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This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure (ES) methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green’s function based many-body perturbation theory, wave-function based and stochastic ES approaches, relativistic effects and semiempirical ES theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the ES, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for ES based modeling from the vantage point of industry environments. Overall, the field of ES software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

Recent research has shown that wavefunction evolution in real and imaginary time can generate quantum subspaces with significant utility for obtaining accurate ground state energies. Inspired by these methods, we propose combining quantum subspace techniques with the variational quantum eigensolver (VQE). In our approach, the parameterized quantum circuit is divided into a series of smaller subcircuits. The sequential application of these subcircuits to an initial state generates a set of wavefunctions that we use as a quantum subspace to obtain high-accuracy groundstate energies. We call this technique the circuit subspace variational quantum eigensolver (CSVQE) algorithm. By benchmarking CSVQE on a range of quantum chemistry problems, we show that it can achieve significant error reduction in the best case compared to conventional VQE, particularly for poorly optimized circuits, greatly improving convergence rates. Furthermore, we demonstrate that when applied to circuits trapped at local minima, CSVQE can produce energies close to the global minimum of the energy landscape, making it a potentially powerful tool for diagnosing local minima.

Mycobacterium tuberculosis (Mtb) releases the unusual terpene nucleoside 1-tuberculosinyladenosine (1-TbAd) to block lysosomal function and promote survival in human macrophages. Using conventional approaches, we found that genes Rv3377c and Rv3378c, but not Rv3376, were necessary for 1-TbAd biosynthesis. Here, we introduce linear models for mass spectrometry (limms) software as a next-generation lipidomics tool to study the essential functions of lipid biosynthetic enzymes on a whole-cell basis. Using limms, whole-cell lipid profiles deepened the phenotypic landscape of comparative mass spectrometry experiments and identified a large family of approximately 100 terpene nucleoside metabolites downstream of Rv3378c. We validated the identity of previously unknown adenine-, adenosine-, and lipid-modified tuberculosinol-containing molecules using synthetic chemistry and collisional mass spectrometry, including comprehensive profiling of bacterial lipids that fragment to adenine. We tracked terpene nucleoside genotypes and lipid phenotypes among Mycobacterium tuberculosis complex (MTC) species that did or did not evolve to productively infect either human or nonhuman mammals. Although 1-TbAd biosynthesis genes were thought to be restricted to the MTC, we identified the locus in unexpected species outside the MTC. Sequence analysis of the locus showed nucleotide usage characteristic of plasmids from plant-associated bacteria, clarifying the origin and timing of horizontal gene transfer to a pre-MTC progenitor. The data demonstrated correlation between high level terpene nucleoside biosynthesis and mycobacterial competence for human infection, and 2 mechanisms of 1-TbAd biosynthesis loss. Overall, the selective gain and evolutionary retention of tuberculosinyl metabolites in modern species that cause human TB suggest a role in human TB disease, and the newly discovered molecules represent candidate disease-specific biomarkers.

Many autism spectrum disorder (ASD)-associated genes act as transcriptional regulators (TRs). Chromatin immunoprecipitation sequencing (ChIP-seq) was used to identify the regulatory targets of ARID1B, BCL11A, FOXP1, TBR1, and TCF7L2, ASD-associated TRs in the developing human and mouse cortex. These TRs shared substantial overlap in the binding sites, especially within open chromatin. The overlap within a promoter region, 1-2,000 bp upstream of the transcription start site, was highly predictive of brain-expressed genes. This signature was observed in 96 out of 102 ASD-associated genes. In vitro CRISPRi against ARID1B and TBR1 delineated downstream convergent biology in mouse cortical cultures. After 8 days, NeuN+ and CALB+ cells were decreased, GFAP+ cells were increased, and transcriptomic signatures correlated with the postmortem brain samples from individuals with ASD. We suggest that functional convergence across five ASD-associated TRs leads to shared neurodevelopmental outcomes of haploinsufficient disruption.

BACKGROUND: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS: We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P

Belief polarization represents a puzzling and important dynamic in belief updating. There is growing awareness that belief polarization can be Bayesian. We provide pre-registered experimental evidence that beliefs can polarize when people receive conflicting testimony from two groups of sources if they have different beliefs about which group's members have greater independence in terms of the factors which affect their testimony. We show this is predicted by a Bayesian Network model of belief updating.

