Browse Articles

Double nanowire quantum dots and machine learning

Abstract We present an approach to estimate the single-particle energies in double InAs/InP nanowire quantum dots by combining an atomistic tight-binding approach with machine learning. The method works particularly well with a neural network and transfer learning, where we can accurately recover ground state energies with root-mean-square deviation around 1 meV by using only a small training set and capitalizing on earlier, smaller-scale computations. The training set is only a fraction of the multidimensional search space of possible dot sizes and inter-dot spacings. Besides the cases presented in this work, we expect this technique will interest other researchers involved in solving the inverse computational problem of matching spectra to nanostructure morphological properties.

Turbulent mixing controls fixation of growing antagonistic populations

Unlike coffee and cream that homogenize when stirred, growing micro-organisms (e.g., bacteria, baker’s yeast) can actively kill each other and avoid mixing. How do such antagonistic interactions impact the growth and survival of competing strains, while being spatially advected by turbulent flows? By using numerical simulations of a continuum model, we study the dynamics of two antagonistic strains that are dispersed by incompressible turbulent flows in two spatial dimensions. A key parameter is the ratio of the fluid transport time to that of biological reproduction, which determines the winning organism that ultimately takes over the whole population from an initial heterogeneous state, a process known as fixation. By quantifying the probability and mean time for fixation along with the spatial structure of concentration fluctuations, we demonstrate how turbulence raises the threshold for biological nucleation and antagonism suppresses flow-induced mixing by depleting the population at interfaces. Our work highlights the unusual biological consequences of the interplay of turbulent fluid flows with antagonistic population dynamics, with potential implications for marine microbial ecology and origins of biological chirality.

Topological transition on a conformal manifold for the quantum Ising model with a longer range interaction

A preliminary study on dynamic response of cold-region tunnel systems considering the frozen soil in plastic zone

Variable-strength nonlocal measurements reveal quantum violations of classical counting principles

We implement a variant of the quantum pigeonhole paradox thought experiment to study whether classical counting principles survive in the quantum domain. We observe strong measurements significantly violate the pigeonhole principle (that among three pigeons in two holes, at least one pair must be in the same hole) and the sum rule (that the number of pigeon pairs in the same hole is the sum of the number of pairs across each of the holes) in an ensemble that is pre- and postselected into particular separable states. To investigate whether measurement disturbance is a viable explanation for these counterintuitive phenomena, we employ a we employ variable-strength nonlocal measurements. As we decrease the measurement strength, we find the violation of the sum rule decreases, yet the pigeonhole principle remains violated. In the weak limit, the sum rule is restored due to the cancellation between two weak values with equal and opposite imaginary parts. We observe the same kind of cancellation at higher measurement strengths, thus raising the question: do strong measurements have imaginary parts?

Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer

Self-assembled proteomimetic (SAP) with antibody-like binding from short PNA–peptide conjugates

Affinity proteins based on a three-helix bundle (affibodies, alphabodies, and computationally de novo designed ones) have been shown to be a general platform to discover binders with properties reminiscent of antibodies, combining high target specificity with affinities reaching well below the nanomolar. Herein, we report a strategy, coined self-assembled proteomimetic (SAP), to mimic such three-helix bundle architecture with a hybridization-enforced two-helix coiled coil that is obtained by templated native chemical ligation (T-NCL) of PNA–peptide conjugates. This SAP strategy stands out by its synthetic accessibility, reducing the length on the longest synthetic peptide to less than 30 amino acids which is readily attainable by standard SPPS methodologies. We show that the T-NCL dramatically accelerates the ligation, enabling this chemistry to proceed in a combinatorial fashion at low micromolar concentrations. We demonstrate that small combinatorial libraries of SAPs can be prepared in one operation and used directly in affinity selections against a target of interest with an LC–MS analysis of the fittest binders. Moreover, we show that the underlying design paradigm is functional for SAPs based on structurally distinct three-helix peptides aimed at different therapeutic targets, namely HER2 and spike’s RBD, reaching picomolar affinities. We further illustrate that the affinity of the SAP can be allosterically regulated using a toehold displacement of the hybridizing PNAs to disrupt the coiled coil stabilization. Finally, we show that an RBD-targeting SAP effectively inhibits viral entry of SARS-CoV-2 with an IC 50 of 2.8 nM.

