Linked coupled cluster Monte Carlo.

We consider a new formulation of the stochastic coupled cluster method in terms of the similarity transformed Hamiltonian. We show that improvement in the granularity with which the wavefunction is represented results in a reduction in the critical population required to correctly sample the wavefunction for a range of systems and excitation levels and hence leads to a substantial reduction in the computational cost. This development has the potential to substantially extend the range of the method, enabling it to be used to treat larger systems with excitation levels not easily accessible with conventional deterministic methods.R.S.T.F. is grateful to CHESS for a studentship and A.J.W.T. to the Royal Society for a University Research Fellowship. J.S.S. acknowledges the research environment provided by the Thomas Young Centre under Grant No. TYC-101.This is the author accepted manuscript. The final version is available from the American Institute of Physics via http://dx.doi.org/10.1063/1.494031

Ab-initio solution of the many-electron Schrödinger equation with deep neural networks

Given access to accurate solutions of the many-electron Schr\"odinger equation, nearly all chemistry could be derived from first principles. Exact wavefunctions of interesting chemical systems are out of reach because they are NP-hard to compute in general, but approximations can be found using polynomially-scaling algorithms. The key challenge for many of these algorithms is the choice of wavefunction approximation, or Ansatz, which must trade off between efficiency and accuracy. Neural networks have shown impressive power as accurate practical function approximators and promise as a compact wavefunction Ansatz for spin systems, but problems in electronic structure require wavefunctions that obey Fermi-Dirac statistics. Here we introduce a novel deep learning architecture, the Fermionic Neural Network, as a powerful wavefunction Ansatz for many-electron systems. The Fermionic Neural Network is able to achieve accuracy beyond other variational quantum Monte Carlo Ans\"atze on a variety of atoms and small molecules. Using no data other than atomic positions and charges, we predict the dissociation curves of the nitrogen molecule and hydrogen chain, two challenging strongly-correlated systems, to significantly higher accuracy than the coupled cluster method, widely considered the most accurate scalable method for quantum chemistry at equilibrium geometry. This demonstrates that deep neural networks can improve the accuracy of variational quantum Monte Carlo to the point where it outperforms other ab-initio quantum chemistry methods, opening the possibility of accurate direct optimisation of wavefunctions for previously intractable molecules and solids

Linear-scaling time-dependent density-functional theory in the linear response formalism

We present an implementation of time-dependent density-functional theory (TDDFT) in the linear response formalism enabling the calculation of low energy optical absorption spectra for large molecules and nanostructures. The method avoids any explicit reference to canonical representations of either occupied or virtual Kohn-Sham states and thus achieves linear-scaling computational effort with system size. In contrast to conventional localised orbital formulations, where a single set of localised functions is used to span the occupied and unoccupied state manifold, we make use of two sets of in situ optimised localised orbitals, one for the occupied and one for the unoccupied space. This double representation approach avoids known problems of spanning the space of unoccupied Kohn-Sham states with a minimal set of localised orbitals optimised for the occupied space, while the in situ optimisation procedure allows for efficient calculations with a minimal number of functions. The method is applied to a number of medium sized organic molecules and a good agreement with traditional TDDFT methods is observed. Furthermore, linear scaling of computational cost with system size is demonstrated on a system of carbon nanotubes

Distribution of the invasive bryozoan Schizoporella japonica in Great Britain and Ireland and a review of its European distribution

The bryozoan Schizoporella japonica Ortmann (1890) was first recorded in European waters in 2010 and has since been reported from further locations in Great Britain (GB) and Norway. This paper provides a new earliest European record for the species from 2009, a first record from Ireland and presence and absence records from a total of 231 marinas and harbours across GB, Ireland, the Isle of Man, France and Portugal. This species is typically associated with human activity, including commercial and recreational vessels, aquaculture equipment, and both wave and tidal energy devices. It has also been observed in the natural environment, fouling rocks and boulders. The species has an extensive but widely discontinuous distribution in GB and Ireland. Although found frequently in marinas and harbours in Scotland, it inhabits only a few sites in England, Wales and Ireland, interspersed with wide gaps that are well documented as genuine absences. This appears to be a rare example of a southward-spreading invasion in GB and Ireland. The species has been reported from the Isle of Man and Norway but has not been found in France or Portugal. In the future we expect S. japonica to spread into suitable sections of the English, Welsh and Irish coasts, and further within Europe. The species’ capability for long-distance saltatory spread and potential for negative impact on native ecosystems and economic activity suggests that S. japonica should now be considered invasive in GB and Ireland. As such, it is recommended that biosecurity procedures alongside effective surveillance and monitoring should be prioritised for regions outside the species’ current distribution

Correlations of genetic resistance of chickens to Marek's disease viruses with vaccination protection and in vivo response to phytohemaglutinin

International audienc

Semi-stochastic full configuration interaction quantum Monte Carlo: Developments and application.

