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Journal of the American Chemical Society Nov 2023Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous... (Review)
Review
Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous biomolecular structures as information-storing and -processing substrates to tackle computational problems. It is of great significance to investigate the various issues of programmable biomolecule-mediated processors that are capable of automatically processing, storing, and displaying information. This Perspective provides several conceptual designs of programmable biomolecule-mediated processors and provides some insights into potential future research directions for programmable biomolecule-mediated processors.
PubMed: 37864571
DOI: 10.1021/jacs.3c04142 -
Current Opinion in Biotechnology Dec 2023A wide variety of wasted or underutilized organic feedstocks can be leveraged to build a sustainable bioeconomy, ranging from crop residues to food processor residues... (Review)
Review
A wide variety of wasted or underutilized organic feedstocks can be leveraged to build a sustainable bioeconomy, ranging from crop residues to food processor residues and municipal wastes. Leveraging these feedstocks is both high-risk and high-reward. Converting mixed, variable, and/or highly contaminated feedstocks can pose engineering and economic challenges. However, converting these materials to fuels and chemicals can divert waste from landfills, reduce fugitive methane emissions, and enable more responsible forest management to reduce the frequency and severity of wildfires. Historically, low-value components, including ash and lignin, are poised to become valuable coproducts capable of supplementing cement and valuable chemicals. Here, we evaluate the challenges and opportunities associated with converting a range of feedstocks to renewable fuels and chemicals.
Topics: Methane; Recycling; Waste Management; Renewable Energy
PubMed: 37935087
DOI: 10.1016/j.copbio.2023.103017 -
Nature Communications Feb 2024Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision,... (Review)
Review
Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.
PubMed: 38378715
DOI: 10.1038/s41467-024-45982-w -
Nature Communications Nov 2023The Rydberg blockade is a key ingredient for entangling atoms in arrays. However, it requires atoms to be spaced well within the blockade radius, which limits the range...
The Rydberg blockade is a key ingredient for entangling atoms in arrays. However, it requires atoms to be spaced well within the blockade radius, which limits the range of local quantum gates. Here we break this constraint using Floquet frequency modulation, with which we demonstrate Rydberg-blockade entanglement beyond the traditional blockade radius and show how the enlarged entanglement range improves qubit connectivity in a neutral atom array. Further, we find that the coherence of entangled states can be extended under Floquet frequency modulation. Finally, we realize Rydberg anti-blockade states for two sodium Rydberg atoms within the blockade radius. Such Rydberg anti-blockade states for atoms at close range enables the robust preparation of strongly-interacting, long-lived Rydberg states, yet their steady-state population cannot be achieved with only the conventional static drive. Our work transforms between the paradigmatic regimes of Rydberg blockade versus anti-blockade and paves the way for realizing more connected, coherent, and tunable neutral atom quantum processors with a single approach.
PubMed: 37932268
DOI: 10.1038/s41467-023-42899-8 -
Discover Nano Sep 2023Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is... (Review)
Review
Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is also difficult for image processors based on complementary metal-oxide-semiconductor (CMOS) transistors to continuously increase the integration density, causing by their underlying physical restriction and economic costs. However, such obstacles can be eliminated by non-volatile resistive memory technologies (known as memristors), arising from their compacted area, speed, power consumption high efficiency, and in-memory computing capability. This review begins with presenting the image processing methods based on pure algorithm and conventional CMOS-based digital image processing strategies. Subsequently, current issues faced by digital image processing and the strategies adopted for overcoming these issues, are discussed. The state-of-the-art memristor technologies and their challenges in digital image processing applications are also introduced, such as memristor-based image compression, memristor-based edge and line detections, and voice and image recognition using memristors. This review finally envisages the prospects for successful implementation of memristor devices in digital image processing.
PubMed: 37759137
DOI: 10.1186/s11671-023-03901-w -
Foods (Basel, Switzerland) Dec 2023Pathogenic biofilm formation within food processing industries raises a serious public health and safety concern, and places burdens on the economy. Biofilm formation on... (Review)
Review
Pathogenic biofilm formation within food processing industries raises a serious public health and safety concern, and places burdens on the economy. Biofilm formation on equipment surfaces is a rather complex phenomenon, wherein multiple steps are involved in bacterial biofilm formation. In this review we discuss the stages of biofilm formation, the existing literature on the impact of surface properties and shear stress on biofilms, types of bioreactors, and antimicrobial coatings. The review underscores the significance of prioritizing biofilm prevention strategies as a first line of defense, followed by control measures. Utilizing specific biofilm eradication strategies as opposed to a uniform approach is crucial because biofilms exhibit different behavioral outcomes even amongst the same species when the environmental conditions change. This review is geared towards biofilm researchers and food safety experts, and seeks to derive insights into the scope of biofilm formation, prevention, and control. The use of suitable bioreactors is paramount to understanding the mechanisms of biofilm formation. The findings provide useful information to researchers involved in bioreactor selection for biofilm investigation, and food processors in surfaces with novel antimicrobial coatings, which provide minimal bacterial attachment.
PubMed: 38137299
DOI: 10.3390/foods12244495 -
Science Advances Jun 2023Analog optical and electronic hardware has emerged as a promising alternative to digital electronics to improve the efficiency of deep neural networks (DNNs). However,...
