The well-known Hall effect describes the transverse deflection of charged particles (electrons/holes) as a result of the Lorentz force. Similarly, it is intriguing to examine if quasi-particles ...without an electric charge, but with a topological charge, show related transverse motion. Magnetic skyrmions with a well-defined spin texture with a unit topological charge serve as good candidates to test this hypothesis. In spite of the recent progress made on investigating magnetic skyrmions, direct observation of the skyrmion Hall effect has remained elusive. Here, by using a current-induced spin Hall spin torque, we experimentally demonstrate the skyrmion Hall effect, and the resultant skyrmion accumulation, by driving skyrmions from the creep-motion regime (where their dynamics are influenced by pinning defects) into the steady-flow-motion regime. The experimental observation of transverse transport of skyrmions due to topological charge may potentially create many exciting opportunities, such as topological selection.
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The ...increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.
Neuroeconomics applies models from economics and psychology to inform neurobiological studies of choice. This approach has revealed neural signatures of concepts like value, risk, and ambiguity, ...which are known to influence decision making. Such observations have led theorists to hypothesize a single, unified decision process that mediates choice behavior via a common neural currency for outcomes like food, money, or social praise. In parallel, recent neuroethological studies of decision making have focused on natural behaviors like foraging, mate choice, and social interactions. These decisions strongly impact evolutionary fitness and thus are likely to have played a key role in shaping the neural circuits that mediate decision making. This approach has revealed a suite of computational motifs that appear to be shared across a wide variety of organisms. We argue that the existence of deep homologies in the neural circuits mediating choice may have profound implications for understanding human decision making in health and disease.
Most work on the neuroscience of decision making has been based on a rational choice framework borrowed from economics. Pearson et al. review recent studies suggesting that natural behaviors may offer a more biologically grounded view that facilitates comparisons across species.
We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA ...mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.
There are currently many tools available for capturing and defining the context of one's environment. Digital phenotyping, the use of technology and sensors to capture moment-to-moment behavior, has ...shown potential in quantifying the lived experience of mental illness and in the identification of individualized targets related to recovery. Environmental data suggests that greenspace may have a restorative capacity on mental health. In this paper, we explore the relationship of greenspace derived from geolocation with self-reported symptomatology from individuals with schizophrenia as well as healthy controls. Individuals with schizophrenia had less exposure to greenspace than controls, but their exposure demonstrated a dosage effect: high greenspace environments were associated with lower symptoms for anxiety (Cohen's d = -0.70), depression (d = -0.97), and psychosis (d = -0.94), whereas effect sizes for healthy controls were all negligible or small (d < 0.38). The notion that greenspace may have a more pronounced effect on individuals with mental illness presents both potential areas for recovery as well as implications for health care policy, especially in cities with a broad range of greenspace environments.
We investigate four CuAu-I-type metallic antiferromagnets for their potential as spin current detectors using spin pumping and inverse spin Hall effect. Nontrivial spin Hall effects were observed for ...FeMn, PdMn, and IrMn while a much higher effect was obtained for PtMn. Using thickness-dependent measurements, we determined the spin diffusion lengths of these materials to be short, on the order of 1 nm. The estimated spin Hall angles of the four materials follow the relationship PtMn>IrMn>PdMn>FeMn, highlighting the correlation between the spin-orbit coupling of nonmagnetic species and the magnitude of the spin Hall effect in their antiferromagnetic alloys. These experiments are compared with first-principles calculations. Engineering the properties of the antiferromagnets as well as their interfaces can pave the way for manipulation of the spin dependent transport properties in antiferromagnet-based spintronics.
Paramagnetic spin seebeck effect Wu, Stephen M; Pearson, John E; Bhattacharya, Anand
Physical review letters,
2015-May-08, Letnik:
114, Številka:
18
Journal Article
Recenzirano
Odprti dostop
We report the observation of the longitudinal spin Seebeck effect in paramagnetic insulators. By using a microscale on-chip local heater, we generate a large thermal gradient confined to the chip ...surface without a large increase in the total sample temperature. Using this technique at low temperatures (<20 K), we resolve the paramagnetic spin Seebeck effect in the insulating paramagnets Gd3Ga5O12 (gadolinium gallium garnet) and DyScO3 (DSO), using either W or Pt as the spin detector layer. By taking advantage of the strong magnetocrystalline anisotropy of DSO, we eliminate contributions from the Nernst effect in W or Pt, which produces a phenomenologically similar signal.
Continuous decisions Yoo, Seng Bum Michael; Hayden, Benjamin Yost; Pearson, John M
Philosophical transactions - Royal Society. Biological sciences,
03/2021, Letnik:
376, Številka:
1819
Journal Article
Recenzirano
Odprti dostop
Humans and other animals evolved to make decisions that extend over time with continuous and ever-changing options. Nonetheless, the academic study of decision-making is mostly limited to the simple ...case of choice between two options. Here, we advocate that the study of choice should expand to include
. Continuous decisions, by our definition, involve a continuum of possible responses and take place over an extended period of time during which the response is continuously subject to modification. In most continuous decisions, the range of options can fluctuate and is affected by recent responses, making consideration of reciprocal feedback between choices and the environment essential. The study of continuous decisions raises new questions, such as how abstract processes of valuation and comparison are co-implemented with action planning and execution, how we simulate the large number of possible futures our choices lead to, and how our brains employ hierarchical structure to make choices more efficiently. While microeconomic theory has proven invaluable for discrete decisions, we propose that engineering control theory may serve as a better foundation for continuous ones. And while the concept of value has proven foundational for discrete decisions, goal states and policies may prove more useful for continuous ones. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.
The formation of soap bubbles from thin films is accompanied by topological transitions. Here we show how a magnetic topological structure, a skyrmion bubble, can be generated in a solid-state system ...in a similar manner. Using an inhomogeneous in-plane current in a system with broken inversion symmetry, we experimentally "blow" magnetic skyrmion bubbles from a geometrical constriction. The presence of a spatially divergent spin-orbit torque gives rise to instabilities of the magnetic domain structures that are reminiscent of Rayleigh-Plateau instabilities in fluid flows. We determine a phase diagram for skyrmion formation and reveal the efficient manipulation of these dynamically created skyrmions, including depinning and motion. The demonstrated current-driven transformation from stripe domains to magnetic skyrmion bubbles could lead to progress in skyrmion-based spintronics.
Bacteria of the genera Pseudomonas and Bacillus can promote plant growth and protect plants from pathogens. However, the interactions between these plant-beneficial bacteria are understudied. Here, ...we explore the interaction between Bacillus subtilis 3610 and Pseudomonas chlororaphis PCL1606. We show that the extracellular matrix protects B. subtilis colonies from infiltration by P. chlororaphis. The absence of extracellular matrix results in increased fluidity and loss of structure of the B. subtilis colony. The P. chlororaphis type VI secretion system (T6SS) is activated upon contact with B. subtilis cells, and stimulates B. subtilis sporulation. Furthermore, we find that B. subtilis sporulation observed prior to direct contact with P. chlororaphis is mediated by histidine kinases KinA and KinB. Finally, we demonstrate the importance of the extracellular matrix and the T6SS in modulating the coexistence of the two species on melon plant leaves and seeds.
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