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We report on the 1,000-element transition-edge sensor (TES) x-ray spectrometer implementation of the TOMographic Circuit Analysis Tool (TOMCAT). TOMCAT combines a high spatial resolution scanning electron microscope (SEM) with a highly efficient and pixelated TES spectrometer to reconstruct three-dimensional maps of nanoscale integrated circuits (ICs). A 240-pixel prototype spectrometer was recently used to reconstruct ICs at the 130 nm technology node, but to increase imaging speed to more practical levels, the detector efficiency needs to be improved. For this reason, we are building a spectrometer that will eventually contain 3,000 TES microcalorimeters read out with microwave superconducting quantum interference device (SQUID) multiplexing, and we currently have commissioned a 1,000 TES subarray. This still represents a significant improvement from the 240-pixel system and allows us to begin characterizing the full spectrometer performance. Of the 992 maximimum available readout channels, we have yielded 818 devices, representing the largest number of TES x-ray microcalorimeters simultaneously read out to date. These microcalorimeters have been optimized for pulse speed rather than purely energy resolution, and we measure a FWHM energy resolution of 14 eV at the 8.0 keV Cu K$\alpha$ line., Comment: 5 pages, 4 figures, submitted to IEEE Transactions on Applied Superconductivity

Improving adhesives for wet surfaces is an ongoing challenge. While the adhesive proteins of marine mussels have inspired many synthetic wet adhesives, the mechanisms of mussel adhesion are still not fully understood. Using surface forces apparatus (SFA) measurements and replica-exchange and umbrella-sampling molecular dynamics simulations, we probed the relationships between the sequence, structure, and adhesion of mussel-inspired peptides. Experimental and computational results reveal that peptides derived from mussel foot protein 3 slow (mfp-3s) containing 3,4-dihydroxyphenylalanine (Dopa), a post-translationally modified variant of tyrosine commonly found in mussel foot proteins, form adhesive monolayers on mica. In contrast, peptides with tyrosine adsorb as weakly adhesive clusters. We further considered simulations of mfp-3s derivatives on a range of hydrophobic and hydrophilic organic and inorganic surfaces (including silica, self-assembled monolayers, and a lipid bilayer) and demonstrated that the chemical character of the target surface and proximity of cationic and hydrophobic residues to Dopa affect peptide adsorption and adhesion. Collectively, our results suggest that conversion of tyrosine to Dopa in hydrophobic, sparsely charged peptides influences peptide self-association and ultimately dictates their adhesive performance.

X-ray tomography is a non-destructive imaging technique that reveals the interior of an object from its projections at different angles. Under sparse-view and low-photon sampling, regularization priors are required to retrieve a high-fidelity reconstruction. Recently, deep learning has been used in X-ray tomography. The prior learned from training data replaces the general-purpose priors in iterative algorithms, achieving high-quality reconstructions with a neural network. Previous studies typically assume the noise statistics of test data are acquired a priori from training data, leaving the network susceptible to a change in the noise characteristics under practical imaging conditions. In this work, we propose a noise-resilient deep-reconstruction algorithm and apply it to integrated circuit tomography. By training the network with regularized reconstructions from a conventional algorithm, the learned prior shows strong noise resilience without the need for additional training with noisy examples, and allows us to obtain acceptable reconstructions with fewer photons in test data. The advantages of our framework may further enable low-photon tomographic imaging where long acquisition times limit the ability to acquire a large training set.

Frozen peas and carrots are commonly available in grocery stores in the United States. The carrots are cut into cubes roughly 8 mm on a side, while the peas are roughly spherical with a diameter of about 6 mm. They are prepared together in water that is brought to a boil. The peas and carrots, which are initially mixed together, separate as the water is heated. By counting the number of border vs. non-border vegetables before and after boiling, the decrease of the number of vegetables on the border demonstrates separation rather than chance rearrangement with the highly significant value p < 0.00001. The demonstration is a kitchen physics example of pattern formation in nonequilibrium systems.