With a growing demand for time-domain simulations of correlated many-body systems, the development of efficient and stable integration schemes for the time-dependent Schrödinger equation is of keen interest in modern electronic structure theory. In this work, we present two approaches for the formation of the quantum propagator for time-dependent equation-of-motion coupled cluster theory based on the Chebyshev and Arnoldi expansions of the complex, nonhermitian matrix exponential, respectively. The proposed algorithms are compared with the short-iterative Lanczos method of Cooper et al. [J. Phys. Chem. A 2021 125, 5438-5447], the fourth-order Runge-Kutta method, and exact dynamics for a set of small but challenging test problems. For each of the cases studied, both of the proposed integration schemes demonstrate superior accuracy and efficiency relative to the reference simulations.

The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

We use local diffusion maps to assess the quality of two types of collective variables (CVs) for a recently published hydrogen combustion benchmark dataset1 that contains ab initio molecular dynamics (MD) trajectories and normal modes along minimum energy paths. This approach was recently advocated in2 for assessing CVs and analyzing reactions modeled by classical MD simulations. We report the effectiveness of this approach to molecular systems modeled by quantum ab initio MD. In addition to assessing the quality of CVs, we also use global diffusion maps to perform committor analysis as proposed in.2 We show that the committor function obtained from the global diffusion map allows us to identify transition regions of interest in several hydrogen combustion reaction channels.

With the growing reliance of modern supercomputers on accelerator-based architecture such a graphics processing units (GPUs), the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development GPU accelerated, distributed memory algorithms for many modern electronic structure methods, the primary focus of GPU development for Gaussian basis atomic orbital methods has been for shared memory systems with only a handful of examples pursing massive parallelism. In the present work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact exchange matrices for hybrid Kohn-Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.

We present a method to compute the many-body real-time Green's function using an adaptive variational quantum dynamics simulation approach. The real-time Green's function involves the time evolution of a quantum state with one additional electron with respect to the ground state wave function that is first expressed as a linear-linear combination of state vectors. The real-time evolution and the Green's function are obtained by combining the dynamics of the individual state vectors in a linear combination. The use of the adaptive protocol enables us to generate compact ansatzes on-the-fly while running the simulation. In order to improve the convergence of spectral features, Padé approximants are applied to obtain the Fourier transform of the Green's function. We demonstrate the evaluation of the Green's function on an IBM Q quantum computer. As a part of our error mitigation strategy, we develop a resolution-enhancing method that we successfully apply on the noisy data from the real-quantum hardware.

The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr2 (24e, 30o) with 106, 107, and 3 × 108 variational determinants on up to 16 384 CPUs. To the best of the authors' knowledge, this is the largest variational ASCI calculation to date.

For many computational chemistry packages, being able to efficiently and effectively scale across an exascale cluster is a heroic feat. Collective experience from the Department of Energy's Exascale Computing Project suggests that achieving exascale performance requires far more planning, design, and optimization than scaling to petascale. In many cases, entire rewrites of software are necessary to address fundamental algorithmic bottlenecks. This in turn requires a tremendous amount of resources and development time, resources that cannot reasonably be afforded by every computational science project. It thus becomes imperative that computational science transition to a more sustainable paradigm. Key to such a paradigm is modular software. While the importance of modular software is widely recognized, what is perhaps not so widely appreciated is the effort still required to leverage modular software in a sustainable manner. The present manuscript introduces PluginPlay, https://github.com/NWChemEx-Project/PluginPlay, an inversion-of-control framework designed to facilitate developing, maintaining, and sustaining modular scientific software packages. This manuscript focuses on the design aspects of PluginPlay and how they specifically influence the performance of the resulting package. Although, PluginPlay serves as the framework for the NWChemEx package, PluginPlay is not tied to NWChemEx or even computational chemistry. We thus anticipate PluginPlay to prove to be a generally useful tool for a number of computational science packages looking to transition to the exascale.

Quantum computers provide alternative avenues to access ground and excited state properties of systems difficult to simulate on classical hardware. New approaches using subspaces generated by real-time evolution have shown efficiency in extracting eigenstate information, but the full capabilities of such approaches are still not understood. In recent work, we developed the variational quantum phase estimation (VQPE) method, a compact and efficient real-time algorithm to extract eigenvalues using quantum hardware. Here we build on that work by theoretically and numerically exploring a generalized Krylov scheme where the Krylov subspace is constructed through a parametrized real-time evolution, applicable to the VQPE algorithm as well as others. We establish an error bound that justifies the fast convergence of our spectral approximation. We also derive how the overlap with high energy eigenstates becomes suppressed from real-time subspace diagonalization and we visualize the process that shows the signature phase cancellations at specific eigenenergies. We investigate various algorithm implementations and consider performance when stochasticity is added to the target Hamiltonian in the form of spectral statistics. To demonstrate the practicality of such real-time evolution methods, we discuss its application to fundamental problems in quantum computation such as electronic structure predictions for strongly correlated systems.

