Formation of gapless Fermi arcs and fingerprints of order in the pseudogap state of cuprate superconductors
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phonons, thermal conductivity, energy storage (inc↗
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A fingerprint based metric for measuring similarities of crystalline structures
Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Identifying the 'fingerprint' of antiferromagnetic spin fluctuations in iron pnictide superconductors
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phonons, thermal conductivity, energy storage (inc↗
Charge modulation as fingerprints of phase-string triggered interference
Charge order appears to be an ubiquitous phenomenon in doped Mott insulators, which is currently under intense experimental and theoretical investigations particularly in the high T c cuprates. This phenomenon is conventionally understood in terms of Hartree-Fock-type mean-field theory. Here we demonstrate a mechanism for charge modulation which is rooted in the many-particle quantum physics arising in the strong coupling limit. Specifically, we consider the problem of a single hole in a bipartite t - J ladder. As a remnant of the fermion signs, the hopping hole picks up subtle phases pending the fluctuating spins, the so-called phase-string effect. We demonstrate the presence of charge modulations in the density matrix renormalization group solutions which disappear when the phase strings are switched off. This form of charge modulation can be understood analytically in a path-integral language with a mean-field-like approximation adopted, showing that the phase strings give rise to constructive interferences leading to self-localization. When the latter occurs, left- and right-moving propagating modes emerge inside the localization volume and their interference is responsible for the real space charge modulation.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Fingerprints of Berry phases in the bulk exciton spectrum of a topological insulator
Not Available
Allocca, Andrew A.↗
Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
3D structural interaction fingerprints↗
SU-E-I-75: Development of New Biological Fingerprints for Patient Recognition to Identify Misfiled Images in a PACS Server
Purpose: We are trying to develop an image-searching technique to identify misfiled images in a picture archiving and communication system (PACS) server by using five biological fingerprints: the whole lung field, cardiac shadow, superior mediastinum, lung apex, and right lower lung. Each biological fingerprint in a chest radiograph includes distinctive anatomical structures to identify misfiled images. The whole lung field was less effective for evaluating the similarity between two images than the other biological fingerprints. This was mainly due to the variation in the positioning for chest radiographs. The purpose of this study is to develop new biological fingerprints that could reduce influence of differences in the positioning for chest radiography. Methods: Two hundred patients were selected randomly from our database (36,212 patients). These patients had two images each (current and previous images). Current images were used as the misfiled images in this study. A circumscribed rectangular area of the lung and the upper half of the rectangle were selected automatically as new biological fingerprints. These biological fingerprints were matched to all previous images in the database. The degrees of similarity between the two images were calculated for the same and different patients. The usefulness of new the biological fingerprints for automated patient recognition was examined in terms of receiver operating characteristic (ROC) analysis. Results: Area under the ROC curves (AUCs) for the circumscribed rectangle of the lung, upper half of the rectangle, and whole lung field were 0.980, 0.994, and 0.950, respectively. The new biological fingerprints showed better performance in identifying the patients correctly than the whole lung field. Conclusion: We have developed new biological fingerprints: circumscribed rectangle of the lung and upper half of the rectangle. These new biological fingerprints would be useful for automated patient identification system because they are less affected by positioning differences during imaging.
60 APPLIED LIFE SCIENCES↗
Constitutive model fingerprints in medium-amplitude oscillatory shear
Rheologists have expectations for signatures of linear viscoelastic properties, such as shapes of G'(ω) and G"(ω). Medium amplitude (or asymptotically-nonlinear) oscillatory shear (MAOS) provides additional nonlinear rheological information with low dimensional, well-defined material functions. Here, we develop expectations of signatures (or fingerprints) for the four asymptotically-nonlinear material functions associated with MAOS, [e 1 ](ω), [e 3 ](ω), [v 1 ](ω), [v 3 ](ω). Although the linear fingerprints may be identical for different models, the asymptotically-nonlinear fingerprints may be different in magnitude, frequency-scaling, curve shapes, and sign changes. To perform the analysis, we collect/translate a library of available analytical strain-controlled MAOS fingerprints for seven different constitutive models. Using this library, we identify general trends and highlight key differences of asymptotic-nonlinear viscoelasticity. Asymptotic nonlinearities for all models considered here obey the terminal regime inter-relations and frequency scaling predicted by Bharadwaj and Ewoldt. Unlike the positive linear viscoelastic measures, at least one of the four asymptotic nonlinearities changes signs with Deborah number (De). Following sign interpretations of Ewoldt and Bharadwaj, we show that nonlinearities tend to be driven by strain-rates at small De, and by strains at large De, a trend observed for nearly all the constitutive models studied here, the exception being the model for dilute rigid dumbbell suspensions. Some constitutive models exhibit multiple sign changes at intermediate De and there may be no universal behavior of asymptotically-nonlinear fingerprints in this regime. Therefore, frequency-dependent signatures can be material-specific. Furthermore, this will allow inverse problems to infer structure, select models, and fit model parameters using asymptotically-nonlinear signatures. To illustrate this aspect, we demonstrate a fingerprint matching exercise with experimental measurements on a transiently cross-linked hydrogel system. We find that currently available model fingerprints can match the qualitative magnitudes and frequency dependence, but not the signs of the experimental transient network response.