Analog optical and electronic hardware has emerged as a promising alternative to digital electronics to improve the efficiency of deep neural networks (DNNs). However, previous work has been limited in scalability (input vector length ≈ 100 elements) or has required nonstandard DNN models and retraining, hindering widespread adoption. Here, we present an analog, CMOS-compatible DNN processor that uses free-space optics to reconfigurably distribute an input vector and optoelectronics for static, updatable weighting and the nonlinearity-with ≈ 1000 and beyond. We demonstrate single-shot-per-layer classification of the MNIST, Fashion-MNIST, and QuickDraw datasets with standard fully connected DNNs, achieving respective accuracies of 95.6, 83.3, and 79.0% without preprocessing or retraining. We also experimentally determine the fundamental upper bound on throughput (∼0.9 exaMAC/s), set by the maximum optical bandwidth before substantial increase in error. Our combination of wide spectral and spatial bandwidths enables highly efficient computing for next-generation DNNs.
PubMed: 37343096
DOI: 10.1126/sciadv.adg7904 -
Quantitative Imaging in Medicine and... Jul 2023An aberration correction algorithm has been implemented and demonstrated in an echocardiographic clinical trial using two-dimensional (2D) imaging. The method estimates...
BACKGROUND
An aberration correction algorithm has been implemented and demonstrated in an echocardiographic clinical trial using two-dimensional (2D) imaging. The method estimates and compensates arrival time errors between different sub-aperture processor (SAP) signals in a matrix array probe.
METHODS
Five standard views of channel data cineloops were recorded from 22 patients (11 male and 11 female) resulting in a total of 116 cineloops. The channel data were processed with and without the aberration correction algorithm, allowing for side-by-side comparison of images processed from the same channel data cineloops.
RESULTS
The aberration correction algorithm improved image quality, as quantified by a coherence metric, in all 7,380 processed frames. In a blinded and left-right-randomized side-by-side evaluation, four cardiologists (two experienced and two in training) preferred the aberration corrected cineloops in 97% of the cases. The clinicians reported that the corrected cineloops appeared sharper with better contrast and less noise. Many structures like valve leaflets, chordae, endocardium, and endocardial borders appeared narrower and more clearly defined in the aberration corrected images. An important finding is that aberration correction improves contrast between the endocardium and ventricle cavities for every processed image. The gain difference was confirmed by the cardiologists in their feedback and quantified with a median global gain difference estimate between the aberration-corrected and non-corrected images of 1.2 dB.
CONCLUSIONS
The study shows the potential value of aberration correction in clinical echocardiography. Systematic improvement of images acquired with state-of-art equipment was observed both with quantitative metrics of image quality and clinician preference.
PubMed: 37456280
DOI: 10.21037/qims-22-895 -
Molecular Brain Jun 2023Mice hippocampus contains three prominent subregions, CA1, CA3 and DG and is well regarded as an essential multiple task processor for learning, memory and cognition...
Mice hippocampus contains three prominent subregions, CA1, CA3 and DG and is well regarded as an essential multiple task processor for learning, memory and cognition based on tremendous studies on these three subregions. The narrow region sandwiched between CA1 and CA3 called CA2 has been neglected for a long time. But it raises great attentions recently since this region manifests the indispensable role in social memory. Its unique physical position connecting CA1 and CA3 suggests the potential novel functions besides social memory regulation. But the CA2 is too small to be accurately targeted. A flexible AAV tool capable of accurately and efficiently targeting this region is highly demanded. To fill this gap, we generate an AAV expressing Cre driven by the mini Map3k15 promoter, AAV/M1-Cre, which can be easily utilized to help tracing and manipulating CA2 pyramidal neurons. However, M1-Cre labeled a small percentage of M1RGS14 neurons that do not colocalize with any RGS14/STEP/PEP4/Amigo2 pyramidal neurons. They are proved to be the mixture of normal CA2 pyramidal neurons, CA3-like neurons in CA2-CA3 mixed border, some CA2 interneurons and rarely few CA1-like neurons, which are probably the ones projecting to the revealed CA2 downstream targets, VMH, STHY and PMV in WT mice injecting this AAV/M1-Cre virus but not in Amigo2-Cre mice. Though it is still challenging to get a pure CA2 tracking and manipulation system, this tool provides a new, more flexible and extended strategy for in-depth CA2 functional study in the future.
Topics: Animals; Mice; Neurons; Pyramidal Cells; Cognition; Hippocampus; Interneurons
PubMed: 37303064
DOI: 10.1186/s13041-023-01038-6 -
Journal of Experimental Botany Jul 2023The fascination produced by the possibility of engineering plants with augmented capabilities has accompanied plant biotechnology since its origins. This prospect has... (Review)
Review
The fascination produced by the possibility of engineering plants with augmented capabilities has accompanied plant biotechnology since its origins. This prospect has become even more relevant in present times under the pressure imposed by climate change and population growth. Today's plant biotechnologists approach this challenge with the tools of synthetic biology, which facilitate the assembly of synthetic gene circuits (SGCs) from their modular components. Transcriptional SGCs take environmental or endogenous inputs and operate them using transcriptional signals in ways that do not necessarily occur in nature, generating new physiological outputs. Many genetic components have been developed over the years that can be employed in the design and construction of plant SGCs. This review aims to provide an updated view of the components available, proposing a general scheme that facilitates the classification of circuit components in sensor, processor, and actuator modules. Following this analogy, we review the latest advances in the design of SGCs and discuss the main challenges ahead.
Topics: Genes, Synthetic; Gene Regulatory Networks; Biotechnology; Plants; Synthetic Biology
PubMed: 37204924
DOI: 10.1093/jxb/erad167