When people “fail to deny” unflattering claims, it is commonly taken to imply they are true. Yet, the ‘argument from ignorance’ – arguing in favour of something due to a lack of evidence against it – is often deemed a fallacy. Why is abduction from missing evidence permissible in some cases, but not others? We present a framework of factors which disambiguate these cases, using a Bayesian Network model. We suggest that a source’s silence often reflects a latent conflict between their motives for what they want their audience to believe, and complying with external constraints on their speech, like the need to be accurate. In these cases, silence implies that the source does not believe that what they would like to say is true, which licenses a probabilistic inference that it is false. We present data from two studies suggesting people infer from silence like this.

Iron-sulfur clusters comprise an important functional motif in the catalytic centers of biological systems, capable of enabling important chemical transformations at ambient conditions. This remarkable capability derives from a notoriously complex electronic structure that is characterized by a high density of states that is sensitive to geometric changes. The spectral sensitivity to subtle geometric changes has received little attention from correlated, large active space calculations, owing partly to the exceptional computational complexity for treating these large and correlated systems accurately. To provide insight into this aspect, we report the first Complete Active Space Self Consistent Field (CASSCF) calculations for different geometries of the [Fe(II/III)4S4(SMe)4]-2 clusters using two complementary, correlated solvers: spin-pure Adaptive Sampling Configuration Interaction (ASCI) and Density Matrix Renormalization Group (DMRG). We find that the previously established picture of a double-exchange driven magnetic structure, with minute energy gaps (

White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value

Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH), and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters, including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

The numerical integration of the exchange–correlation (XC) potential is one of the primary computational bottlenecks in Gaussian basis set Kohn–Sham density functional theory (KS-DFT). To achieve optimal performance and accuracy, care must be taken in this numerical integration to preserve local sparsity as to allow for near linear weak scaling with system size. This leads to an integration scheme with several performance critical kernels which must be hand optimized for each architecture of interest. As the set of available accelerator hardware goes more diverse, a key challenge for developers of KS-DFT software is to maintain performance portability across a wide range of computational architectures. In this work, we examine a modular software design pattern which decouples the implementation details of performance critical kernels from the expression of high-level algorithmic workflows in a device-agnostic language such as C++; thus allowing for developers to target existing and emerging accelerator hardware within a single code base. We consider the efficacy of such a design pattern in the numerical integration of the XC potential by demonstrating its ability to achieve performance portability across a set of accelerator architectures which are representative of those on current and future U.S. Department of Energy Leadership Computing Facilities.

Using parallel file systems efficiently is a tricky problem due to inter-dependencies among multiple layers of I/O software, including high-level I/O libraries (HDF5, netCDF, etc.), MPI-IO, POSIX, and file systems (GPFS, Lustre, etc.). Profiling tools such as Darshan collect traces to help understand the I/O performance behavior. However, there are significant gaps in analyzing the collected traces and then applying tuning options offered by various layers of I/O software. Seeking to connect the dots between I/O bottleneck detection and tuning, we propose DXT Explorer, an interactive log analysis tool. In this paper, we present a case study using our interactive log analysis tool to identify and apply various I/O optimizations. We report an evaluation of performance improvement achieved for four I/O kernels extracted from science applications.

The excellent mechanical properties of high-entropy alloys (HEAs) make them promising materials for advances in science and technology. However, the underlying mechanism of plastic deformation is not well understood. In situ experiments are urgently required to provide a fundamental understanding of the plastic deformation under high pressure. We performed in situ synchrotron X-ray diffraction (XRD) experiments to study compression deformation behavior of the HEA MoNbTaVW in a radial diamond anvil cell (rDAC). Our results show that the strength and ratio of the stress-to-shear modulus values are ~1.5 and 3 times that of pure tungsten (W), respectively. MoNbTaVW showed plastic deformation above 5 GPa and displayed a much stronger texture. We found that the active dislocation behavior is mainly responsible for the high strength in MoNbTaVW under compression. This unique technique opens a new avenue to investigate the in situ mechanical properties and their mechanism in other types of HEAs.