Feature sizes in integrated circuits have decreased substantially over time, and it has become increasingly difficult to three-dimensionally image these complex circuits after fabrication. This can be important for process development, defect analysis, and detection of unexpected structures in externally sourced chips, among other applications. Here, we report on a non-destructive, tabletop approach that addresses this imaging problem through x-ray tomography, which we uniquely realize with an instrument that combines a scanning electron microscope (SEM) with a transition-edge sensor (TES) x-ray spectrometer. Our approach uses the highly focused SEM electron beam to generate a small x-ray generation region in a carefully designed target layer that is placed over the sample being tested. With the high collection efficiency and resolving power of a TES spectrometer, we can isolate x-rays generated in the target from background and trace their paths through regions of interest in the sample layers, providing information about the various materials along the x-ray paths through their attenuation functions. We have recently demonstrated our approach using a 240 Mo/Cu bilayer TES prototype instrument on a simplified test sample containing features with sizes of $\sim$1 $\mu$m. Currently, we are designing and building a 3000 Mo/Au bilayer TES spectrometer upgrade, which is expected to improve the imaging speed by factor of up to 60 through a combination of increased detector number and detector speed., Comment: 5 pages, 3 figures, published in IEEE Transactions on Applied Superconductivity

A technique for characterizing and correcting the linearity of radiometric instruments is known by the names the ‘flux-addition method’ and the ‘combinatorial technique’. In this paper, we develop a rigorous uncertainty quantification method for use with this technique and illustrate its use with both synthetic data and experimental data from a ‘beam conjoiner’ instrument. We present a probabilistic model that relates the instrument readout to a set of unknown fluxes via a set of polynomial coefficients. Maximum likelihood estimates (MLEs) of the unknown fluxes and polynomial coefficients are recommended, while a non-parametric bootstrap algorithm enables uncertainty quantification including standard errors and confidence intervals. The synthetic data represent plausible outputs of a radiometric instrument and enable testing and validation of the method. The MLEs for these data are found to be approximately unbiased, and confidence intervals derived from the bootstrap replicates are found to be consistent with their target coverage of 95%. For the polynomial coefficients, the observed coverages range from 91% to 99%. The experimental data set illustrates how a complete calibration with uncertainties can be achieved using the method plus one well-known flux level. The uncertainty contribution attributable to estimation of the instrument’s non-linear response is less than 0.025% over most of its range.

Despite being relatively benign and not an indicative signature of toxicity, fibril formation and fibrillar structures continue to be key factors in assessing the structure–function relationship in protein aggregation diseases. The inability to capture molecular cross-talk among key players at the tissue level before fibril formation greatly accounts for the missing link toward the development of an efficacious therapeutic intervention for Type II diabetes mellitus (T2DM). We show that human α-calcitonin gene-related peptide (α-CGRP) remodeled amylin fibrillization. Furthermore, while CGRP and/or amylin monomers reduce the secretion of both mouse Ins1 and Ins2 proteins, CGRP oligomers have a reverse effect on Ins1. Genetically reduced Ins2, the orthologous version of human insulin, has been shown to enhance insulin sensitivity and extend the life-span in old female mice. Beyond the mechanistic insights, our data suggest that CGRP regulates insulin secretion and lowers the risk of T2DM. Our result rationalizes how migraine might be protective against T2DM. We envision the new paradigm of CGRP : amylin interactions as a pivotal aspect for T2DM diagnostics and therapeutics. Maintaining a low level of amylin while increasing the level of CGRP could become a viable approach toward T2DM prevention and treatment., CGRP concentration is elevated in migraine conditions. The protective effect of migraine against type 2 diabetes is attributed to the ability of CGRP to remodel human amylin aggregation and to suppress the secretion of mouse insulin 2 (the orthologue of human insulin).

The neuronal membrane-associated periodic spectrin skeleton (MPS) contributes to neuronal development, remodeling, and organization. Post-translational modifications impinge on spectrin, the major component of the MPS, but their role remains poorly understood. One modification targeting spectrin is cleavage by calpains, a family of calcium-activated proteases. Spectrin cleavage is regulated by activated calpain, but also by the calcium-dependent binding of calmodulin (CaM) to spectrin. The physiologic significance of this balance between calpain activation and substrate-level regulation of spectrin cleavage is unknown. We report a strain of C57BL/6J mice harboring a single αII spectrin point mutation (Sptan1 c.3293G > A:p.R1098Q) with reduced CaM affinity and intrinsically enhanced sensitivity to calpain proteolysis. Homozygotes are embryonic lethal. Newborn heterozygotes of either gender appear normal, but soon develop a progressive ataxia characterized biochemically by accelerated calpain-mediated spectrin cleavage and morphologically by disruption of axonal and dendritic integrity and global neurodegeneration. Molecular modeling predicts unconstrained exposure of the mutant spectrin’s calpain-cleavage site. These results reveal the critical importance of substrate-level regulation of spectrin cleavage for the maintenance of neuronal integrity. Given that excessive activation of calpain proteases is a common feature of neurodegenerative disease and traumatic encephalopathy, we propose that damage to the spectrin MPS may contribute to the neuropathology of many disorders.

