Marker counter-selection via CRISPR/Cas9 co-targeting for efficient generation of genome edited avian cell lines and germ cells.

Efficient isolation of genetically modified cells that are phenotypically indistinguishable from the unmodified cells remains a major technical barrier for the broader utilization of CRISPR/Cas9. Here, we report a novel enrichment approach to select the genome engineered cells by co-targeting a genomically integrated GFP gene along with the endogenous gene of interest (GOI). Using this co-targeting approach, multiple genomic loci were successfully targeted in chicken (DF1) and quail (CEC-32) fibroblast cell lines by transient transfection of Cas9 and guide RNAs (gRNAs). Clonal isolation of co-targeted DF1 cells showed 75% of cell clones had deletion of GFP and biallelic deletion of the GOI. To assess the utility of this approach to generate genome modified animals, we tested it on chicken primordial germ cells (PGCs) expressing GFP by co-targeting with gRNAs against GFP and endogenous ovomucoid (OVM) gene. PGCs enriched for loss of GFP and confirmed for OVM deletion, derived by co-targeting, were injected into Hamburger and Hamilton stage 14-15 chicken embryos, and their ability to migrate to the genital ridge was confirmed. This simple, efficient enrichment approach could easily be applied to the creation of knock-out or edited cell lines or animals.

Coupled cluster Green function: Model involving single and double excitations.

In this paper, we report on the development of a parallel implementation of the coupled-cluster (CC) Green function formulation (GFCC) employing single and double excitations in the cluster operator (GFCCSD). A key aspect of this work is the determination of the frequency dependent self-energy, Σ(ω). The detailed description of the underlying algorithm is provided, including approximations used that preserve the pole structure of the full GFCCSD method, thereby reducing the computational costs while maintaining an accurate character of methodology. Furthermore, for systems with strong local correlation, our formulation reveals a diagonally dominate block structure where as the non-local correlation increases, the block size increases proportionally. To demonstrate the accuracy of our approach, several examples including calculations of ionization potentials for benchmark systems are presented and compared against experiment.

Sign Learning Kink-based (SiLK) Quantum Monte Carlo for molecular systems.

The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initio ground state energies for multiple geometries of the H2O, N2, and F2 molecules. The method is based on Feynman's path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the linear combinations of Slater determinants which are used in the second stage, a conventional QMC simulation. The method is tested using different vector spaces and compared to the results of other quantum chemical methods and to exact diagonalization. Our findings demonstrate that the SiLK method is accurate and reduces or eliminates the minus sign problem.

Probing microhydration effect on the electronic structure of the GFP chromophore anion: Photoelectron spectroscopy and theoretical investigations.

The photophysics of the Green Fluorescent Protein (GFP) chromophore is critically dependent on its local structure and on its environment. Despite extensive experimental and computational studies, there remain many open questions regarding the key fundamental variables that govern this process. One outstanding problem is the role of autoionization as a possible relaxation pathway of the excited state under different environmental conditions. This issue is considered in our work through combined experimental and theoretical studies of microsolvated clusters of the deprotonated p-hydroxybenzylidene-2,3-dimethylimidazolinone anion (HBDI(-)), an analog of the GFP chromophore. Through selective generation of microsolvated structures of predetermined size and subsequent analysis of experimental photoelectron spectra by high level ab initio methods, we are able to precisely identify the structure of the system, establish the accuracy of theoretical data, and provide reliable description of auto-ionization process as a function of hydrogen-bonding environment. Our study clearly illustrates the first few water molecules progressively stabilize the excited state of the chromophore anion against the autodetached neutral state, which should be an important trait for crystallographic water molecules in GFPs that has not been fully explored to date.

Toward enabling large-scale open-shell equation-of-motion coupled cluster calculations: triplet states of ß-carotene.

In this paper we discuss the application of novel parallel implementation of the coupled cluster (CC) and equation-of-motion coupled cluster methods (EOMCC) in calculations of excitation energies of triplet states in ß-carotene. Calculated excitation energies are compared with experimental data, where available. We also provide a detailed description of the new parallel algorithms for iterative CC and EOMCC models involving singles and doubles excitations.