Iron-sulfur clusters comprise an important functional motif of the catalyticcenters of biological systems, capable of enabling important chemicaltransformations at ambient conditions. This remarkable capability derives froma notoriously complex electronic structure that is characterized by a highdensity of states that is sensitive to geometric changes. The spectralsensitivity to subtle geometric changes has received little attention fromfully-correlated calculations, owing partly to the exceptional computationalcomplexity for treating these large and correlated systems accurately. Toprovide insight into this aspect, we report the first Complete Active SpaceSelf Consistent Field (CASSCF) calculations for different geometries ofcubane-based clusters using two complementary, fully-correlated solvers:spin-pure Adaptive Sampling Configuration Interaction (ASCI) and Density MatrixRenormalization Group (DMRG). We find that the previously established pictureof a double-exchange driven magnetic structure, with minute energy gaps (< 1mHa) between consecutive spin states, has a weak dependence on the underlyinggeometry. However, the spin gap between the lowest singlet and the highest spinstates is strongly geometry dependent, changing by an order of magnitude uponslight deformations that are still within biologically relevant parameters. TheCASSCF orbital optimization procedure, using active spaces as large as 86electrons in 52 orbitals, was found to reduce this gap by a factor of twocompared to typical mean-field orbital approaches. Our results clearlydemonstrate the need for performing highly correlated calculations to unveilthe challenging electronic structure of these complex catalytic centers.

Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.

3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from 15 whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the pipeline as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development.

The central importance of large-scale eigenvalue problems in scientific computation necessitates the development of massively parallel algorithms for their solution. Recent advances in dense numerical linear algebra have enabled the routine treatment of eigenvalue problems with dimensions on the order of hundreds of thousands on the world's largest supercomputers. In cases where dense treatments are not feasible, Krylov subspace methods offer an attractive alternative due to the fact that they do not require storage of the problem matrices. However, demonstration of scalability of either of these classes of eigenvalue algorithms on computing architectures capable of expressing massive parallelism is non-trivial due to communication requirements and serial bottlenecks, respectively. In this work, we introduce the SISLICE method: a parallel shift-invert algorithm for the solution of the symmetric self-consistent field (SCF) eigenvalue problem. The SISLICE method drastically reduces the communication requirement of current parallel shift-invert eigenvalue algorithms through various shift selection and migration techniques based on density of states estimation and k-means clustering, respectively. This work demonstrates the robustness and parallel performance of the SISLICE method on a representative set of SCF eigenvalue problems and outlines research directions that will be explored in future work.

MagellanMapper is a software suite designed for visual inspection and end-to-end automated processing of large-volume, 3D brain imaging datasets in a memory-efficient manner. The rapidly growing number of large-volume, high-resolution datasets necessitates visualization of raw data at both macro- and microscopic levels to assess the quality of data, as well as automated processing to quantify data in an unbiased manner for comparison across a large number of samples. To facilitate these analyses, MagellanMapper provides both a graphical user interface for manual inspection and a command-line interface for automated image processing. At the macroscopic level, the graphical interface allows researchers to view full volumetric images simultaneously in each dimension and to annotate anatomical label placements. At the microscopic level, researchers can inspect regions of interest at high resolution to build ground truth data of cellular locations such as nuclei positions. Using the command-line interface, researchers can automate cell detection across volumetric images, refine anatomical atlas labels to fit underlying histology, register these atlases to sample images, and perform statistical analyses by anatomical region. MagellanMapper leverages established open-source computer vision libraries and is itself open source and freely available for download and extension. © 2020 Wiley Periodicals LLC. Basic Protocol 1: MagellanMapper installation Alternate Protocol: Alternative methods for MagellanMapper installation Basic Protocol 2: Import image files into MagellanMapper Basic Protocol 3: Region of interest visualization and annotation Basic Protocol 4: Explore an atlas along all three dimensions and register to a sample brain Basic Protocol 5: Automated 3D anatomical atlas construction Basic Protocol 6: Whole-tissue cell detection and quantification by anatomical label Support Protocol: Import a tiled microscopy image in proprietary format into MagellanMapper.

The Chronus Quantum (ChronusQ) software package is an open source (under the GNU General Public License v2) software infrastructure which targets the solution of challenging problems that arise in ab initio electronic structure theory. Special emphasis is placed on the consistent treatment of time dependence and spin in the electronic wave function, as well as the inclusion of relativistic effects in said treatments. In addition, ChronusQ provides support for the inclusion of uniform finite magnetic fields as external perturbations through the use of gauge-including atomic orbitals. ChronusQ is a parallel electronic structure code written in modern C++ which utilizes both message passing implementation and shared memory (OpenMP) parallelism. In addition to the examination of the current state of code base itself, a discussion regarding ongoing developments and developer contributions will also be provided. This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Ab Initio Electronic Structure Methods Electronic Structure Theory > Density Functional Theory.

This paper describes the rapidly evolving and unusual supernova LSQ13ddu, discovered by the La Silla-QUEST survey. LSQ13ddu displayed a rapid rise of just 4.8 ± 0.9 d to reach a peak brightness of −19.70 ± 0.02 mag in the LSQgr band. Early spectra of LSQ13ddu showed the presence of weak and narrow He I features arising from interaction with circumstellar material (CSM). These interaction signatures weakened quickly, with broad features consistent with those seen in stripped-envelope SNe becoming dominant around two weeks after maximum. The narrow He I velocities are consistent with the wind velocities of luminous blue variables but its spectra lack the typically seen hydrogen features. The fast and bright early light curve is inconsistent with radioactive 56Ni powering but can be explained through a combination of CSM interaction and an underlying 56Ni decay component that dominates the later time behaviour of LSQ13ddu. Based on the strength of the underlying broad features, LSQ13ddu appears deficient in He compared to standard SNe Ib.

