Quantum Vision Transformers

We design and analyse quantum transformers, extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon the previous work of parametrised quantum circuits for data loading and orthogonal neural layers, we introduce three quantum attention mechanisms, including a quantum transformer based on compound matrices. These quantum architectures can be built using shallow quantum circuits and can provide qualitatively different classification models. We performed extensive simulations of the quantum transformers on standard medical image datasets that showed competitive, and at times better, performance compared with the best classical transformers and other classical benchmarks. The computational complexity of our quantum attention layer proves to be advantageous compared with the classical algorithm with respect to the size of the classified images. Our quantum architectures have thousands of parameters compared with the best classical methods with millions of parameters. Finally, we have implemented our quantum transformers on superconducting quantum computers and obtained encouraging results for up to six qubit experiments.Comment: 16 page

Quantum Methods for Neural Networks and Application to Medical Image Classification

Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known training algorithms. The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation for inference and training of classical neural networks. We then present extensive experiments applied to medical image classification tasks using current state of the art quantum hardware, where we compare different quantum methods with classical ones, on both real quantum hardware and simulators. Our results show that quantum and classical neural networks generates similar level of accuracy, supporting the promise that quantum methods can be useful in solving visual tasks, given the advent of better quantum hardware.Comment: arXiv admin note: substantial text overlap with arXiv:2109.01831, arXiv:2106.0719

The prospects of quantum computing in computational molecular biology

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.Comment: 23 pages, 3 figure

Evolutionary conservation of otd/Otx2 transcription factor action: a genome-wide microarray analysis in Drosophila

BACKGROUND: Homeobox genes of the orthodenticle (otd)/Otx family have conserved roles in the embryogenesis of head and brain. Gene replacement experiments show that the Drosophila otd gene and orthologous mammalian Otx genes are functionally equivalent, in that overexpression of either gene in null mutants of Drosophila or mouse can restore defects in cephalic and brain development. This suggests that otd and Otx genes control a comparable subset of downstream target genes in either organism. Here we use quantitative transcript imaging to analyze this equivalence of otd and Otx gene action at a genomic level. RESULTS: Oligonucleotide arrays representing 13,400 annotated Drosophila genes were used to study differential gene expression in flies in which either the Drosophila otd gene or the human Otx2 gene was overexpressed. Two hundred and eighty-seven identified transcripts showed highly significant changes in expression levels in response to otd overexpression, and 682 identified transcripts showed highly significant changes in expression levels in response to Otx2 overexpression. Among these, 93 showed differential expression changes following overexpression of either otd or Otx2, and for 90 of these, comparable changes were observed under both experimental conditions. We postulate that these transcripts are common downstream targets of the fly otd gene and the human Otx2 gene in Drosophila. CONCLUSION: Our experiments indicate that approximately one third of the otd-regulated transcripts also respond to overexpression of the human Otx2 gene in Drosophila. These common otd/Otx2 downstream genes are likely to represent the molecular basis of the functional equivalence of otd and Otx2 gene action in Drosophila

Quantum Vision Transformers

In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon the previous work, which uses parametrised quantum circuits for data loading and orthogonal neural layers, we introduce three types of quantum transformers for training and inference, including a quantum transformer based on compound matrices, which guarantees a theoretical advantage of the quantum attention mechanism compared to their classical counterpart both in terms of asymptotic run time and the number of model parameters. These quantum architectures can be built using shallow quantum circuits and produce qualitatively different classification models. The three proposed quantum attention layers vary on the spectrum between closely following the classical transformers and exhibiting more quantum characteristics. As building blocks of the quantum transformer, we propose a novel method for loading a matrix as quantum states as well as two new trainable quantum orthogonal layers adaptable to different levels of connectivity and quality of quantum computers. We performed extensive simulations of the quantum transformers on standard medical image datasets that showed competitively, and at times better performance compared to the classical benchmarks, including the best-in-class classical vision transformers. The quantum transformers we trained on these small-scale datasets require fewer parameters compared to standard classical benchmarks. Finally, we implemented our quantum transformers on superconducting quantum computers and obtained encouraging results for up to six qubit experiments

Clinical relevance of molecular characteristics in Burkitt lymphoma differs according to age

While survival has improved for Burkitt lymphoma patients, potential differences in outcome between pediatric and adult patients remain unclear. In both age groups, survival remains poor at relapse. Therefore, we conducted a comparative study in a large pediatric cohort, including 191 cases and 97 samples from adults. While TP53 and CCND3 mutation frequencies are not age related, samples from pediatric patients showed a higher frequency of mutations in ID3, DDX3X, ARID1A and SMARCA4, while several genes such as BCL2 and YY1AP1 are almost exclusively mutated in adult patients. An unbiased analysis reveals a transition of the mutational profile between 25 and 40 years of age. Survival analysis in the pediatric cohort confirms that TP53 mutations are significantly associated with higher incidence of relapse (25 ± 4% versus 6 ± 2%, p-value 0.0002). This identifies a promising molecular marker for relapse incidence in pediatric BL which will be used in future clinical trials