Equation of motion coupled cluster methods for electron attachment and ionization potential in fullerenes C60 and C70.

In both molecular and periodic solid-state systems there is a need for the accurate determination of the ionization potential and the electron affinity for systems ranging from light harvesting polymers and photocatalytic compounds to semiconductors. The development of a Green's function approach based on the coupled cluster (CC) formalism would be a valuable tool for addressing many properties involving many-body interactions along with their associated correlation functions. As a first step in this direction, we have developed an accurate and parallel efficient approach based on the equation of motion-CC technique. To demonstrate the high degree of accuracy and numerical efficiency of our approach we calculate the ionization potential and electron affinity for C60 and C70. Accurate predictions for these molecules are well beyond traditional molecular scale studies. We compare our results with experiments and both quantum Monte Carlo and GW calculations.

Biodegradable and 3D printable lysine functionalized polycaprolactone scaffolds for tissue engineering applications.

Tissue engineering (TE) has sparked interest in creating scaffolds with customizable properties and functional bioactive sites. However, due to limitations in medical practices and manufacturing technologies, it is challenging to replicate complex porous frameworks with appropriate architectures and bioactivity in vitro. To address these challenges, herein, we present a green approach that involves the amino acid (l-lysine) initiated polymerization of ɛ-caprolactone (CL) to produce modified polycaprolactone (PCL) with favorable active sites for TE applications. Further, to better understand the effect of morphology and porosity on cell attachment and proliferation, scaffolds of different geometries with uniform and interconnected pores are designed and fabricated, and their properties are evaluated in comparison with commercial PCL. The scaffold morphology and complex internal micro-architecture are imaged by micro-computed tomography (micro-CT), revealing pore size in the range of ~300-900 µm and porosity ranging from 30 to 70 %, while based on the geometry of scaffolds the compressive strength varied from 143 ± 19 to 214 ± 10 MPa. Additionally, the degradation profiles of fabricated scaffolds are found to be influenced by both the chemical nature and product design, where Lys-PCL-based scaffolds with better porosity and lower crystallinity degraded faster than commercial PCL scaffolds. According to in vitro studies, Lys-PCL scaffolds have produced an environment that is better for cell adhesion and proliferation. Moreover, the scaffold design affects the way cells interact; Lys-PCL with zigzag geometry has demonstrated superior in vitro vitality (>90 %) and proliferation in comparison to other designs. This study emphasizes the importance of enhancing bioactivity while meeting morphology and porosity requirements in the design of scaffolds for tissue engineering applications.

Temperature-dependent predation predicts a more reptilian future.

Diversity increases toward the tropics, but the strength of this pattern diverges with thermoregulatory strategy. Synthesizing over 30,000 species distributions, we quantified patterns of richness in terrestrial vertebrates, and present evidence for a latitudinal gradient of community composition. We observe a two orders of magnitude shift in comparative diversity with temperature, from endothermic mammal and avian dominance near the poles, toward ectothermic reptile and amphibian majority in the tropics. Next, we provide mechanistic support for a corresponding latitudinal gradient of predatory interactions. Using automated video tracking in >4500 trials, we show that differences in thermal sensitivity of locomotion in endothermic predators and ectothermic prey favors endotherms in colder environments and yields theoretically predicted foraging outcomes across thermal conditions, including the number of strikes, the distance traveled, and the time to capture prey. We also present evidence that endotherms use thermal cues to anticipate prey behavior, modulating the impact of temperature. Finally, we integrate theory and data to forecast future patterns of diversity, revealing that as the world get warmer, it will become increasingly reptilian. Overall, our results point toward a broad reorganization of vertebrate diversity with latitude, elevation, and temperature: from endotherm dominance in cold systems toward ectotherm dominance in warm.

Failure in a population: Tauopathy disrupts homeostatic set-points in emergent dynamics despite stability in the constituent neurons.