Effective central sensory integration of visual, vestibular, and proprioceptive information is required to promote adaptability in response to changes in the environment during postural control. Patients with a lesion in the posterior parietal cortex (PPC) have an impaired ability to form an internal representation of body position, an important factor for postural control and adaptation. Suppression of PPC excitability has also been shown to decrease postural stability in some contexts. As of yet, it is unknown whether stimulation of the PPC may influence postural adaptation. This investigation aimed to identify whether transcranial direct current stimulation (tDCS) of the bilateral PPC could modulate postural adaptation in response to a bipedal incline postural adaptation task. Using young, healthy subjects, we delivered tDCS over bilateral PPC followed by bouts of inclined stance (incline-interventions). Analysis of postural after-effects identified differences between stimulation conditions for maximum lean after-effect (LAE; p = 0.005) as well as a significant interaction between condition and measurement period for the average position (p = 0.03). We identified impaired postural adaptability following both active stimulation conditions. Results reinforce the notion that the PPC is involved in motor adaptation and extend this line of research to the realm of standing posture. The results further highlight the role of the bilateral PPC in utilizing sensory feedback to update one's internal representation of verticality and demonstrates the diffuse regions of the brain that are involved in postural control and adaptation. This information improves our understanding of the role of the cortex in postural control, highlighting the potential for the PPC as a target for sensorimotor rehabilitation.

Gait is one of the fundamental behaviors we use to interact with the world. The functionality of the locomotor system is thus related to enriching interactions with our environment. The posterior parietal cortex (PPC) has been found to contribute to motor adaptation during both visuomotor and postural adaptation tasks. Additionally, structural or functional deficits of the PPC lead to impairments in gaits such as shortened steps and increased step width. Based on the aforementioned roles of the PPC, and the importance of gait adaptability, the current investigation sought to identify the role of the PPC in gait adaptation. To achieve this, we performed transcranial direct current stimulation (tDCS) over the bilateral PPC before performing a split-belt treadmill gait adaptation paradigm. We used three stimulation conditions in a within-subject design. tDCS was administered in a randomized and double-blinded order. Following each stimulation session, subjects first performed baseline walking with both belts running at the same speed. Then, subjects walked for 15 min on an uncoupled treadmill, with the belts being driven at a 3:1 speed ratio. Last, they returned to normal (i.e., tied-belt) walking for 5 min. Results from 15 young and healthy subjects identified that subjects required more steps to adapt to split-belt walking following the suppression of the left hemisphere PPC, contralateral to the fast belt. Furthermore, while suppression of the left hemisphere PPC did not increase the number of steps required to re-adapt to tied-belt walking, this condition did lead to increased magnitude of after-effects. Together, these findings indicate that the PPC is involved in locomotor adaptation. These results support previous literature regarding the upper body or postural adaptation and extend these findings to the realm of gait. Results highlight the PPC as a potential target for neurorehabilitation designed to improve gait adaptability.

The predominance of Kohn-Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn-Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.

We describe using the Newton Krylov method to solve the coupled cluster equation. The method uses a Krylov iterative method to compute the Newton correction to the approximate coupled cluster amplitude. The multiplication of the Jacobian with a vector, which is required in each step of a Krylov iterative method such as the Generalized Minimum Residual (GMRES) method, is carried out through a finite difference approximation, and requires an additional residual evaluation. The overall cost of the method is determined by the sum of the inner Krylov and outer Newton iterations. We discuss the termination criterion used for the inner iteration and show how to apply pre-conditioners to accelerate convergence. We will also examine the use of regularization technique to improve the stability of convergence and compare the method with the widely used direct inversion of iterative subspace (DIIS) methods through numerical examples.