Somatic mutations and progressive monosomy modify SAMD9-related phenotypes in humans

It is well established that somatic genomic changes can influence phenotypes in cancer, but the role of adaptive changes in developmental disorders is less well understood. Here we have used next-generation sequencing approaches to identify de novo heterozygous mutations in sterile α motif domain–containing protein 9 (SAMD9, located on chromosome 7q21.2) in 8 children with a multisystem disorder termed MIRAGE syndrome that is characterized by intrauterine growth restriction (IUGR) with gonadal, adrenal, and bone marrow failure, predisposition to infections, and high mortality. These mutations result in gain of function of the growth repressor product SAMD9. Progressive loss of mutated SAMD9 through the development of monosomy 7 (–7), deletions of 7q (7q–), and secondary somatic loss-of-function (nonsense and frameshift) mutations in SAMD9 rescued the growth-restricting effects of mutant SAMD9 proteins in bone marrow and was associated with increased length of survival. However, 2 patients with –7 and 7q– developed myelodysplastic syndrome, most likely due to haploinsufficiency of related 7q21.2 genes. Taken together, these findings provide strong evidence that progressive somatic changes can occur in specific tissues and can subsequently modify disease phenotype and influence survival. Such tissue-specific adaptability may be a more common mechanism modifying the expression of human genetic conditions than is currently recognized

Prevalence, clinical characteristics, and prognosis of GATA2-related myelodysplastic syndromes in children and adolescents

GermlineGATA2 mutations cause cellular deficiencieswith high propensity for myeloid disease. We investigated 426 children and adolescents with primary myelodysplastic syndrome (MDS) and 82 cases with secondary MDS enrolled in 2 consecutive prospective studies of the European Working Group of MDS in Childhood (EWOGMDS) conducted in Germany over a period of 15 years. Germline GATA2 mutations accounted for 15% of advanced and 7% of all primary MDS cases, but were absent in children with MDS secondary to therapy or acquired aplastic anemia. Mutation carriers were older at diagnosis and more likely to present with monosomy 7 and advanced disease compared with wild-type cases. For stratified analysis according to karyotype, 108 additional primary MDS patients registered with EWOG-MDS were studied. Overall, we identified 57 MDS patients with germline GATA2mutations. GATA2 mutations were highly prevalent among patients with monosomy 7 (37%, all ages) reaching its peak in adolescence (72%of adolescents withmonosomy 7).Unexpectedly, monocytosis was more frequent in GATA2-mutated patients. However, when adjusted for the selection bias from monosomy 7, mutational status had no effect on the hematologic phenotype. Finally, overall survival and outcome of hematopoietic stem cell transplantation (HSCT) were not influenced by mutational status. This study identifies GATA2 mutations as the most common germline defect predisposing to pediatric MDS with a very high prevalence in adolescents with monosomy 7. GATA2 mutations do not confer poor prognosis in childhood MDS. However, the high risk for progression to advanced diseasemust guide decision-making toward timely HSCT

Disturbance and the resilience of coupled carbon and nitrogen cycling in a north temperate forest

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96419/1/jgrg864.pd

Range-wide experiment to investigate nutrient and soil moisture interactions in loblolly pine plantations

The future climate of the southeastern USA is predicted to be warmer, drier and more variable in rainfall, which may increase drought frequency and intensity. Loblolly pine (Pinus taeda) is the most important commercial tree species in the world and is planted on ~11 million ha within its native range in the southeastern USA. A regional study was installed to evaluate effects of decreased rainfall and nutrient additions on loblolly pine plantation productivity and physiology. Four locations were established to capture the range-wide variability of soil and climate. Treatments were initiated in 2012 and consisted of a factorial combination of throughfall reduction (approximate 30% reduction) and fertilization (complete suite of nutrients). Tree and stand growth were measured at each site. Results after two growing seasons indicate a positive but variable response of fertilization on stand volume increment at all four sites and a negative effect of throughfall reduction at two sites. Data will be used to produce robust process model parameterizations useful for simulating loblolly pine growth and function under future, novel climate and management scenarios. The resulting improved models will provide support for developing management strategies to increase pine plantation productivity and carbon sequestration under a changing climate.Peer reviewedNatural Resource Ecology and Managemen