Identification of genomic loci regulating grain iron content in rice under two irrigation management systems.

Iron (Fe) deficiency is one of the common causes of anaemia in humans. Improving grain Fe in rice, therefore, could have a positive impact for humans worldwide, especially for those people who consume rice as a staple food. In this study, 225-269 accessions of the Bengal and Assam Aus Panel (BAAP) were investigated for their accumulation of grain Fe in two consecutive years in a field experiment under alternative wetting and drying (AWD) and continuous flooded (CF) irrigation.

Prioritisation of patients awaiting hip and knee arthroplasty: Lower pre-operative EQ-5D is associated with greater improvement in quality of life and joint function.

Introduction: The COVID-19 pandemic has led to unprecedented delays for those awaiting elective hip and knee arthroplasty. Current demand far exceeds available resource, and therefore it is integral that healthcare resource is fairly rationed to those who need it most. We therefore set out to determine if pre-operative health-related quality of life assessment (HRQoL) could be used to triage arthroplasty waiting lists.

GPU-accelerated connectome discovery at scale.

Diffusion magnetic resonance imaging and tractography enable the estimation of anatomical connectivity in the human brain, in vivo. Yet, without ground-truth validation, different tractography algorithms can yield widely varying connectivity estimates. Although streamline pruning techniques mitigate this challenge, slow compute times preclude their use in big-data applications.

ReAl-LiFE: Accelerating the Discovery of Individualized Brain Connectomes on GPUs.

Diffusion imaging and tractography enable mapping structural connections in the human brain, . Linear Fascicle Evaluation (LiFE) is a state-of-the-art approach for pruning spurious connections in the estimated structural connectome, by optimizing its fit to the measured diffusion data. Yet, LiFE imposes heavy demands on computing time, precluding its use in analyses of large connectome databases.

Genetic loci regulating arsenic content in rice grains when grown flooded or under alternative wetting and drying irrigation.

Background: Rice is a global staple crop, being the main calorific component of many people living subsistence livelihoods. Rice can accumulate toxic elements such as arsenic, with the crop water management strongly affecting uptake. This study utilises the Bengal and Assam Aus Panel to conduct genome wide association (GWA) mapping for arsenic in shoots and grains of rice grown over 2 years under continually flooded (CF) and alternate wetting and drying (AWD).

QTL-seq reveals a major root-knot nematode resistance locus on chromosome 11 in rice ( L.).

The root-knot nematode is a serious pest in rice affecting production in many rice growing areas. Natural host resistance is an attractive control strategy because the speed of the parasite's life cycle and the broad host range it attacks make other control measures challenging. Although resistance has been found in the domesticated African rice and the wild rice species the introgression of resistance genes to Asian rice is challenging.

Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications.

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the (CMTF) problem. Can we enhance CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of CMTF algorithm, produces sparse and interpretable solutions, and parallelizes CMTF algorithm, producing sparse and interpretable solutions (up to ).

Biallelic and Genome Wide Association Mapping of Germanium Tolerant Loci in Rice (Oryza sativa L.).

Rice plants accumulate high concentrations of silicon. Silicon has been shown to be involved in plant growth, high yield, and mitigating biotic and abiotic stresses. However, it has been demonstrated that inorganic arsenic is taken up by rice through silicon transporters under anaerobic conditions, thus the ability to efficiently take up silicon may be considered either a positive or a negative trait in rice.

Interpretable Semantic Vectors from a Joint Model of Brain- and Text-Based Meaning.

Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words.

Good-Enough Brain Model: Challenges, Algorithms, and Discoveries in Multisubject Experiments.

Given a simple noun such as apple, and a question such as "Is it edible?," what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects? In this work, we show that this problem, even though originating from the field of neuroscience, can benefit from big data techniques; we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text). We evaluate GeBM by using real brain data.

Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses.

Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy.

Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×.

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the (CMTF) problem. Can we accelerate CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce TURBO-SMT, a meta-method capable of doing exactly that: it boosts the performance of CMTF algorithm, by up to ×, along with an up to increase in sparsity, with comparable accuracy to the baseline.