Synergistic effect of nanosilver fluoride with L-arginine on remineralization of early carious lesions

Molecular characterization of Streptococcus agalactiae strains isolated from pregnant women

A method for unsupervised learning of coherent spatiotemporal patterns in multiscale data

The unsupervised and principled diagnosis of multiscale data is a fundamental obstacle in modern scientific problems from, for instance, weather and climate prediction, neurology, epidemiology, and turbulence. Multiscale data are characterized by a combination of processes acting along multiple dimensions simultaneously, spatiotemporal scales across orders of magnitude, nonstationarity, and/or invariances such as translation and rotation. Existing methods are not well-suited to multiscale data, usually requiring supervised strategies such as human intervention, extensive tuning, or selection of ideal time periods. We present the multiresolution coherent spatio-temporal scale separation (mrCOSTS), a hierarchical and automated algorithm for the diagnosis of coherent patterns or modes in multiscale data. mrCOSTS is a variant of dynamic mode decomposition which decomposes data into bands of spatial patterns with shared time dynamics, thereby providing a robust method for analyzing multiscale data. It requires no training but instead takes advantage of the hierarchical nature of multiscale systems. We demonstrate mrCOSTS using complex multiscale datasets that are canonically difficult to analyze: 1) climate patterns of sea surface temperature, 2) electrophysiological observations of neural signals of the motor cortex, and 3) horizontal wind in the mountain boundary layer. With mrCOSTS, we trivially retrieve complex dynamics that were previously difficult to resolve while additionally extracting hitherto unknown patterns of activity embedded in the dynamics, allowing for advancing the understanding of these fields of study. This method is an important advancement for addressing the multiscale data which characterize many of the grand challenges in science and engineering.

An improved method of AUD-YOLO for surface damage detection of wind turbine blades

Superfluid weight cross-over and critical temperature enhancement in singular flat bands

Nonanalytic Bloch eigenstates at isolated band degeneracy points exhibit singular behavior in the quantum metric. Here, a description of superfluid weight for zero-energy flat bands in proximity to other high-energy bands is presented, where they together form a singular band gap system. When the singular band gap closes, the geometric and conventional contributions to the superfluid weight as a function of the superconducting gap exhibit different cross-over behaviors. The scaling behavior of superfluid weight with the band gap is studied in detail, and the effect on the Berezinskii–Kosterlitz–Thouless transition temperature is explored. It is found that tuning the singular band gap provides a unique mechanism for enhancing the supercurrent and critical temperature of two-dimensional superconductors.

The impact of 24-forms Tai Chi on alpha band power and physical fitness in young adults: a randomized controlled trial

Detrimental influence of Arginase-1 in infiltrating macrophages on poststroke functional recovery and inflammatory milieu

Poststroke inflammation critically influences functional outcomes following ischemic stroke. Arginase-1 (Arg1) is considered a marker for anti-inflammatory macrophages, associated with the resolution of inflammation and promotion of tissue repair in various pathological conditions. However, its specific role in poststroke recovery remains to be elucidated. This study investigates the functional impact of Arg1 expressed in macrophages on poststroke recovery and inflammatory milieu. We observed a time-dependent increase in Arg1 expression, peaking at 7 d after photothrombotic stroke in mice. Cellular mapping analysis revealed that Arg1 was predominantly expressed in LysM-positive infiltrating macrophages. Using a conditional knockout (cKO) mouse model, we examined the role of Arg1 expressed in infiltrating macrophages. Contrary to its presumed beneficial effects, Arg1 cKO in LysM-positive macrophages significantly improved skilled forelimb motor function recovery after stroke. Mechanistically, Arg1 cKO attenuated fibrotic scar formation, enhanced peri-infarct remyelination, and increased synaptic density while reducing microglial synaptic elimination in the peri-infarct cortex. Gene expression analysis of fluorescence-activated single cell sorting (FACS)-sorted CD45 low microglia revealed decreased transforming growth factor-β (TGF-β) signaling and proinflammatory cytokine activity in peri-infarct microglia from Arg1 cKO animals. In vitro coculture experiments demonstrated that Arg1 activity in macrophages modulates microglial synaptic phagocytosis, providing evidence for macrophage–microglia interaction. These findings present unique insights into the function of Arg1 in central nervous system injury and highlight an interaction between infiltrating macrophages and resident microglia in shaping the poststroke inflammatory milieu. Our study identifies Arg1 in macrophages as a potential therapeutic target for modulating poststroke inflammation and improving functional recovery.