42 ENGINEERING↗
Volatile monoterpene ‘fingerprints’ of resinous Protium tree species in the Amazon rainforest
Volatile terpenoid resins represent a diverse group of plant defense chemicals involved in defense against herbivory, abiotic stress, and communication. However, their composition in tropical forests remains poorly characterized. As a part of tree identification, the 'smell' of damaged trunks is widely used, but is highly subjective. Here, we analyzed trunk volatile monoterpene emissions from 15 species of the genus Protium in the central Amazon. By normalizing the abundances of 28 monoterpenes, 9 monoterpene 'fingerprint' patterns emerged, characterized by a distinct dominant monoterpene. While 4 of the 'fingerprint' patterns were composed of multiple species, 5 were composed of a single species. Moreover, among individuals of the same species, 6 species had a single 'fingerprint' pattern, while 9 species had two or more 'fingerprint' patterns among individuals. A comparison of 'fingerprints' between 2015 and 2017 from 15 individuals generally showed excellent agreement, demonstrating a strong dependence on species identity, but not time of collection. The results are consistent with a previous study that found multiple divergent copies of monoterpene synthase enzymes in Protium. We conclude that the monoterpene 'fingerprint' database has important implications for constraining Protium species identification and phylogenetic relationships and enhancing understanding of physiological and ecological functions of resins and their potential commercial applications.
59 BASIC BIOLOGICAL SCIENCES↗
Analysis of cellular and extracellular DNA in fingerprints
It has been previously shown that DNA can be recovered from latent fingerprints left on various surfaces [R. A. H. van Oorschot and M. K. Jones, Nature 387, 767 (1997)]. However, the source of the DNA, extracellular versus cellular origin, is difficult to determine. If the DNA is cellular, it is believed to belong to skin cells while extracellular DNA is believed to originate from body fluids such as sweat [D. J. Daly et. al, Forensic Sci. Int. Genet. 6, 41-46 (2012); V. V. Vlassov et. al, BioEssays 29, 654-667 (2007)]. The origin of the DNA in fingerprints has implications for processing and interpretation of forensic evidence. The determination of the origin of DNA in fingerprints is further complicated by the fact that the DNA in fingerprints tends to be at a very low quantity [R. A. H. van Oorschot and M. K. Jones, Nature 387, 767 (1997)]. This study examined fingerprints from five volunteers left on sterilized glass slides and plastic pens. Three fingerprints were left on each glass slide (thumb, index, and middle fingers) while the pens were held as if one was writing with them. The DNA was collected from the objects using the wet swabbing technique (TE buffer). Following collection, the cellular and extracellular components of each sample were separated using centrifugation and an acoustofluidics system. Centrifugation is still the primary separation technique utilized in forensics laboratories, while acoustic focusing uses sound waves to focus large particles (cells) into low pressure nodes, separating them from the rest of the sample matrix. After separation, all samples were quantified using real-time quantitative PCR (qPCR). The overall trend is that there is more DNA in the extracellular fractions than cellular fractions for both centrifugation and acoustofluidic processing. Additionally, more DNA was generally collected from the pen samples than the samples left on glass slides.
47 OTHER INSTRUMENTATION↗
Wireless infrastructure protection using low-cost radio frequency fingerprinting receivers
We report that low-data-rate wireless networks incorporated in critical infrastructure applications can be protected through 128-bit encryption keys and address-based access control lists. However, these bit-level credentials are vulnerable to interception, extraction and spoofing using software tools available free of charge on the Internet. Recent research has demonstrated that wireless physical layer device fingerprinting can be used to defend against replay and spoofing attacks. However, radio frequency (RF) fingerprinting typically uses expensive signal collection systems; this is because fingerprinting wireless devices with low-cost receivers has been reported to have inconsistent accuracy. In conclusion, this paper demonstrates a robust radio frequency fingerprinting process that is consistently accurate with both high-end and low-cost receivers. Indeed, the results demonstrate that low-cost software-defined radios can be used to perform accurate radio frequency fingerprinting and to identify spoofing attacks in critical IEEE 802.154-based infrastructure networks such as ZigBee.
97 MATHEMATICS AND COMPUTING↗
Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)
Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.
59 BASIC BIOLOGICAL SCIENCES↗
Exceptional stratospheric contribution to human fingerprints on atmospheric temperature
In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO 2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S 25 − 50 ). To date, however, S 25 − 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S 25 − 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S 25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S 25 − 50 signal and noise patterns are accompanied by large cooling of S 25 − 50 (1 to 2 ° C over 1986 to 2022) and low S 25 − 50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere.
climate modeling↗
Transformer Masked Autoencoders for RF Device Fingerprinting
Machine learning methods for RF device fingerprinting typically rely on CNN-based models. Transformer-based models have outperformed CNNs for modulation classification tasks, but there are few implementations for device fingerprinting. We train a transformer for device fingerprinting with the largest device count to date and explore several variations of the architecture. Additionally, we demonstrate that pre-training an RF transformer as a Masked Autoencoder improves classification accuracy, as has been observed for CNN fingerprinting models and vision transformers.
artificial intelligence↗