The loss of proteostasis over the life course is associated with a wide range of debilitating degenerative diseases and is a central hallmark of human aging. When left unchecked, proteins that are intrinsically disordered can pathologically aggregate into highly ordered fibrils, plaques, and tangles (termed amyloids), which are associated with countless disorders such as Alzheimer's disease, Parkinson's disease, type II diabetes, cancer, and even certain viral infections. However, despite significant advances in protein folding and solution biophysics techniques, determining the molecular cause of these conditions in humans has remained elusive. This has been due, in part, to recent discoveries showing that soluble protein oligomers, not insoluble fibrils or plaques, drive the majority of pathological processes. This has subsequently led researchers to focus instead on heterogeneous and often promiscuous protein oligomers. Unfortunately, significant gaps remain in how to prepare, model, experimentally corroborate, and extract amyloid oligomers relevant to human disease in a systematic manner. This Review will report on each of these techniques and their successes and shortcomings in an attempt to standardize comparisons between protein oligomers across disciplines, especially in the context of neurodegeneration. By standardizing multiple techniques and identifying their common overlap, a clearer picture of the soluble neuropathological aggresome can be constructed and used as a baseline for studying human disease and aging.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mitochondrially derived peptides (MDPs) such as humanin (HN) have shown a remarkable ability to modulate neurological amyloids and apoptosis-associated proteins in cells and animal models. Recently, we found that humanin-like peptides also inhibit amyloid formation outside of neural environments in islet amyloid polypeptide (IAPP) fibrils and plaques, which are hallmarks of Type II diabetes. However, the biochemical basis for regulating amyloids through endogenous MDPs remains elusive. One hypothesis is that MDPs stabilize intermediate amyloid oligomers and discourage the formation of insoluble fibrils. To test this hypothesis, we carried out simulations and experiments to extract the dominant interactions between the S14G-HN mutant (HNG) and a diverse set of IAPP structures. Replica-exchange molecular dynamics suggests that MDPs cap the growth of amyloid oligomers. Simulations also indicate that HNG-IAPP heterodimers are 10 times more stable than IAPP homodimers, which explains the substoichiometric ability of HNG to inhibit amyloid growth. Despite this strong attraction, HNG does not denature IAPP. Instead, HNG binds IAPP near the disordered NFGAIL motif, wedging itself between amyloidogenic fragments. Shielding of NFGAIL-flanking fragments reduces the formation of parallel IAPP β-sheets and subsequent nucleation of mature amyloid fibrils. From ThT spectroscopy and electron microscopy, we found that HNG does not deconstruct mature IAPP fibrils and oligomers, consistent with the simulations and our proposed hypothesis. Taken together, this work provides new mechanistic insight into how endogenous MDPs regulate pathological amyloid growth at the molecular level and in highly substoichiometric quantities, which can be exploited through peptidomimetics in diabetes or Alzheimer's disease.

Using a commercial X-ray tomography instrument, we have obtained reconstructions of a graded-index optical fiber with voxels of edge length 1.05 µm at 12 tube voltages. The fiber manufacturer created a graded index in the central region by varying the germanium concentration from a peak value in the center of the core to a very small value at the core-cladding boundary. Operating on 12 tube voltages, we show by a singular value decomposition that there are only two singular vectors with significant weight. Physically, this means scans beyond two tube voltages contain largely redundant information. We concentrate on an analysis of the images associated with these two singular vectors. The first singular vector is dominant and images of the coefficients of the first singular vector at each voxel look are similar to any of the single-energy reconstructions. Images of the coefficients of the second singular vector by itself appear to be noise. However, by averaging the reconstructed voxels in each of several narrow bands of radii, we can obtain values of the second singular vector at each radius. In the core region, where we expect the germanium doping to go from a peak value at the fiber center to zero at the core-cladding boundary, we find that a plot of the two coefficients of the singular vectors forms a line in the two-dimensional space consistent with the dopant decreasing linearly with radial distance from the core center. The coating, made of a polymer rather than silica, is not on this line indicating that the two-dimensional results are sensitive not only to the density but also to the elemental composition.