Efficient underwater object detection based on feature enhancement and attention detection head

Receptor clustering tunes and sharpens the selectivity of multivalent binding

The immune system exploits a wide range of strategies to combine sensitivity with selectivity for optimal response. We propose a generic physical mechanism that allows tuning the location and steepness of the response threshold of cellular processes activated by multivalent binding. The mechanism is based on the possibility to modulate the attraction between membrane receptors. We use theory and simulations to show how tuning interreceptor attraction can enhance or suppress the binding of multivalent ligand-coated particles to surfaces. The changes in the interreceptor attraction less than the thermal energy k B T can selectively switch the receptor-clustering and activation on or off in an almost step-wise fashion, which we explain by near-critical receptor density fluctuations. We also show that the same mechanism can efficiently regulate the onset of endocytosis for, e.g. , drug delivery vehicles.

4D sensor perception in relativistic image processing

Abstract This article introduces the 4D sensor perception in relativistic image processing as a novel way of position and depth estimation. Relativistic image processing extends conventional image processing in computer vision to include the theory of relativity and combines temporal sensor and image data. In consideration of these temporal and relativistic aspects, we process diverse types of information in a novel model of 4D space through 10 different degrees of freedom consisting of 4 translations and 6 rotations. In this way, sensor and image data can be related and processed as a causal tensor field. This enables the temporal prediction of a user’s own position and environmental changes as well as the extraction of depth and sensor maps by related sensors and images. The dynamic influences and cross-sensor dependencies are incorporated into the metric calculation of spatial distances and positions, opening up new perspectives on numerous fields of application in mobility, measurement technology, robotics, and medicine.

Hidden complexity of α7 nicotinic acetylcholine receptor desensitization revealed by MD simulations and Markov state modeling

The α7 nicotinic acetylcholine receptor is a pentameric ligand-gated ion channel that plays an important role in neuronal signaling throughout the nervous system. Its implication in neurological disorders and inflammation has spurred the development of numerous compounds that enhance channel activation. However, the therapeutic potential of these compounds has been limited by the characteristically fast desensitization of the α7 receptor. Using recent high-resolution structures from cryo-EM, and all-atom molecular dynamic simulations augmented by Markov state modeling, here we explore the mechanism of α7 receptor desensitization and its implication on allosteric modulation. The results provide a precise characterization of the desensitization gate and illuminate the mechanism of ion-pore opening/closing with an agonist bound. In addition, the simulations reveal the existence of a short-lived, open-channel intermediate between the activated and desensitized states that rationalizes the paradoxical pharmacology of the L247T mutant and may be relevant to type-II allosteric modulation. This analysis provides an interpretation of the signal transduction mechanism and its regulation in α7 receptors.

Phenotypic and genotypic characterization of Aeromonas hydrophila isolated from freshwater fishes at Middle Upper Egypt

Abstract Aeromonas hydrophila is a common fish pathogen and a significant foodborne pathogen of increasing public health concern. This study was conducted in Middle Upper Egypt to determine the prevalence of A. hydrophila among the diseased Oreochromis niloticus (n = 100) and Clarias gariepinus (n = 100) at Assiut and Sohag Governorates. A. hydrophila isolates (n = 44) were assessed for antimicrobial susceptibility and biofilm production. Moreover, PCR was performed to analyze the incidence of some genes in 20 isolates of A. hydrophila. The results showed that 24% and 20% of the examined O. niloticus and C. gariepinus were infected with A. hydrophila respectively, with all (100%) showing a variety of clinical signs of septicemia. A. hydrophila isolates were all biofilm producers, with varied degrees of biofilm production. 79.5% of the isolates were multidrug-resistant and had a high multiple antimicrobial resistance index > 0.2. PCR analysis revealed that all isolates carried act and blaTEM genes but not carried int2 gene. Additionally, sul1, aer, tetA, int1, and qnrA genes were present in 75%, 60%, 55%, 55% and 45% of them, respectively. This study highlights the high incidence of multidrug-resistant pathogenic A. hydrophila in the infected fishes, posing a serious risk to humans and fish.