The Green's function coupled cluster (GFCC) method, originally proposed in the early 1990s, is a powerful many-body tool for computing and analyzing the electronic structure of molecular and periodic systems, especially when electrons of the system are strongly correlated. However, in order for the GFCC to become a method that may be routinely used in the electronic structure calculations, robust numerical techniques and approximations must be employed to reduce its extremely high computational overhead. In our recent studies, it has been demonstrated that the GFCC equations can be solved directly in the frequency domain using iterative linear solvers, which can be easily distributed in a massively parallel environment. In the present work, we demonstrate a successful application of model-order-reduction (MOR) techniques in the GFCC framework. Briefly speaking, for a frequency regime of interest that requires high-resolution descriptions of spectral function, instead of solving the GFCC linear equation of full dimension for every single frequency point of interest, an efficiently solvable linear system model of a reduced dimension may be built upon projecting the original GFCC linear system onto a subspace. From this reduced order model is obtained a reasonable approximation to the full dimensional GFCC linear equations in both interpolative and extrapolative spectral regions. Here, we show that the subspace can be properly constructed in an iterative manner from the auxiliary vectors of the GFCC linear equations at some selected frequencies within the spectral region of interest. During the iterations, the quality of the subspace, as well as the linear system model, can be systematically improved. The method is tested in this work in terms of the efficiency and accuracy of computing spectral functions for some typical molecular systems such as carbon monoxide, 1,3-butadiene, benzene, and adenine. To reach the same level of accuracy as that of the original GFCC method, the application of MOR in the GFCC method is able to significantly lower the original computational cost for the aforementioned molecules in designated frequency regimes. As a byproduct, the reduced order model obtained by this method is found to provide a high-quality initial guess, which improves the convergence rate for the existing iterative linear solver.

Quaternion symmetry is ubiquitous in the physical sciences. As such, muchwork has been afforded over the years to the development of efficient schemesto exploit this symmetry using real and complex linear algebra. Recent yearshave also seen many advances in the formal theoretical development ofexplicitly quaternion linear algebra with promising applications in imageprocessing and machine learning. Despite these advances, there do not currentlyexist optimized software implementations of quaternion linear algebra. Theleverage of optimized linear algebra software is crucial in the achievement ofhigh levels of performance on modern computing architectures, and thus providesa central tool in the development of high-performance scientific software. Inthis work, a case will be made for the efficacy of high-performance quaternionlinear algebra software for appropriate problems. In this pursuit, an optimizedsoftware implementation of quaternion matrix multiplication will be presentedand will be shown to outperform a vendor tuned implementation for the analogouscomplex matrix operation. The results of this work pave the path for furtherdevelopment of high-performance quaternion linear algebra software which willimprove the performance of the next generation of applicable scientificapplications.

There has been virtual explosion of studies published in cognitive neuroscience primarily due to increased accessibility to neuroimaging methods, which has led to different approaches in interpretation. This review seeks to synthesize both developmental approaches and more recent views that consider neuroimaging. The ways in which Neuronal Recycling, Neural Reuse, and Language as Shaped by the Brain perspectives seek to clarify the brain bases of cognition will be addressed. Neuroconstructivism as an additional explanatory framework which seeks to bind brain and cognition to development will also be presented. Despite sharing similar goals, the four approaches to understanding how the brain is related to cognition have generally been considered separately. However, we propose that all four perspectives argue for a form of Emergentism in which combinations of smaller elements can lead to a greater whole. This discussion seeks to provide a synthesis of these approaches that leads to the emergence of a theory itself. We term this new synthesis Neurocomputational Emergentism (or Neuromergentism for short).

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator's eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.

BackgroundUncomplicated skin abscesses are common, yet the appropriate management of the condition in the era of community-associated methicillin-resistant Staphylococcus aureus (MRSA) is unclear.MethodsWe conducted a multicenter, prospective, double-blind trial involving outpatient adults and children. Patients were stratified according to the presence of a surgically drainable abscess, abscess size, the number of sites of skin infection, and the presence of nonpurulent cellulitis. Participants with a skin abscess 5 cm or smaller in diameter were enrolled. After abscess incision and drainage, participants were randomly assigned to receive clindamycin, trimethoprim-sulfamethoxazole (TMP-SMX), or placebo for 10 days. The primary outcome was clinical cure 7 to 10 days after the end of treatment.ResultsWe enrolled 786 participants: 505 (64.2%) were adults and 281 (35.8%) were children. A total of 448 (57.0%) of the participants were male. S. aureus was isolated from 527 participants (67.0%), and MRSA was isolated from 388 (49.4%). Ten days after therapy in the intention-to-treat population, the cure rate among participants in the clindamycin group was similar to that in the TMP-SMX group (221 of 266 participants [83.1%] and 215 of 263 participants [81.7%], respectively; P=0.73), and the cure rate in each active-treatment group was higher than that in the placebo group (177 of 257 participants [68.9%], P

Although small molecules shed from pathogens are widely used to diagnose infection, such tests have not been widely implemented for tuberculosis. Here we show that the recently identified compound, 1-tuberculosinyladenosine (1-TbAd), accumulates to comprise >1% of all Mycobacterium tuberculosis lipid. In vitro and in vivo, two isomers of TbAd were detected that might serve as infection markers. Using mass spectrometry and nuclear magnetic resonance, we established the structure of the previously unknown molecule, N(6)-tuberculosinyladenosine (N(6)-TbAd). Its biosynthesis involves enzymatic production of 1-TbAd by Rv3378c followed by conversion to N(6)-TbAd via the Dimroth rearrangement. Intact biosynthetic genes are observed only within M. tuberculosis complex bacteria, and TbAd was not detected among other medically important pathogens, environmental bacteria, and vaccine strains. With no substantially similar known molecules in nature, the discovery and in vivo detection of two abundant terpene nucleosides support their development as specific diagnostic markers of tuberculosis.