We present the structure of an engineered protein–protein interface between two beta barrel proteins, which is mediated by interactions between threonine (Thr) residues. This Thr zipper structure suggests that the protein interface is stabilized by close‐packing of the Thr residues, with only one intermonomer hydrogen bond (H‐bond) between two of the Thr residues. This Thr‐rich interface provides a unique opportunity to study the behavior of Thr in the context of many other Thr residues. In previous work, we have shown that the side chain (χ (1)) dihedral angles of interface and core Thr residues can be predicted with high accuracy using a hard sphere plus stereochemical constraint (HS) model. Here, we demonstrate that in the Thr‐rich local environment of the Thr zipper structure, we are able to predict the χ (1) dihedral angles of most of the Thr residues. Some, however, are not well predicted by the HS model. We therefore employed explicitly solvated molecular dynamics (MD) simulations to further investigate the side chain conformations of these residues. The MD simulations illustrate the role that transient H‐bonding to water, in combination with steric constraints, plays in determining the behavior of these Thr side chains. Broader Audience Statement: Protein–protein interactions are critical to life and the search for ways to disrupt adverse protein–protein interactions involved in disease is an ongoing area of drug discovery. We must better understand protein–protein interfaces, both to be able to disrupt existing ones and to engineer new ones for a variety of biotechnological applications. We have discovered and characterized an artificial Thr‐rich protein–protein interface. This novel interface demonstrates a heretofore unknown property of Thr‐rich surfaces: mediating protein–protein interactions.

The loss of proteostasis over the life course is associated with a wide range of debilitating degenerative diseases and is a central hallmark of human aging. When left unchecked, proteins that are intrinsically disordered can pathologically aggregate into highly ordered fibrils, plaques, and tangles (termed amyloids), which are associated with countless disorders such as Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, cancer, and even certain viral infections. However, despite significant advances in protein folding and solution biophysics techniques, determining the molecular cause of these conditions in humans has remained elusive. This has been due, in part, to recent discoveries showing that soluble protein oligomers, not insoluble fibrils or plaques, drive the majority of pathological processes. This has subsequently led researchers to focus instead on heterogeneous and often promiscuous protein oligomers. Unfortunately, significant gaps remain in how to prepare, model, experimentally corroborate, and extract amyloid oligomers relevant to human disease in a systematic manner. This review will report on each of these techniques and their successes and shortcomings in an attempt to standardize comparisons between protein oligomers across disciplines, especially in the context of neurodegeneration. By standardizing multiple techniques and identifying their common overlap, a clearer picture of the soluble neuropathological aggresome can be constructed and used as a baseline for studying human disease and aging.

A reconstruction algorithm for partially coherent x-ray computed tomography (XCT) including Fresnel diffraction is developed and applied to an optical fiber. The algorithm is applicable to a high-resolution tube-based laboratory-scale x-ray tomography instrument. The computing time is only a few times longer than the projective counterpart. The algorithm is used to reconstruct, with projections and diffraction, a tilt series acquired at the micrometer scale of a graded-index optical fiber using maximum likelihood and a Bayesian method based on the work of Bouman and Sauer. The inclusion of Fresnel diffraction removes some reconstruction artifacts and use of a Bayesian prior probability distribution removes others, resulting in a substantially more accurate reconstruction.

Physics-aware machine learning (PAML) has proven to be successful in re- trieving object in a low-photon imaging condition. In this study, we take an information theoretic approach to study maximum amount of information (i.e., the imaging capacity) that can be reliably retrieved under photon-starved imaging condition. We formulate a sim- ple problem to derive the imaging capacity using Monte Carlo approach: small integrated circuit objects (made out of two different type of materials) imaged with monochromatic x-rays. The simple problem is useful in that the entropies for priors and measurements can be explicitly computed. It is a simple version of the imaging system proposed in Ref. [3]. The constructed problem will help us understand the limit on photon budget, information gain associated with varying number of scan angles, and optimal PAML algorithm that can approach the theoretic imaging capacity.

Coherent states are used to prepare a crystal using the Atomic Frequency Comb protocol for quantum memory. Here, semiclassical theory is developed and compared to experimental photon echoes of a coherent pulse.

Photon echoes in rare-earth-doped crystals are studied to understand the challenges of making broadband quantum memories using the atomic frequency comb (AFC) protocol in systems with hyperfine structure. The hyperfine structure of Pr^{3+} poses an obstacle to this goal because frequencies associated with the hyperfine transitions change the simple picture of modulation at an externally imposed frequency. The current work focuses on the intermediate case where the hyperfine spacing is comparable to the comb spacing, a challenging regime that has recently been considered. Operating in this regime may facilitate storing quantum information over a larger spectral range in such systems. In this work, we prepare broadband AFCs using optical combs with tooth spacings ranging from 1 MHz to 16 MHz in fine steps, and measure transmission spectra and photon echoes for each. We predict the spectra and echoes theoretically using the optical combs as input to either a rate equation code or a density matrix code, which calculates the redistribution of populations. We then use the redistributed populations as input to a semiclassical theory using the frequency-dependent dielectric function. The two sets of predictions each give a good, but different account of the photon echoes., 33 pages, 8 figures

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures---in the root-mean-square deviations of the C$_{\alpha}$ atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography., Comment: 9 pages, 4 figures

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (specifically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features., Comment: 7 pages, 5 figures

Praseodymium-doped yttrium orthosilicate prepared by an optical frequency comb is a candidate for quantum memory, although its hyperfine splitting poses challenges. Transmission after preparation is predicted using the density matrix.