Homeostatic regulation of neuronal activity is essential for robust computation; set-points, such as firing rate, are actively stabilized to compensate for perturbations. The disruption of brain function central to neurodegenerative disease likely arises from impairments of computationally essential set-points. Here, we systematically investigated the effects of tau-mediated neurodegeneration on all known set-points in neuronal activity. We continuously tracked hippocampal neuronal activity across the lifetime of a mouse model of tauopathy. We were unable to detect effects of disease in measures of single-neuron firing activity. By contrast, as tauopathy progressed, there was disruption of network-level neuronal activity, quantified by measuring neuronal pairwise interactions and criticality, a homeostatically controlled, ideal computational regime. Deviations in criticality correlated with symptoms, predicted underlying anatomical pathology, occurred in a sleep-wake-dependent manner, and could be used to reliably classify an animal's genotype. This work illustrates how neurodegeneration may disrupt the computational capacity of neurobiological systems.

Thermodynamics of tetravalent thorium and uranium complexes from first-principles calculations.

Enthalpies of formation for the ThX4 and UX4 (X = F, Cl, OH) species have been investigated with density functional theory and coupled-cluster methods. ThX4 molecules are all confirmed as tetrahedral, while all UX4 molecules are predicted to adopt D2d symmetry using density functional theory. Multireference coupled cluster approaches confirm the D2d symmetry for UF4. The bonding is mostly ionic, and predicted formation energies for the halogen species show good agreement with experiment. Our calculated hydration energy of UF4 (-54.0 kcal/mol) is in very good agreement with the experimental data (-54.8 kcal/mol). We predict CCSD(T) formation energies of ΔfG[U(OH)4(g)] = -286.3 kcal/mol and ΔfG[U(OH)4(aq)] = -318.7 kcal/mol. ΔfG[U(OH)4(aq)] is 21 kcal/mol less stable than the established experimental thermodynamic data.

Bridging single and multireference coupled cluster theories with universal state selective formalism.

The universal state selective (USS) multireference approach is used to construct new energy functionals which offer a possibility of bridging single and multireference coupled cluster theories (SR/MRCC). These functionals, which can be used to develop iterative and non-iterative approaches, utilize a special form of the trial wavefunctions, which assure additive separability (or size-consistency) of the USS energies in the non-interacting subsystem limit. When the USS formalism is combined with approximate SRCC theories, the resulting formalism can be viewed as a size-consistent version of the method of moments of coupled cluster equations employing a MRCC trial wavefunction. Special cases of the USS formulations, which utilize single reference state specific CC [V. V. Ivanov, D. I. Lyakh, and L. Adamowicz, Phys. Chem. Chem. Phys. 11, 2355 (2009)] and tailored CC [T. Kinoshita, O. Hino, and R. J. Bartlett, J. Chem. Phys. 123, 074106 (2005)] expansions are also discussed.

Transcriptomic cell type structures in vivo neuronal activity across multiple timescales.

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.

Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not in the activity of individual neurons.

The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function central to neurodegenerative disease should reflect impairments of computationally essential set-points. Despite connecting neurodegeneration to functional outcomes, the impact of disease on set-points in neuronal activity is unknown. Here we present a comprehensive, theory-driven investigation of the effects of tau-mediated neurodegeneration on homeostatic set-points in neuronal activity. In a mouse model of tauopathy, we examine 27,000 hours of hippocampal recordings during free behavior throughout disease progression. Contrary to our initial hypothesis that tauopathy would impact set-points in spike rate and variance, we found that cell-level set-points are resilient to even the latest stages of disease. Instead, we find that tauopathy disrupts neuronal activity at the network-level, which we quantify using both pairwise measures of neuron interactions as well as measurement of the network's nearness to criticality, an ideal computational regime that is known to be a homeostatic set-point. We find that shifts in network criticality 1) track with symptoms, 2) predict underlying anatomical and molecular pathology, 3) occur in a sleep/wake dependent manner, and 4) can be used to reliably classify an animal's genotype. Our data suggest that the critical set-point is intact, but that homeostatic machinery is progressively incapable of stabilizing hippocampal networks, particularly during waking. This work illustrates how neurodegenerative processes can impact the computational capacity of neurobiological systems, and suggest an important connection between molecular pathology, circuit function, and animal behavior.

Protosequences in human cortical organoids model intrinsic states in the developing cortex.

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

Communication: Application of state-specific multireference coupled cluster methods to core-level excitations.