We expand upon the recent semi-stochastic adaptation to full configuration interaction quantum Monte Carlo (FCIQMC). We present an alternate method for generating the deterministic space without a priori knowledge of the wave function and present stochastic efficiencies for a variety of both molecular and lattice systems. The algorithmic details of an efficient semi-stochastic implementation are presented, with particular consideration given to the effect that the adaptation has on parallel performance in FCIQMC. We further demonstrate the benefit for calculation of reduced density matrices in FCIQMC through replica sampling, where the semi-stochastic adaptation seems to have even larger efficiency gains. We then combine these ideas to produce explicitly correlated corrected FCIQMC energies for the beryllium dimer, for which stochastic errors on the order of wavenumber accuracy are achievable.N.S.B. gratefully acknowledges Trinity College, Cambridge for funding. J.S.S. acknowledges the research environment provided by the Thomas Young Centre under Grant No. TYC-101. G.H.B. gratefully acknowledges the Royal Society for a university research fellowship. This work has been supported by the EPSRC under grant no. EP/J003867/1.This is the author accepted manuscript. The final version is available from AIP at http://scitation.aip.org/content/aip/journal/jcp/142/18/10.1063/1.4920975

Discovering quantum phase transitions with fermionic neural networks

Deep neural networks have been extremely successful as highly accurate wave function ans\"atze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network is given no \emph{a priori} knowledge that a phase transition exists, but converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density

Comprehensive analysis of alternative splicing across multiple transcriptomic cohorts reveals prognostic signatures in prostate cancer

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The gene expression profiling procedure using the Affymetrix Clariom D Human Array for this study is detailed in our previous paper [22]. The corresponding raw CEL data have been submitted to the Gene Expression Omnibus (GEO) database, under accession number GSE246282. Percent-spliced-in (PSI) values of alternative splicing events for TCGA-PRAD cohort were available at TCGA SpliceSeq (https://bioinformatics.mdanderson.org/TCGASpliceSeq/index.jsp). Raw RNA-seq data for PRJEB2449 cohort were available at ENA (https://www.ebi.ac.uk/ena/browser/home). Raw CEL files of microarray data for GSE107299 cohort were available at GEO (https://www.ncbi.nlm.nih.gov/geo/).Background Alternative splicing (AS) plays a crucial role in transcriptomic diversity and is a hallmark of cancer that profoundly influences the development and progression of prostate cancer (PCa), a prevalent and potentially life-limiting cancer among men. Accumulating evidence has highlighted the association between AS dysregulation and the onset and progression of PCa. However, a comprehensive and integrative analysis of AS profiles at the event level, utilising data from multiple high-throughput cohorts and evaluating the prognosis of PCa progression, remains lacking and calls for thorough exploration. Results We identified a differentially expressed retained intron event in ZWINT across three distinct cohorts, encompassing an original array-based dataset profiled by us previously and two RNA sequencing (RNA-seq) datasets. Subsequent in-depth analyses of these RNA-seq datasets revealed 141 altered events, of which 21 demonstrated a significant association with patients’ biochemical recurrence-free survival (BCRFS). We formulated an AS event-based prognostic signature, capturing six pivotal events in genes CYP4F12, NFATC4, PIGO, CYP3A5, ALS2CL, and FXYD3. This signature effectively differentiated high-risk patients diagnosed with PCa, who experienced shorter BCRFS, from their low-risk counterparts. Notably, the signature's predictive power surpassed traditional clinicopathological markers in forecasting 5-year BCRFS, demonstrating robust performance in both internal and external validation sets. Lastly, we constructed a novel nomogram that integrates patients’ Gleason scores with pathological tumour stages, demonstrating improved prognostication of BCRFS. Conclusions Prediction of clinical progression remains elusive in PCa. This research uncovers novel splicing events associated with BCRFS, augmenting existing prognostic tools, thus potentially refining clinical decision-making