel, but had no effect on TRPV4. This was accompanied by decreased secretion of IFN-γ and reduced expression of TRPV1 in capsaicin activated CD4+ T cells. In addition, DPV576 inhibited the capsaicin, induced the production of IFN-γ, and enhanced the secretion of IL-10. Conclusion: We conclude that short term exposure to DPV576 inhibits the activity of TRPV1 channels in CD4+ T lymphocytes, which may suggest its possible beneficial use for pain management.

Food safety science is an important field due to its practical applications in maintaining public safety and confidence in consumer goods. A significant component of food safety science is the detection and regulation of heavy metals in food. Heavy metals such as mercury (Hg) are of particular concern because of their potential to damage the nervous system, gastrointestinal tract, and other organ systems in humans and other organisms. The stringent standards and practices for the analysis of Hg in fish, as implemented by institutions such as the U.S. Food and Drug Administration (FDA), require both skilled analytical chemists and sensitive quantitative techniques, e.g., inductively coupled plasma mass spectrometry (ICP-MS). These needs inspired the development of an upper-division undergraduate analytical chemistry experiment that is designed to teach students how to quantify mercury in commercial fish products via ICP-MS analysis. In this hands-on laboratory exercise, students were taught how to use a standard reference material (SRM) for method validation and to understand how different matrices can affect the accuracy of the analysis. Students also learned how to optimize ICP-MS instrument parameters such as the kinetic energy discrimination (KED) voltage. Students worked in small groups and across lab sections to analyze their data and to identify the best parameter set for their experimental conditions. This lab exercise provides a rigorous, practical, and challenging experience for aspiring analytical chemists and can be readily adapted to the needs and interests of any institution with access to an ICP-MS instrument.

Open source analytical software for the analysis of electrophysiological cardiomyocyte data offers a variety of new functionalities to complement closed-source, proprietary tools. Here, we present the Cardio PyMEA application, a free, modifiable, and open source program for the analysis of microelectrode array (MEA) data obtained from cardiomyocyte cultures. Major software capabilities include: beat detection; pacemaker origin estimation; beat amplitude and interval; local activation time, upstroke velocity, and conduction velocity; analysis of cardiomyocyte property-distance relationships; and robust power law analysis of pacemaker spatiotemporal instability. Cardio PyMEA was written entirely in Python 3 to provide an accessible, integrated workflow that possesses a user-friendly graphical user interface (GUI) written in PyQt5 to allow for performant, cross-platform utilization. This application makes use of object-oriented programming (OOP) principles to facilitate the relatively straightforward incorporation of custom functionalities, e.g. power law analysis, that suit the needs of the user. Cardio PyMEA is available as an open source application under the terms of the GNU General Public License (GPL). The source code for Cardio PyMEA can be downloaded from Github at the following repository: https://github.com/csdunhamUC/cardio_pymea.

The heart switches its energy substrate from glucose to fatty acids at birth, and maternal hyperglycemia is associated with congenital heart disease. However, little is known about how blood glucose impacts heart formation. Using a chemically defined human pluripotent stem-cell-derived cardiomyocyte differentiation system, we found that high glucose inhibits the maturation of cardiomyocytes at genetic, structural, metabolic, electrophysiological, and biomechanical levels by promoting nucleotide biosynthesis through the pentose phosphate pathway. Blood glucose level in embryos is stable in utero during normal pregnancy, but glucose uptake by fetal cardiac tissue is drastically reduced in late gestational stages. In a murine model of diabetic pregnancy, fetal hearts showed cardiomyopathy with increased mitotic activity and decreased maturity. These data suggest that high glucose suppresses cardiac maturation, providing a possible mechanistic basis for congenital heart disease in diabetic pregnancy.

Tightly regulated Ca2+ homeostasis is a prerequisite for proper cardiac function. To dissect the regulatory network of cardiac Ca2+ handling, we performed a chemical suppressor screen on zebrafish tremblor embryos, which suffer from Ca2+ extrusion defects. Efsevin was identified based on its potent activity to restore coordinated contractions in tremblor. We show that efsevin binds to VDAC2, potentiates mitochondrial Ca2+ uptake and accelerates the transfer of Ca2+ from intracellular stores into mitochondria. In cardiomyocytes, efsevin restricts the temporal and spatial boundaries of Ca2+ sparks and thereby inhibits Ca2+ overload-induced erratic Ca2+ waves and irregular contractions. We further show that overexpression of VDAC2 recapitulates the suppressive effect of efsevin on tremblor embryos whereas VDAC2 deficiency attenuates efsevin's rescue effect and that VDAC2 functions synergistically with MCU to suppress cardiac fibrillation in tremblor. Together, these findings demonstrate a critical modulatory role for VDAC2-dependent mitochondrial Ca2+ uptake in the regulation of cardiac rhythmicity.

Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime. [ABSTRACT FROM AUTHOR]

Developments in computing hardware are constrained by the operating principles of complementary metal oxide semiconductor (CMOS) technology, fabrication limits of nanometer scaled features, and difficulties in effective utilization of high density interconnects. This set of obstacles has promulgated a search for alternative, energy efficient approaches to computing inspired by natural systems including the mammalian brain. Atomic switch network (ASN) devices are a unique platform specifically developed to overcome these current barriers to realize adaptive neuromorphic technology. ASNs are composed of a massively interconnected network of atomic switches with a density of ∼109 units/cm2 and are structurally reminiscent of the neocortex of the brain. ASNs possess both the intrinsic capabilities of individual memristive switches, such as memory capacity and multi-state switching, and the characteristics of large-scale complex systems, such as power-law dynamics and non-linear transformations of input signals. Here we describe the successful nanoarchitectonic fabrication of next-generation ASN devices using combined top-down and bottom-up processing and experimentally demonstrate their utility as reservoir computing hardware. Leveraging their intrinsic dynamics and transformative input/output (I/O) behavior enabled waveform regression of periodic signals in the absence of embedded algorithms, further supporting the potential utility of ASN technology as a platform for unconventional approaches to computing. [ABSTRACT FROM AUTHOR]

Nanoparticles have recently gained increased attention as drug delivery systems for the treatment of cancer due to their minute size and unique chemical properties. However, very few studies have tested the biophysical changes associated with nanoparticles on metastatic cancer cells at the cellular and sub-cellular scales. Here, we investigated the mechanical and morphological properties of cancer cells by measuring the changes in cell Young’s Modulus using AFM, filopodial retraction (FR) by time lapse optical light microscopy imaging and filopodial disorganization by high resolution AFM imaging of cells upon treatment with nanoparticles. In the current study, nanomechanical changes in live murine metastatic breast cancer cells (4T1) post exposure to a nanodiamond/nanoplatinum mixture dispersed in aqueous solution (DPV576), were monitored. Results showed a decrease in Young’s modulus at two hours post treatment with DPV576 in a dose dependent manner. Partial FR at 20 min and complete FR at 40 min were observed. Moreover, analysis of the retraction distance (in microns) measured over time (minutes), showed that a DPV576 concentration of 15%v/v yielded the highest FR rate. In addition, DPV576 treated cells showed early signs of filopodial disorganization and disintegration. This study demonstrates the changes in cell stiffness and tracks early structural alterations of metastatic breast cancer cells post treatment with DPV576, which may have important implications in the role of nanodiamond/nanoplatinum based cancer cell therapy and sensitization to chemotherapy drugs. [ABSTRACT FROM AUTHOR]

We have used an atomic force microscope to examine a clinically derived sample of single-molecule gene transcripts, in the form of double-stranded cDNA, (c: complementary) obtained from human cardiac muscle without the use of polymerase chain reaction (PCR) amplification. We observed a log-normal distribution of transcript sizes, with most molecules being in the range of 0.4-7.0 kilobase pairs (kb) or 130-2300 nm in contour length, in accordance with the expected distribution of mRNA (m: messenger) sizes in mammalian cells. We observed novel branching structures not previously known to exist in cDNA, and which could have profound negative effects on traditional analysis of cDNA samples through cloning, PCR and DNA sequencing. [ABSTRACT FROM AUTHOR]

Recently biomechanics of cancer cells, in particular stiffness or elasticity, has been identified as an important factor relating to cancer cell function, adherence, motility, transformation and invasion. We report on the nanomechanical responses of metastatic cancer cells and benign mesothelial cells taken from human body cavity fluids using atomic force microscopy. Following our initial study (Cross et al 2007 Nat. Nanotechnol. 2 780-3), we report on the biophysical properties of patient-derived effusion cells and address the influence of cell morphology on measured cell stiffness. Using a cytocentrifugation method, which yields morphologically indistinguishable cells that can be prepared in 1 min and avoids any possible artifacts due to 12 h ex vivo culture, we find that metastatic tumor cells are more than 80% softer than benign cells with a distribution over six times narrower than that of normal cells. Consistent with our previous study, which yielded distinguishable cell populations based on ex vivo growth and morphological characteristics, our results show it is unlikely that morphology alone is sufficient to explain the difference in elastic moduli for these two cell types. Moreover, analysis of non-specific cell adhesion inherent to tumor and normal cells collected from patients show surface adhesion of tumor cells is [?]33% less adhesive compared to that of normal cells. Our findings indicate that biomechanical-based functional analysis may provide an additional platform for cytological evaluation and diagnosis of cancer in the future. [ABSTRACT FROM AUTHOR]