The concept of the model space underlying multireference coupled-cluster (MRCC) formulations is a powerful tool to deal with complex correlation effects for various electronic states. Here, we demonstrate that iterative state-specific MRCC methods (SS-MRCC) based on properly defined model spaces can be used to describe core-level excited states even when Hartree-Fock orbitals are utilized. We show that the SS-MRCC models with single and double excitations are comparable in accuracy to high-level single reference equation-of-motion coupled cluster (EOMCC) formalism.

Implementation of the multireference Brillouin-Wigner and Mukherjee's coupled cluster methods with non-iterative triple excitations utilizing reference-level parallelism.

In this paper we discuss the performance of the non-iterative state-specific multireference coupled cluster (SS-MRCC) methods accounting for the effect of triply excited cluster amplitudes. The corrections to the Brillouin-Wigner and Mukherjee's MRCC models based on the manifold of singly and doubly excited cluster amplitudes (BW-MRCCSD and Mk-MRCCSD, respectively) are tested and compared with exact full configuration interaction results for small systems (H(2)O, N(2), and Be(3)). For the larger systems (naphthyne isomers) the BW-MRCC and Mk-MRCC methods with iterative singles, doubles, and non-iterative triples (BW-MRCCSD(T) and Mk-MRCCSD(T)) are compared against the results obtained with single reference coupled cluster methods. We also report on the parallel performance of the non-iterative implementations based on the use of processor groups.

Multireference state-specific Mukherjee's coupled cluster method with noniterative triexcitations using uncoupled approximation.

A new version of the multireference Mukherjee's coupled cluster method with perturbative triexcitations has been formulated, which is based on the uncoupled approximation applied to the triples equation. In contrast to the method developed by Evangelista et al. [J. Chem. Phys. 132, 074107 (2010)], the proposed approach does not require to solve the equation for T(3) amplitudes iteratively, yet yields results of essentially the same quality. The method, abbreviated as MR MkCCSD(Tu), has been implemented in the ACES II program package and its assessment has been performed on the BeH(2) model and on the tetramethyleneethane molecule.

Uncoupled multireference state-specific Mukherjee's coupled cluster method with triexcitations.

We have developed the uncoupled version of multireference Mukherjee's coupled cluster method with connected triexcitations. The method has been implemented in ACES II program package. The agreement between the uncoupled and the standard version of Mukherjee's multireference coupled cluster method has been reported previously at the singles and doubles level by Das et al. [J. Mol. Struct.: THEOCHEM 79, 771 (2006); Chem. Phys. 349, 115 (2008)]. The aim of this article is to investigate this method further, in order to establish how its performance changes with the size of the basis set, size of the model space, multireference character of different molecules, and inclusion of connected triple excitations. Assessment of the new method has been performed on the singlet methylene, potential energy curve of fluorine molecule, and third b (1)Σ(g)(+) electronic state of oxygen molecule.

Multireference Mukherjee's coupled cluster method with triexcitations in the linked formulation: Efficient implementation and applications.

We have formulated the multireference Mukherjee's coupled clusters method with triexcitations (MR MkCCSDT) in the linked version and implemented it in the ACES II program package. The assessment of the new method has been performed on the first three electronic states of the oxygen molecule, on studies of singlet-triplet gap in methylene and twisted ethylene, where a comparison with other multireference CC treatments and with experimental data is available. The MR MkCCSDT results show accuracy comparable to which can be achieved with CCSDT in single reference cases. Comparison of the previously developed MkCCSD(T) method with MkCCSDT as a reference suggests, that MkCCSD(T) might be a promising candidate for an accurate treatment of systems where the static correlation plays an important role, at least for situations where small model spaces are sufficient.

Multireference state-specific Mukherjee's coupled cluster method with noniterative triexcitations.

We have formulated and implemented the multireference Mukherjee's coupled cluster method with connected singles, doubles, and perturbative triples [MR MkCCSD(T)] in the ACES II program package. Assessment of the new method has been performed on the first three electronic states of the oxygen molecule and on the automerization barrier of cyclobutadiene, where a comparison with other multireference CC treatments and with experimental data where available. The MR MkCCSD(T) method seems to be a promising candidate for an accurate, yet computationally tractable, treatment of systems where the static correlation plays an important role.