Patient-specific computational modeling is increasingly used to assist with visualization, planning, and execution of medical treatments. This trend is placing more reliance on medical imaging to provide accurate representations of anatomical structures. Digital image analysis is used to extract anatomical data for use in clinical assessment/planning. However, the presence of image artifacts, whether due to interactions between the physical object and the scanning modality or the scanning process, can degrade image accuracy. The process of extracting anatomical structures from medical images introduces additional sources of variability, e.g., when thresholding or when eroding along apparent edges of biological structures. An estimate of the uncertainty associated with extracting anatomical data from medical images would therefore assist with assessing the reliability of patient-specific treatment plans. To this end, two image datasets were developed and analyzed using standard image analysis procedures. The first dataset was developed by performing a “virtual voxelization” of a computer-aided design (CAD) model of a sphere, representing the idealized scenario of no error in the image acquisition and reconstruction algorithms (i.e., a perfect scan). The second dataset was acquired by scanning three spherical balls using a laboratory-grade computed tomography (CT) scanner. For the idealized sphere, the error in sphere diameter was less than or equal to 2% if five or more voxels were present across the diameter. The measurement error degraded to approximately 4% for a similar degree of voxelization of the physical phantom. The adaptation of established thresholding procedures to improve segmentation accuracy was also investigated.

There exist only two case reports to date of open cardiac defibrillation with deep brain stimulator system (DBS) implantation. We report a 64-year-old male with DBS system in place for essential tremor who underwent cardiac defibrillation after cardiac arrest. Afterwards, his device impedances were all high and his tremor symptoms returned. Both problems resolved with implantation of a new generator and required no changes to the intracranial leads or extension cables. This is significantly different from the two previous reports. One included a significantly different DBS system relying on transcutaneous RF transmission and reported a lesioning effect after cardioversion. The other utilized a modern DBS system but reported damage to the generator and intracranial leads. We report that only the generator sustained damage, and that there were no intracranial changes that occurred.

Fundamental limits for the calculation of scattering corrections within X-ray computed tomography (CT) are found within the independent atom approximation from an analysis of the cross sections, CT geometry, and the Nyquist sampling theorem, suggesting large reductions in computational time compared to existing methods. By modifying the scatter by less than 1 %, it is possible to treat some of the elastic scattering in the forward direction as inelastic to achieve a smoother elastic scattering distribution. We present an analysis showing that the number of samples required for the smoother distribution can be greatly reduced. We show that fixed forced detection can be used with many fewer points for inelastic scattering, but that for pure elastic scattering, a standard Monte Carlo calculation is preferred. We use smoothing for both elastic and inelastic scattering because the intrinsic angular resolution is much poorer than can be achieved for projective tomography. Representative numerical examples are given.

The low-latency requirements of a practical loophole-free Bell test preclude time-consuming post-processing steps that are often used to improve the statistical quality of a physical random number generator (RNG). Here we demonstrate a post-processing-free RNG that produces a random bit within 2.4(2) ns of an input trigger. We use weak feedback to eliminate long-term drift, resulting in 24 hour operation with output that is statistically indistinguishable from a Bernoulli process. We quantify the impact of the feedback on the predictability of the output as less than 6.4×10(−7) and demonstrate the utility of the Allan variance as a tool for characterizing non-idealities in RNGs.

Intrinsically disordered proteins (IDPs) and regions (IDRs) make up a significant part of the proteome and facilitate a wide range of physiological and pathological functions that are only beginning to be understood. As such, they are highly attractive targets for drug development and bioengineering. However, their inability to adopt well-defined structures provides significant obstacles for developing ligands that regulate their behaviors. In this chapter, we review how the conformational flexibility of IDPs and their propensity to phase separate make them tractable targets for small-molecule manipulation. We also describe both theoretical and experimental approaches to characterize disordered proteins, including novel thermodynamic and single-molecule techniques that help identify complimentary partners of IDPs and their ability to shift protein ensembles toward preferred conformations.