BackgroundSkin and skin-structure infections are common in ambulatory settings. However, the efficacy of various antibiotic regimens in the era of community-acquired methicillin-resistant Staphylococcus aureus (MRSA) is unclear.MethodsWe enrolled outpatients with uncomplicated skin infections who had cellulitis, abscesses larger than 5 cm in diameter (smaller for younger children), or both. Patients were enrolled at four study sites. All abscesses underwent incision and drainage. Patients were randomly assigned in a 1:1 ratio to receive either clindamycin or trimethoprim-sulfamethoxazole (TMP-SMX) for 10 days. Patients and investigators were unaware of the treatment assignments and microbiologic test results. The primary outcome was clinical cure 7 to 10 days after the end of treatment.ResultsA total of 524 patients were enrolled (264 in the clindamycin group and 260 in the TMP-SMX group), including 155 children (29.6%). One hundred sixty patients (30.5%) had an abscess, 280 (53.4%) had cellulitis, and 82 (15.6%) had mixed infection, defined as at least one abscess lesion and one cellulitis lesion. S. aureus was isolated from the lesions of 217 patients (41.4%); the isolates in 167 (77.0%) of these patients were MRSA. The proportion of patients cured was similar in the two treatment groups in the intention-to-treat population (80.3% in the clindamycin group and 77.7% in the TMP-SMX group; difference, -2.6 percentage points; 95% confidence interval [CI], -10.2 to 4.9; P=0.52) and in the populations of patients who could be evaluated (466 patients; 89.5% in the clindamycin group and 88.2% in the TMP-SMX group; difference, -1.2 percentage points; 95% CI, -7.6 to 5.1; P=0.77). Cure rates did not differ significantly between the two treatments in the subgroups of children, adults, and patients with abscess versus cellulitis. The proportion of patients with adverse events was similar in the two groups.ConclusionsWe found no significant difference between clindamycin and TMP-SMX, with respect to either efficacy or side-effect profile, for the treatment of uncomplicated skin infections, including both cellulitis and abscesses. (Funded by the National Institute of Allergy and Infectious Diseases and the National Center for Advancing Translational Sciences, National Institutes of Health; ClinicalTrials.gov number, NCT00730028.).

BACKGROUND: As the elderly population continues to expand, it becomes increasingly important to develop treatments to improve wound healing in the elderly. One problem limiting the research is the lack of appropriate animal models for wound healing in elde

BackgroundAs the elderly population continues to expand, it becomes increasingly important to develop treatments to improve wound healing in the elderly. One problem limiting the research is the lack of appropriate animal models for wound healing in elderly patients. We hypothesized that the Klotho mouse of premature aging is a suitable animal model to shed light on many of the biological processes involved in aging skin.MethodsKlotho mice (kl/kl), Klotho-heterozygous mice (kl/+), and wild-type mice (+/+) were wounded, and the area of the wound was measured every 3 days until the wound was healed. To compare the klotho phenotype with wild-type mice, wounds were also harvested at 4 and 7 days after wounding. For histological examination, paraffin-embedded sections were stained with hematoxylin and eosin and Masson trichrome. Collagen expression in the wound was also studied by analyzing messenger RNA using real-time polymerase chain reaction.ResultsKlotho mice showed a significantly slower rate of wound closure compared with Klotho-heterozygous mice and wild-type mice. Histology showed substantial less healing and collagen deposition in the wounds of the Klotho mice. The expression of collagen messenger RNA in Klotho mice was also less than that in heterozygous and wild-type mice. The Klotho mice exhibited significant phenotypic similarities with aged skin, such as atrophy and delayed wound healing.ConclusionThese preliminary data suggest that the Klotho mouse may be a model to further investigate wound healing in the elderly.