Resistive switching devices have garnered significant consideration for their potential use in nanoelectronics and non-volatile memory applications. Here we investigate the nonlinear current–voltage behavior and resistive switching properties of composite nanoparticle films comprising a large collective of metal–insulator–metal junctions. Silver nanoparticles prepared via the polyol process and coated with an insulating polymer layer of tetraethylene glycol were deposited onto silicon oxide substrates. Activation required a forming step achieved through application of a bias voltage. Once activated, the nanoparticle films exhibited controllable resistive switching between multiple discrete low resistance states that depended on operational parameters including the applied bias voltage, temperature and sweep frequency. The films’ resistance switching behavior is shown here to be the result of nanofilament formation due to formative electromigration effects. Because of their tunable and distinct resistance states, scalability and ease of fabrication, nanoparticle films have a potential place in memory technology as resistive random access memory cells.

Efforts to emulate the formidable information processing capabilities of the brain through neuromorphic engineering have been bolstered by recent progress in the fabrication of nonlinear, nanoscale circuit elements that exhibit synapse-like operational characteristics. However, conventional fabrication techniques are unable to efficiently generate structures with the highly complex interconnectivity found in biological neuronal networks. Here we demonstrate the physical realization of a self-assembled neuromorphic device which implements basic concepts of systems neuroscience through a hardware-based platform comprised of over a billion interconnected atomic-switch inorganic synapses embedded in a complex network of silver nanowires. Observations of network activation and passive harmonic generation demonstrate a collective response to input stimulus in agreement with recent theoretical predictions. Further, emergent behaviors unique to the complex network of atomic switches and akin to brain function are observed, namely spatially distributed memory, recurrent dynamics and the activation of feedforward subnetworks. These devices display the functional characteristics required for implementing unconventional, biologically and neurally inspired computational methodologies in a synthetic experimental system.

One intriguing discovery in modern microbiology is the extensive presence of extracellular DNA (eDNA) within biofilms of various bacterial species. Although several biological functions have been suggested for eDNA, including involvement in biofilm formation, the detailed mechanism of eDNA integration into biofilm architecture is still poorly understood. In the biofilms formed by Myxococcus xanthus, a Gram-negative soil bacterium with complex morphogenesis and social behaviors, DNA was found within both extracted and native extracellular matrices (ECM). Further examination revealed that these eDNA molecules formed well organized structures that were similar in appearance to the organization of exopolysaccharides (EPS) in ECM. Biochemical and image analyses confirmed that eDNA bound to and colocalized with EPS within the ECM of starvation biofilms and fruiting bodies. In addition, ECM containing eDNA exhibited greater physical strength and biological stress resistance compared to DNase I treated ECM. Taken together, these findings demonstrate that DNA interacts with EPS and strengthens biofilm structures in M. xanthus.

While adult heart muscle is the least regenerative of tissues, embryonic cardiomyocytes are proliferative, with embryonic stem (ES) cells providing an endless reservoir. In addition to secreted factors and cell–cell interactions, the extracellular microenvironment has been shown to play an important role in stem cell lineage specification, and understanding how scaffold elasticity influences cardiac differentiation is crucial to cardiac tissue engineering. Though previous studies have analyzed the role of matrix elasticity on the function of differentiated cardiomyocytes, whether it affects the induction of cardiomyocytes from pluripotent stem cells is poorly understood. Here, we examine the role of matrix rigidity on cardiac differentiation using mouse and human ES cells. Culture on polydimethylsiloxane (PDMS) substrates of varied monomer-to-crosslinker ratios revealed that rigid extracellular matrices promote a higher yield of de novo cardiomyocytes from undifferentiated ES cells. Using a genetically modified ES system that allows us to purify differentiated cardiomyocytes by drug selection, we demonstrate that rigid environments induce higher cardiac troponin T expression, beating rate of foci, and expression ratio of adult α- to fetal β- myosin heavy chain in a purified cardiac population. M-mode and mechanical interferometry image analyses demonstrate that these ES-derived cardiomyocytes display functional maturity and synchronization of beating when co-cultured with neonatal cardiomyocytes harvested from a developing embryo. Together, these data identify matrix stiffness as an independent factor that instructs not only the maturation of already differentiated cardiomyocytes but also the induction and proliferation of cardiomyocytes from undifferentiated progenitors. Manipulation of the stiffness will help direct the production of functional cardiomyocytes en masse from stem cells for regenerative medicine purposes.