BackgroundAs the elderly population continues to expand, it becomes increasingly important to develop treatments to improve wound healing in the elderly. One problem limiting the research is the lack of appropriate animal models for wound healing in elderly patients. We hypothesized that the Klotho mouse of premature aging is a suitable animal model to shed light on many of the biological processes involved in aging skin.MethodsKlotho mice (kl/kl), Klotho-heterozygous mice (kl/+), and wild-type mice (+/+) were wounded, and the area of the wound was measured every 3 days until the wound was healed. To compare the klotho phenotype with wild-type mice, wounds were also harvested at 4 and 7 days after wounding. For histological examination, paraffin-embedded sections were stained with hematoxylin and eosin and Masson trichrome. Collagen expression in the wound was also studied by analyzing messenger RNA using real-time polymerase chain reaction.ResultsKlotho mice showed a significantly slower rate of wound closure compared with Klotho-heterozygous mice and wild-type mice. Histology showed substantial less healing and collagen deposition in the wounds of the Klotho mice. The expression of collagen messenger RNA in Klotho mice was also less than that in heterozygous and wild-type mice. The Klotho mice exhibited significant phenotypic similarities with aged skin, such as atrophy and delayed wound healing.ConclusionThese preliminary data suggest that the Klotho mouse may be a model to further investigate wound healing in the elderly.

Rapamycin, a specific inhibitor for mTOR complex 1, is an FDA-approved immunosuppressant for organ transplant. Recent developments have raised the prospect of using rapamycin to treat cancer or diabetes and to delay aging. It is therefore important to asse

The regulated recruitment and differentiation of multipotent bone marrow-derived cells (BMDCs) to sites of injury are critical for efficient wound healing. Previously we demonstrated that sustained expression of HOXA3 both accelerated wound healing and promoted angiogenesis in diabetic mice. In this study, we have used green fluorescent protein-positive bone marrow chimeras to investigate the effect of HOXA3 expression on recruitment of BMDCs to wounds. We hypothesized that the enhanced neovascularization induced by HOXA3 is due to enhanced mobilization, recruitment, and/or differentiation of BMDCs. Here we show that diabetic mice treated with HOXA3 displayed a significant increase in both mobilization and recruitment of endothelial progenitor cells compared with control mice. Importantly, we also found that HOXA3-treated mice had significantly fewer inflammatory cells recruited to the wound compared with control mice. Microarray analyses of HOXA3-treated wounds revealed that indeed HOXA3 locally increased expression of genes that selectively promote stem/progenitor cell mobilization and recruitment while also suppressing expression of numerous members of the proinflammatory nuclear factor kappaB pathway, including myeloid differentiation primary response gene 88 and toll-interacting protein. Thus HOXA3 accelerates wound repair by mobilizing endothelial progenitor cells and attenuating the excessive inflammatory response of chronic wounds.

Single crystals of TbRhIn5 were synthesized using the flux growth method. TbRhIn5 adopts the HoCoGa5 structure type and crystallizes in the space group P4/mmm, Z = 1. Lattice parameters are a = 4.6000(6) A, c = 7.4370(11) A, and V = 157.29(6) A3. Transport measurements show that TbRhIn5 is metallic (drho/dT > 0). A sharp antiferromagnetic transition is observed at T(N) = 48 K in the susceptibility data for TbRhIn5, which is highly anisotropic when the field is oriented along the c axis and a-b plane of the crystal and has an average effective moment of 9.72 muB/Tb3+.

Ln3Co4Sn13 (Ln=La, Ce) have been synthesized by flux growth and characterized by single crystal X-ray diffraction. These compounds adopt the Yb3Rh4Sn13-type structure and crystallize in the cubic space group Pm over(3, -) n (No. 223) with Z = 2. Lattice parameters at 298 K are a = 9.6430 ( 6 ) Å, V = 896.68 ( 10 ) Å3, and a = 9.6022 ( 5 ) Å, V = 885.34 ( 8 ) Å3 for the La and Ce analogues, respectively. The crystal structure consists of an Sn-centered icosahedron at the origin of the unit cell, which shares faces with eight Co trigonal prisms and 12 Ln-centered cuboctahedra. Magnetization data at 0.1 T show paramagnetic behavior down to 1.8 K for Ce3Co4Sn13, with μeff = 2.56 μB per Ce3+, while conventional type II superconductivity appears below 2.85 K in the La compound. Electrical resistivity and specific heat data for the La compound show a corresponding sharp superconducting transition at Tc∼2.85 K. The entropy and resistivity data for Ce3Co4Sn13 show the existence of the Kondo effect with a complicated semiconducting-like behavior in the resistivity data. In addition, a large enhanced specific heat coefficient at low T with a low magnetic transition temperature suggests a heavy-fermionic character for the Ce compound. Herein, the structure and physical properties of Ln3Co4Sn13 (Ln=La, Ce) are discussed. © 2006 Elsevier Inc. All rights reserved.